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chore: import upstream snapshot with attribution

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wehub-resource-sync
2026-07-13 12:32:19 +08:00
commit 4332b17d72
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.agents/plugins/marketplace.json
+56
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{
"name": "agent-toolkit-for-aws",
"interface": {
"displayName": "Agent Toolkit for AWS"
},
"plugins": [
{
"name": "aws-core",
"source": {
"source": "local",
"path": "./plugins/aws-core"
},
"policy": {
"installation": "AVAILABLE",
"authentication": "ON_INSTALL"
},
"category": "Cloud"
},
{
"name": "aws-agents",
"source": {
"source": "local",
"path": "./plugins/aws-agents"
},
"policy": {
"installation": "AVAILABLE",
"authentication": "ON_INSTALL"
},
"category": "Cloud"
},
{
"name": "aws-data-analytics",
"source": {
"source": "local",
"path": "./plugins/aws-data-analytics"
},
"policy": {
"installation": "AVAILABLE",
"authentication": "ON_INSTALL"
},
"category": "Cloud"
},
{
"name": "aws-agents-for-devsecops",
"source": {
"source": "local",
"path": "./plugins/aws-agents-for-devsecops"
},
"policy": {
"installation": "AVAILABLE",
"authentication": "ON_INSTALL"
},
"category": "Cloud"
}
]
}
.claude-plugin/marketplace.json
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{
"metadata": {
"description": "A plugin marketplace hosting installable agent plugins for AWS.",
"version": "1.0.0"
},
"name": "agent-toolkit-for-aws",
"owner": {
"name": "Amazon Web Services"
},
"plugins": [
{
"category": "cloud",
"description": "Build, deploy, and operate applications on AWS. Skills to author infrastructure-as-code (CDK, CloudFormation), use core services (Lambda, API Gateway, Step Functions, ECS/Fargate, ECR, IAM, Amazon Bedrock with Knowledge Bases and Guardrails, AWS Blocks), select and operate databases across relational, key-value, document, wide-column, graph, time-series, and in-memory engines (Aurora PostgreSQL/MySQL, Aurora DSQL, RDS, Oracle Database@AWS, DynamoDB, DocumentDB, Keyspaces, Neptune, Timestream, ElastiCache, and MemoryDB), and complete common tasks across observability (CloudWatch, X-Ray, CloudTrail, ADOT), messaging and streaming (SQS, SNS, EventBridge, Kinesis, MSK), AWS SDKs (boto3, JS v3, Swift), and cost optimization.",
"keywords": [
"aws",
"amazon",
"amazon-web-services",
"iam",
"bedrock",
"amazon-bedrock",
"aws-blocks",
"billing",
"cost-explorer",
"cost-optimization",
"savings-plans",
"compute-optimizer",
"cdk",
"aws-cdk",
"cloudformation",
"sam",
"serverless",
"lambda",
"api-gateway",
"step-functions",
"eventbridge",
"containers",
"ecs",
"fargate",
"ecr",
"messaging",
"streaming",
"sqs",
"sns",
"kinesis",
"kinesis-firehose",
"msk",
"kafka",
"flink",
"amazon-mq",
"observability",
"cloudwatch",
"logs-insights",
"x-ray",
"cloudtrail",
"adot",
"opentelemetry",
"sdk",
"aws-sdk",
"boto3",
"aws-sdk-js-v3",
"aws-sdk-swift",
"knowledge-bases",
"rag",
"guardrails",
"opensearch",
"opensearch-serverless",
"amazon-opensearch-service",
"cognito",
"appsync",
"dynamodb",
"database",
"databases",
"relational-database",
"aurora",
"aurora-postgresql",
"aurora-mysql",
"aurora-dsql",
"dsql",
"postgres",
"postgresql",
"mysql",
"mariadb",
"oracle",
"sql-server",
"sqlserver",
"db2",
"rds",
"rds-postgresql",
"rds-mysql",
"rds-mariadb",
"rds-oracle",
"rds-sqlserver",
"rds-db2",
"odb",
"oracle-database-at-aws",
"documentdb",
"mongodb",
"elasticache",
"memorydb",
"redis",
"valkey",
"memcached",
"keyspaces",
"cassandra",
"neptune",
"graph-database",
"timestream",
"time-series",
"influxdb",
"app-runner",
"snapstart",
"powertools",
"durable-functions",
"budgets",
"reserved-instances",
"right-sizing"
],
"name": "aws-core",
"source": "./plugins/aws-core",
"version": "1.1.0"
},
{
"category": "cloud",
"description": "Build, deploy, and operate AI agents on AWS. Skills for scaffolding agents with Amazon Bedrock AgentCore (Strands, LangGraph), connecting tools via Gateway and MCP, multi-agent and A2A orchestration, memory, Cedar policies, evaluation, observability, debugging traces and logs, and production hardening (inbound auth, IAM, rate limiting, cold-start tuning).",
"keywords": [
"aws",
"amazon",
"amazon-web-services",
"ai",
"ai-agents",
"agents",
"agentcore",
"bedrock",
"amazon-bedrock",
"iam",
"deploy",
"debug",
"memory",
"gateway",
"policy",
"cedar",
"evaluation",
"evals",
"strands",
"langgraph",
"mcp",
"a2a",
"multi-agent",
"tool-use",
"rag",
"vpc",
"observability",
"cloudwatch",
"tracing",
"x-ray",
"production-hardening",
"jwt",
"sigv4",
"oauth",
"openapi",
"code-interpreter",
"browser-tool",
"rate-limiting"
],
"name": "aws-agents",
"source": "./plugins/aws-agents",
"version": "1.0.0"
},
{
"category": "cloud",
"description": "Data lake, analytics, and ETL workflows with S3 Tables, AWS Glue, and Athena. Covers managed Iceberg tables on S3 Tables, ingestion from JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Amazon Redshift, Snowflake, BigQuery, and DynamoDB, AWS Glue Data Catalog inventory and asset discovery, federated Athena queries, and vector storage and semantic search on Amazon S3 Vectors.",
"keywords": [
"aws",
"amazon",
"amazon-web-services",
"iam",
"analytics",
"data-lake",
"lakehouse",
"athena",
"glue",
"aws-glue",
"data-catalog",
"s3",
"s3-tables",
"s3-vectors",
"iceberg",
"apache-iceberg",
"etl",
"redshift",
"snowflake",
"bigquery",
"rds",
"dynamodb",
"jdbc",
"secrets-manager",
"vector-search",
"semantic-search",
"rag",
"embeddings",
"vector-database",
"aurora"
],
"name": "aws-data-analytics",
"source": "./plugins/aws-data-analytics",
"version": "1.0.0"
},
{
"category": "cloud",
"description": "Investigate incidents, review code and execute UAT for release readiness, scan code for vulnerabilities, and run penetration tests with AWS DevOps Agent and AWS Security Agent.",
"keywords": [
"Amazon Web Services",
"agentspace",
"api-testing",
"appsec",
"architecture-review",
"aws",
"cloudwatch",
"code-review",
"cost-optimization",
"devops",
"devsecops",
"incident",
"investigation",
"is-my-code-secure",
"mcp",
"observability",
"operations",
"penetration-test",
"pentest",
"pre-merge",
"qa",
"release",
"release-analysis",
"release-readiness",
"release-testing",
"reliability",
"remediation",
"risk-analysis",
"security",
"security-scan",
"security-vulnerabilities",
"testing",
"threat-model",
"topology",
"uat",
"ui-testing"
],
"name": "aws-agents-for-devsecops",
"source": "./plugins/aws-agents-for-devsecops",
"version": "1.0.0"
}
]
}
.cursor-plugin/marketplace.json
+31
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{
"name": "agent-toolkit-for-aws",
"owner": {
"name": "Amazon Web Services"
},
"metadata": {
"description": "A plugin marketplace hosting installable agent plugins for AWS."
},
"plugins": [
{
"name": "aws-core",
"source": "./plugins/aws-core",
"description": "Build, deploy, and operate applications on AWS. Skills to author infrastructure-as-code (CDK, CloudFormation), use core services (Lambda, API Gateway, Step Functions, ECS/Fargate, ECR, IAM, Amazon Bedrock with Knowledge Bases and Guardrails, AWS Blocks), select and operate databases across relational, key-value, document, wide-column, graph, time-series, and in-memory engines (Aurora PostgreSQL/MySQL, Aurora DSQL, RDS, Oracle Database@AWS, DynamoDB, DocumentDB, Keyspaces, Neptune, Timestream, ElastiCache, and MemoryDB), and complete common tasks across observability (CloudWatch, X-Ray, CloudTrail, ADOT), messaging and streaming (SQS, SNS, EventBridge, Kinesis, MSK), AWS SDKs (boto3, JS v3, Swift), and cost optimization."
},
{
"name": "aws-agents",
"source": "./plugins/aws-agents",
"description": "Build, deploy, and operate AI agents on AWS. Skills for scaffolding agents with Amazon Bedrock AgentCore (Strands, LangGraph), connecting tools via Gateway and MCP, multi-agent and A2A orchestration, memory, Cedar policies, evaluation, observability, debugging traces and logs, and production hardening (inbound auth, IAM, rate limiting, cold-start tuning)."
},
{
"name": "aws-data-analytics",
"source": "./plugins/aws-data-analytics",
"description": "Data lake, analytics, and ETL workflows with S3 Tables, AWS Glue, and Athena. Covers managed Iceberg tables on S3 Tables, ingestion from JDBC databases (Oracle, SQL Server, PostgreSQL, MySQL, RDS), Amazon Redshift, Snowflake, BigQuery, and DynamoDB, AWS Glue Data Catalog inventory and asset discovery, federated Athena queries, and vector storage and semantic search on Amazon S3 Vectors."
},
{
"name": "aws-agents-for-devsecops",
"source": "./plugins/aws-agents-for-devsecops",
"description": "Investigate incidents, scan code for vulnerabilities, and run penetration tests with AWS DevOps Agent and AWS Security Agent."
}
]
}
.editorconfig
+18
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root = true
[*]
charset = utf-8
end_of_line = lf
insert_final_newline = true
trim_trailing_whitespace = true
indent_style = space
indent_size = 2
[*.py]
indent_size = 4
[*.md]
trim_trailing_whitespace = false
[Makefile]
indent_style = tab
.github/CODEOWNERS
+7
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* @aws/agent-toolkit-admins
## Alphabetical listing of Agent Plugins
/plugins/aws-agents @aws/agent-toolkit-admins @aws/agentcore-devex-devs @aws/agentcore-devex-pms
/plugins/aws-agents-for-devsecops @aws/agent-toolkit-admins @aws/aws-agentic-devsecops @coffeencoke @tipuq @HuiSF @ljainiaz @adthiru @AhmetAhunbayAWS
/plugins/aws-core @aws/agent-toolkit-admins
/plugins/aws-data-analytics @aws/agent-toolkit-admins @npmajisha @risears @mitczach @harshabattapady @shoukasg @sanjolia @dhruvyadav2007 @mukeshsahay
.github/ISSUE_TEMPLATE/bug_report.yml
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---
name: "Bug Report"
description: Report a bug
title: "(plugin name): (short issue description)"
labels: [bug, needs-triage]
assignees: []
body:
- type: textarea
id: description
attributes:
label: Describe the bug
description: What is the problem? A clear and concise description of the bug.
validations:
required: true
- type: textarea
id: expected
attributes:
label: Expected Behavior
description: What did you expect to happen?
validations:
required: true
- type: textarea
id: current
attributes:
label: Current Behavior
description: |
What actually happened?
Please include full errors, uncaught exceptions, stack traces, and relevant logs.
validations:
required: true
- type: textarea
id: reproduction
attributes:
label: Reproduction Steps
description: |
Provide a self-contained, concise snippet of code that can be used to reproduce the issue.
validations:
required: true
- type: input
id: plugin-version
attributes:
label: Plugin Version
validations:
required: true
- type: input
id: ai-assistant
attributes:
label: AI Assistant
description: E.g. Claude Code | Codex | Kiro | Cursor
validations:
required: true
- type: input
id: ai-assistant-version
attributes:
label: AI Assistant Version
validations:
required: true
- type: input
id: operating-system
attributes:
label: OS
validations:
required: true
- type: textarea
id: other
attributes:
label: Other information
description: |
e.g. detailed explanation, related issues, suggestions how to fix, links for context
validations:
required: false
.github/ISSUE_TEMPLATE/config.yml
+8
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blank_issues_enabled: false
contact_links:
- name: Security vulnerability
url: https://aws.amazon.com/security/vulnerability-reporting/
about: Please report security vulnerabilities through AWS Security, not as a public GitHub issue.
- name: Documentation
url: https://docs.aws.amazon.com/agent-toolkit/latest/userguide/
about: Setup, configuration, and reference documentation for the Agent Toolkit for AWS.
.github/ISSUE_TEMPLATE/feature_request.yml
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---
name: Feature Request
description: Suggest an idea for this project
title: "(plugin name): (short issue description)"
labels: [feature-request, needs-triage]
assignees: []
body:
- type: textarea
id: description
attributes:
label: Describe the feature
description: A clear and concise description of the feature you are proposing.
validations:
required: true
- type: textarea
id: use-case
attributes:
label: Use Case
description: |
Why do you need this feature? For example: "I'm always frustrated when..."
validations:
required: true
- type: textarea
id: solution
attributes:
label: Proposed Solution
description: |
Suggest how to implement the addition or change.
validations:
required: false
- type: textarea
id: other
attributes:
label: Other Information
description: |
Any alternative solutions or features you considered, links for context, etc.
validations:
required: false
- type: checkboxes
id: ack
attributes:
label: Acknowledgements
options:
- label: I may be able to implement this feature request
required: false
- label: This feature might incur a breaking change
required: false
.github/dependabot.yml
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# Dependabot configuration.
#
# Covers the only automated dependency surface in this repo: the pinned
# action SHAs in .github/workflows/. Mise-managed tools (node, gitleaks,
# markdownlint-cli2) are bumped manually via `mise upgrade`.
#
# https://docs.github.com/en/code-security/dependabot/dependabot-version-updates/configuration-options-for-the-dependabot.yml-file
version: 2
updates:
- package-ecosystem: github-actions
directory: /
schedule:
interval: weekly
day: monday
time: "09:00"
timezone: Etc/UTC
open-pull-requests-limit: 5
commit-message:
prefix: chore
include: scope
labels:
- dependencies
- github-actions
groups:
# Group all action SHA bumps into a single PR per week to reduce noise.
actions:
patterns:
- "*"
.github/pull_request_template.md
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## Description
<!-- Describe the changes in this PR -->
## Type of Change
- [ ] New plugin
- [ ] New skill
- [ ] Bug fix
- [ ] Documentation update
- [ ] CI/CD change
- [ ] Other
## Checklist
- [ ] I have read the [CONTRIBUTING](../CONTRIBUTING.md) guide
- [ ] My changes pass `python3 tools/validate.py`
- [ ] I have updated relevant documentation
*By submitting this pull request, I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.*
.github/workflows/build.yml
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name: Build
on:
push:
branches: [main]
pull_request:
branches: [main]
merge_group:
workflow_dispatch:
# Cancel superseded PR runs; let push/merge_group runs complete.
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
# Default to no permissions; grant minimally at the job level.
permissions:
actions: none
attestations: none
checks: none
contents: none
deployments: none
discussions: none
id-token: none
issues: none
models: none
packages: none
pages: none
pull-requests: none
repository-projects: none
security-events: none
statuses: none
jobs:
build:
runs-on: ubuntu-latest
timeout-minutes: 10
permissions:
contents: read
steps:
- uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
- uses: jdx/mise-action@e6a8b3978addb5a52f2b4cd9d91eafa7f0ab959d # v4.2.0
- name: Lint markdown
run: mise run lint:md
- name: Validate manifests and skills
run: mise run lint:manifests
- name: Security scan (gitleaks)
run: mise run security
.github/workflows/codeql.yml
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name: CodeQL
on:
push:
branches: [main]
pull_request:
branches: [main]
schedule:
# Weekly on Wednesday at 14:23 UTC (arbitrary off-peak time)
- cron: "23 14 * * 3"
workflow_dispatch: {}
# Default to no permissions; grant minimally at the job level.
permissions:
actions: none
attestations: none
checks: none
contents: none
deployments: none
discussions: none
id-token: none
issues: none
models: none
packages: none
pages: none
pull-requests: none
repository-projects: none
security-events: none
statuses: none
jobs:
analyze:
name: Analyze (${{ matrix.language }})
runs-on: ubuntu-latest
timeout-minutes: 30
permissions:
security-events: write # upload SARIF to code scanning
packages: read # fetch internal CodeQL packs (no-op for public repos)
actions: read
contents: read
strategy:
fail-fast: false
matrix:
include:
# Scans workflow YAML for GitHub Actions security pitfalls
# (script injection, untrusted checkout patterns, over-broad tokens).
- language: actions
build-mode: none
# Scans Python helpers (e.g. tools/validate.py).
- language: python
build-mode: none
steps:
- uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
- name: Initialize CodeQL
uses: github/codeql-action/init@5e7a52feb2a3dfb87f88be2af33b9e2275f48de6 # v4.32.2
with:
languages: ${{ matrix.language }}
build-mode: ${{ matrix.build-mode }}
- name: Perform CodeQL Analysis
uses: github/codeql-action/analyze@5e7a52feb2a3dfb87f88be2af33b9e2275f48de6 # v4.32.2
with:
category: "/language:${{ matrix.language }}"
.github/workflows/dependency-review.yml
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name: Dependency Review
on:
pull_request:
branches: [main]
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: true
# Default to no permissions; grant minimally at the job level.
permissions:
actions: none
attestations: none
checks: none
contents: none
deployments: none
discussions: none
id-token: none
issues: none
models: none
packages: none
pages: none
pull-requests: none
repository-projects: none
security-events: none
statuses: none
jobs:
dependency-review:
runs-on: ubuntu-latest
timeout-minutes: 5
permissions:
contents: read
steps:
- uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
- uses: actions/dependency-review-action@a1d282b36b6f3519aa1f3fc636f609c47dddb294 # v5.0.0
with:
# Shared Amazon OSPO dependency-review config used across awslabs/aws repos.
config-file: amazon-ospo/dependency-review-config/default/dependency-review-config.yml@8e4c9fdde54d2b7c6a3a28b97eddd26c4cd90a66 # main
.github/workflows/merge-prevention.yml
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---
# Prevents unintentional merges beyond what branch rulesets can express.
#
# Two independent gates:
# 1. Label gate — a PR labelled with `do-not-merge` (or `vars.DO_NOT_MERGE_LABEL`)
# is blocked until the label is removed.
# 2. Global gate — the `HALT_MERGES` repo variable blocks all merges when set:
# HALT_MERGES=0 → gate disabled (default)
# HALT_MERGES=<N> → only PR #N is allowed to merge
# HALT_MERGES=-1 → all merges blocked
#
# Both gates are cheap escape hatches for incident response; they are not a
# replacement for branch protection rules.
name: Merge Prevention
on:
pull_request:
types:
- opened
- reopened
- synchronize
- edited
- labeled
- unlabeled
merge_group:
types:
- checks_requested
# Cancel superseded PR runs; never cancel merge_group runs (they gate a queued merge).
concurrency:
group: ${{ github.workflow }}-${{ github.ref }}
cancel-in-progress: ${{ github.event_name == 'pull_request' }}
# Default to no permissions; grant minimally at the job level.
permissions:
actions: none
attestations: none
checks: none
contents: none
deployments: none
discussions: none
id-token: none
issues: none
models: none
packages: none
pages: none
pull-requests: none
repository-projects: none
security-events: none
statuses: none
env:
DO_NOT_MERGE_LABEL: ${{ vars.DO_NOT_MERGE_LABEL || 'do-not-merge' }}
HALT_MERGES: ${{ vars.HALT_MERGES || '0' }}
jobs:
get-pr-info:
runs-on: ubuntu-latest
timeout-minutes: 5
permissions:
contents: read
pull-requests: read
outputs:
pr_number: ${{ steps.get-pr.outputs.pr-number }}
pr_labels: ${{ steps.get-pr.outputs.pr-labels }}
env:
GH_TOKEN: ${{ github.token }}
PR_LABELS_JSON: ${{ toJson(github.event.pull_request.labels.*.name) }}
steps:
- name: Get PR info
id: get-pr
run: |
if [ "${{ github.event_name }}" = "merge_group" ]; then
PR_NUMBER=$(echo "${{ github.ref }}" | grep -oP '(?<=/pr-)\d+' || echo "")
PR_LABELS=$(gh api "repos/${{ github.repository }}/pulls/$PR_NUMBER" | jq -c '[.labels[].name] // []')
else
PR_NUMBER="${{ github.event.pull_request.number }}"
PR_LABELS=$(echo "$PR_LABELS_JSON" | jq -c '.')
fi
echo "pr-number=$PR_NUMBER" >> "$GITHUB_OUTPUT"
echo "pr-labels=$PR_LABELS" >> "$GITHUB_OUTPUT"
check-halt-merges:
runs-on: ubuntu-latest
timeout-minutes: 5
needs: get-pr-info
if: always()
steps:
- name: Enforce HALT_MERGES gate
env:
PR_NUMBER: ${{ needs.get-pr-info.outputs.pr_number }}
run: |
# Default to 0 (allow all) if not set
if [ -z "$HALT_MERGES" ]; then
HALT_MERGES=0
fi
if [ "$HALT_MERGES" = "0" ]; then
echo "::debug::All merges allowed (HALT_MERGES=0)"
exit 0
elif [ "$HALT_MERGES" = "$PR_NUMBER" ]; then
echo "::debug::PR #$PR_NUMBER is explicitly allowed"
exit 0
else
if [ "$HALT_MERGES" -lt 0 ] 2>/dev/null; then
echo "::error::All merges are blocked (HALT_MERGES=$HALT_MERGES)"
else
echo "::warning::Only PR #$HALT_MERGES is allowed to merge"
fi
exit 1
fi
check-do-not-merge-label:
runs-on: ubuntu-latest
timeout-minutes: 5
needs: get-pr-info
if: always()
steps:
- name: Block when PR has the "${{ env.DO_NOT_MERGE_LABEL }}" label
if: contains(needs.get-pr-info.outputs.pr_labels, env.DO_NOT_MERGE_LABEL)
run: |
echo "::error::The label \"${{ env.DO_NOT_MERGE_LABEL }}\" is used to prevent merging."
exit 1
.github/workflows/pull-request-lint.yml
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name: PR Lint
on:
pull_request_target:
types: [opened, edited, synchronize, reopened]
branches: [main]
# Key on PR number because pull_request_target's github.ref is the base branch.
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number }}
cancel-in-progress: true
# Default to no permissions; grant minimally at the job level.
permissions:
actions: none
attestations: none
checks: none
contents: none
deployments: none
discussions: none
id-token: none
issues: none
models: none
packages: none
pages: none
pull-requests: none
repository-projects: none
security-events: none
statuses: none
jobs:
pr-title:
runs-on: ubuntu-latest
timeout-minutes: 5
permissions:
pull-requests: read
steps:
- uses: amannn/action-semantic-pull-request@48f256284bd46cdaab1048c3721360e808335d50 # v6.1.1
env:
GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }}
with:
types: |
feat
fix
docs
style
refactor
perf
test
chore
ci
requireScope: false
.github/workflows/scorecard-analysis.yml
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name: Scorecard Analysis
on:
push:
branches: [main]
schedule:
# Weekly on Mondays at 11:00 UTC (03:00 Pacific)
- cron: "0 11 * * 1"
workflow_dispatch: {}
permissions: {}
jobs:
analysis:
name: Scorecard analysis
runs-on: ubuntu-latest
timeout-minutes: 10
permissions:
contents: read
security-events: write # upload SARIF to code scanning
id-token: write # publish results to OSSF
steps:
- name: Checkout code
uses: actions/checkout@9c091bb21b7c1c1d1991bb908d89e4e9dddfe3e0 # v7.0.0
with:
persist-credentials: false
- name: Run analysis
uses: ossf/scorecard-action@4eaacf0543bb3f2c246792bd56e8cdeffafb205a # v2.4.3
with:
results_file: scorecard-results.sarif
results_format: sarif
# Scorecard team runs a weekly scan of public GitHub repos
# (https://github.com/ossf/scorecard#public-data). Setting
# `publish_results: true` lets the OSSF reuse our workflow output
# instead of scanning us independently.
publish_results: true
- name: Upload SARIF artifact
uses: actions/upload-artifact@043fb46d1a93c77aae656e7c1c64a875d1fc6a0a # v7.0.1
with:
name: SARIF file
path: scorecard-results.sarif
retention-days: 14
- name: Upload to code scanning
uses: github/codeql-action/upload-sarif@5e7a52feb2a3dfb87f88be2af33b9e2275f48de6 # v4.32.2
with:
sarif_file: scorecard-results.sarif
.github/workflows/skill-frontmatter-check.yml
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name: Skill Frontmatter Check
on:
pull_request_target:
types: [opened, synchronize]
branches: [main]
paths:
- "**/SKILL.md"
concurrency:
group: ${{ github.workflow }}-${{ github.event.pull_request.number }}
cancel-in-progress: true
permissions:
contents: read
pull-requests: write
jobs:
check-frontmatter:
runs-on: ubuntu-latest
timeout-minutes: 5
steps:
- name: Check SKILL.md frontmatter via API
uses: actions/github-script@f28e40c7f34bde8b3046d885e986cb6290c5673b # v7
with:
retries: 3
script: |
const DISALLOWED_FIELDS = new Set(['stages']);
const DISALLOWED_PREFIXES = ['owner_'];
const files = await github.paginate(
github.rest.pulls.listFiles,
{
owner: context.repo.owner,
repo: context.repo.repo,
pull_number: context.payload.pull_request.number,
per_page: 100
}
);
const skillFiles = files
.filter(f => f.filename.endsWith('SKILL.md') && f.status !== 'removed');
if (skillFiles.length === 0) {
core.info('No SKILL.md files changed.');
return;
}
const violations = [];
for (const file of skillFiles) {
const { data } = await github.rest.repos.getContent({
owner: context.payload.pull_request.head.repo.owner.login,
repo: context.payload.pull_request.head.repo.name,
path: file.filename,
ref: context.payload.pull_request.head.sha
});
const content = Buffer.from(data.content, 'base64').toString('utf-8');
const lines = content.split('\n');
if (lines[0] !== '---') continue;
const disallowed = [];
for (let i = 1; i < lines.length; i++) {
if (lines[i] === '---') break;
const match = lines[i].match(/^([a-zA-Z_][a-zA-Z0-9_-]*):/);
if (match && (DISALLOWED_FIELDS.has(match[1]) || DISALLOWED_PREFIXES.some(p => match[1].startsWith(p)))) {
disallowed.push(match[1]);
}
}
if (disallowed.length > 0) {
violations.push({ path: file.filename, fields: disallowed });
}
}
if (violations.length === 0) {
core.info('All SKILL.md files pass frontmatter check.');
return;
}
const list = violations
.map(v => `- \`${v.path}\`: ${v.fields.map(f => '`' + f + '`').join(', ')}`)
.join('\n');
const body = [
'⚠️ **PR closed automatically.**',
'',
'SKILL.md frontmatter contains fields that are not permitted in this public repository:',
'',
list,
'',
'Please remove these fields and reopen the PR.'
].join('\n');
await github.rest.issues.createComment({
owner: context.repo.owner,
repo: context.repo.repo,
issue_number: context.payload.pull_request.number,
body
});
await github.rest.pulls.update({
owner: context.repo.owner,
repo: context.repo.repo,
pull_number: context.payload.pull_request.number,
state: 'closed'
});
core.setFailed('SKILL.md files contained disallowed frontmatter fields.');
.github/workflows/stale.yml
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@@ -0,0 +1,43 @@
name: Stale
on:
schedule:
- cron: "0 0 * * *"
workflow_dispatch:
# Default to no permissions; grant minimally at the job level.
permissions:
actions: none
attestations: none
checks: none
contents: none
deployments: none
discussions: none
id-token: none
issues: none
models: none
packages: none
pages: none
pull-requests: none
repository-projects: none
security-events: none
statuses: none
jobs:
stale:
runs-on: ubuntu-latest
timeout-minutes: 10
permissions:
issues: write
pull-requests: write
steps:
- uses: actions/stale@eb5cf3af3ac0a1aa4c9c45633dd1ae542a27a899 # v10.3.0
with:
stale-pr-message: "This PR has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs."
stale-issue-message: "This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs."
days-before-pr-stale: 14
days-before-pr-close: 7
days-before-issue-stale: 60
days-before-issue-close: 14
stale-pr-label: stale
stale-issue-label: stale
.gitignore
+13
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@@ -0,0 +1,13 @@
node_modules/
dist/
build/
.dprint/
.ruff_cache/
.mise/
.tmp/
.DS_Store
.idea/
.vscode/
.env
.claude/settings.local.json
__pycache__/
.markdownlint-cli2.yaml
+21
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@@ -0,0 +1,21 @@
config:
default: true
MD003: false
MD013: false
MD022: false
MD033:
allowed_elements:
- details
- summary
- Resource
- model-id
- region
- Name
MD024:
siblings_only: true
MD026: false
MD034: false
MD040: false
MD041: false
MD060: false
frontMatter: "^---\\s*$[\\s\\S]*?^---\\s*$"
CODE_OF_CONDUCT.md
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@@ -0,0 +1,5 @@
## Code of Conduct
This project has adopted the [Amazon Open Source Code of Conduct](https://aws.github.io/code-of-conduct).
For more information see the [Code of Conduct FAQ](https://aws.github.io/code-of-conduct-faq) or contact
opensource-codeofconduct@amazon.com with any additional questions or comments.
CONTRIBUTING.md
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# Contributing
Thank you for your interest in this project. This project is not accepting external code contributions at this time. You can still help us improve the project by reporting bugs, requesting features, and reporting security issues.
## Table of Contents
- [Reporting Bugs](#reporting-bugs)
- [Feature Requests](#feature-requests)
- [Security](#security)
## Reporting Bugs
- Before reporting a bug, make sure you are on the latest version.
- Check existing issues to see if the bug has already been reported.
- Submit a [GitHub Issue](https://github.com/aws/agent-toolkit-for-aws/issues/new?template=bug_report.yml) with detailed steps to reproduce the bug, plus your system information (AI assistant and version, operating system).
## Feature Requests
- Before submitting a feature request, make sure you are on the latest version.
- Check existing issues to see if the feature has already been requested.
- Submit a [GitHub Issue](https://github.com/aws/agent-toolkit-for-aws/issues/new?template=feature_request.yml) with a clear description of the feature and your use case.
## Security
If you discover a potential security issue in this project, please notify AWS/Amazon Security via the [vulnerability reporting page](https://aws.amazon.com/security/vulnerability-reporting/) or directly via email to aws-security@amazon.com. Please do **not** create a public GitHub issue, pull request, or other public disclosure.
See [SECURITY.md](SECURITY.md) for more information.
LICENSE
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@@ -0,0 +1,175 @@
Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
and distribution as defined by Sections 1 through 9 of this document.
"Licensor" shall mean the copyright owner or entity authorized by
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other entities that control, are controlled by, or are under common
control with that entity. For the purposes of this definition,
"control" means (i) the power, direct or indirect, to cause the
direction or management of such entity, whether by contract or
otherwise, or (ii) ownership of fifty percent (50%) or more of the
outstanding shares, or (iii) beneficial ownership of such entity.
"You" (or "Your") shall mean an individual or Legal Entity
exercising permissions granted by this License.
"Source" form shall mean the preferred form for making modifications,
including but not limited to software source code, documentation
source, and configuration files.
"Object" form shall mean any form resulting from mechanical
transformation or translation of a Source form, including but
not limited to compiled object code, generated documentation,
and conversions to other media types.
"Work" shall mean the work of authorship, whether in Source or
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(an example is provided in the Appendix below).
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to that Work or Derivative Works thereof, that is intentionally
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to the Licensor or its representatives, including but not limited to
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designated in writing by the copyright owner as "Not a Contribution."
"Contributor" shall mean Licensor and any individual or Legal Entity
on behalf of whom a Contribution has been received by Licensor and
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2. Grant of Copyright License. Subject to the terms and conditions of
this License, each Contributor hereby grants to You a perpetual,
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Work and such Derivative Works in Source or Object form.
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this License, each Contributor hereby grants to You a perpetual,
worldwide, non-exclusive, no-charge, royalty-free, irrevocable
(except as stated in this section) patent license to make, have made,
use, offer to sell, sell, import, and otherwise transfer the Work,
where such license applies only to those patent claims licensable
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Contribution(s) alone or by combination of their Contribution(s)
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4. Redistribution. You may reproduce and distribute copies of the
Work or Derivative Works thereof in any medium, with or without
modifications, and in Source or Object form, provided that You
meet the following conditions:
(a) You must give any other recipients of the Work or
Derivative Works a copy of this License; and
(b) You must cause any modified files to carry prominent notices
stating that You changed the files; and
(c) You must retain, in the Source form of any Derivative Works
that You distribute, all copyright, patent, trademark, and
attribution notices from the Source form of the Work,
excluding those notices that do not pertain to any part of
the Derivative Works; and
(d) If the Work includes a "NOTICE" text file as part of its
distribution, then any Derivative Works that You distribute must
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of the following places: within a NOTICE text file distributed
as part of the Derivative Works; within the Source form or
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within a display generated by the Derivative Works, if and
wherever such third-party notices normally appear. The contents
of the NOTICE file are for informational purposes only and
do not modify the License. You may add Your own attribution
notices within Derivative Works that You distribute, alongside
or as an addendum to the NOTICE text from the Work, provided
that such additional attribution notices cannot be construed
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You may add Your own copyright statement to Your modifications and
may provide additional or different license terms and conditions
for use, reproduction, or distribution of Your modifications, or
for any such Derivative Works as a whole, provided Your use,
reproduction, and distribution of the Work otherwise complies with
the conditions stated in this License.
5. Submission of Contributions. Unless You explicitly state otherwise,
any Contribution intentionally submitted for inclusion in the Work
by You to the Licensor shall be under the terms and conditions of
this License, without any additional terms or conditions.
Notwithstanding the above, nothing herein shall supersede or modify
the terms of any separate license agreement you may have executed
with Licensor regarding such Contributions.
6. Trademarks. This License does not grant permission to use the trade
names, trademarks, service marks, or product names of the Licensor,
except as required for reasonable and customary use in describing the
origin of the Work and reproducing the content of the NOTICE file.
7. Disclaimer of Warranty. Unless required by applicable law or
agreed to in writing, Licensor provides the Work (and each
Contributor provides its Contributions) on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
implied, including, without limitation, any warranties or conditions
of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
PARTICULAR PURPOSE. You are solely responsible for determining the
appropriateness of using or redistributing the Work and assume any
risks associated with Your exercise of permissions under this License.
8. Limitation of Liability. In no event and under no legal theory,
whether in tort (including negligence), contract, or otherwise,
unless required by applicable law (such as deliberate and grossly
negligent acts) or agreed to in writing, shall any Contributor be
liable to You for damages, including any direct, indirect, special,
incidental, or consequential damages of any character arising as a
result of this License or out of the use or inability to use the
Work (including but not limited to damages for loss of goodwill,
work stoppage, computer failure or malfunction, or any and all
other commercial damages or losses), even if such Contributor
has been advised of the possibility of such damages.
9. Accepting Warranty or Additional Liability. While redistributing
the Work or Derivative Works thereof, You may choose to offer,
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License. However, in accepting such obligations, You may act only
on Your own behalf and on Your sole responsibility, not on behalf
of any other Contributor, and only if You agree to indemnify,
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of your accepting any such warranty or additional liability.
NOTICE
+1
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@@ -0,0 +1 @@
Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
README.md
+190
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@@ -0,0 +1,190 @@
# Agent Toolkit for AWS
[![License](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](LICENSE)
[![Build](https://github.com/aws/agent-toolkit-for-aws/actions/workflows/build.yml/badge.svg)](https://github.com/aws/agent-toolkit-for-aws/actions/workflows/build.yml)
[![Status](https://img.shields.io/badge/status-GA-green.svg)](https://github.com/aws/agent-toolkit-for-aws)
Help AI coding agents build, deploy, and manage applications on AWS.
The Agent Toolkit for AWS gives AI coding agents the tools, knowledge, and guardrails they need to work with AWS services. It works with the coding agents developers already use — including Claude Code, Codex, Cursor, and Kiro.
## Quick start
### AWS CLI
Use the Agent Toolkit directly from your terminal with the AWS CLI:
```
aws configure agent-toolkit
```
See the [AWS CLI integration guide](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/aws-cli.html) for setup, configuration, and usage instructions.
### Claude Code
The plugins are available on the official Anthropic marketplace (`claude-plugins-official`) which is added to your Claude Code installation by default.
Use the following commands to install supported plugins from the toolkit:
For `aws-core` that covers service selection, CDK/CloudFormation, serverless, containers, storage, observability, billing, SDK usage, and deployment:
```
/plugin install aws-core@claude-plugins-official
```
> **Tip:** If you get `Plugin not found`, update your local marketplace index first:
>
> ```
> /plugin marketplace update claude-plugins-official
> ```
For `aws-agents` that covers building AI agents on AWS with Amazon Bedrock and AgentCore:
```
/plugin install aws-agents@claude-plugins-official
```
For `aws-data-analytics` that covers data lake, analytics, and ETL workflows with S3 Tables, AWS Glue, and Athena:
```
/plugin install aws-data-analytics@claude-plugins-official
```
For `aws-agents-for-devsecops` used to investigate incidents, review code and execute UAT for release readiness, scan code for vulnerabilities, and run penetration tests with AWS DevOps Agent and AWS Security Agent.
```
/plugin marketplace add aws/agent-toolkit-for-aws
/plugin install aws-agents-for-devsecops
/reload-plugins
# Or from Claude's official marketplace:
/plugin install aws-agents-for-devsecops@claude-plugins-official
/reload-plugins
# Setup:
/aws-agents-for-devsecops:setup
```
### Codex
In your terminal:
```
codex plugin marketplace add aws/agent-toolkit-for-aws
```
Then launch Codex and run `/plugins` to browse and install the **aws-core** plugin.
### Cursor
Add this repository as a team marketplace from **Settings → Plugins → Team Marketplaces → Add Marketplace → Import from Repo**, pointing it at `aws/agent-toolkit-for-aws`. Cursor indexes the plugins listed in [`.cursor-plugin/marketplace.json`](.cursor-plugin/marketplace.json) on import.
Then open the **Plugins** panel and install the **aws-core** plugin (start here), or **aws-agents** and **aws-data-analytics** as needed. Each plugin bundles the AWS MCP Server configuration and agent skills.
### Kiro
Kiro setup has two independent parts: the AWS MCP Server (for runtime AWS API access and documentation search) and local skills (for task-specific agent guidance). They complement each other but work independently — skills don't require the MCP server, and the MCP server doesn't serve locally-installed skills.
**1. Add the AWS MCP Server** to your Kiro MCP configuration (`.kiro/settings/mcp.json`):
```json
{
"mcpServers": {
"aws": {
"command": "uvx",
"args": [
"mcp-proxy-for-aws@1.6.3",
"https://aws-mcp.us-east-1.api.aws/mcp",
"--metadata",
"AWS_REGION=us-west-2"
]
}
}
}
```
> **Note:** It is recommended to pin to a specific version (e.g., `@1.6.3`) to ensure reproducible behavior and protect against supply chain risks. We recommend regularly checking [PyPI](https://pypi.org/project/mcp-proxy-for-aws/) for new stable versions and updating accordingly.
The MCP server gives your agent access to AWS APIs, sandboxed script execution, and real-time documentation search.
**2. Install skills** from this repository:
```
npx skills add aws/agent-toolkit-for-aws/skills
```
This installs skill files to `~/.kiro/skills/` (global) or `.kiro/skills/` (project-level). Each skill is a directory containing a `SKILL.md` file and optionally a `references/` subdirectory with additional context the agent reads from the local filesystem when needed. Kiro discovers installed skills automatically and activates them on demand when a task matches.
> **Prerequisites:** You need [uv](https://docs.astral.sh/uv/) installed. An AWS account with credentials configured locally is required for API calls and script execution, but not for documentation search or skill discovery. See the [user guide](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/) for detailed setup instructions.
### Other agents
See the [AWS MCP Server getting started guide](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/getting-started-aws-mcp-server.html) for instructions on configuring the AWS MCP Server with your agent.
Then install skills from this repository:
```
npx skills add aws/agent-toolkit-for-aws/skills
```
> **Prerequisites:** You need [uv](https://docs.astral.sh/uv/) installed. An AWS account with credentials configured locally is required for API calls and script execution, but not for documentation search or skill discovery. See the [user guide](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/) for detailed setup instructions.
## What's included
### Plugins
Plugins bundle the AWS MCP Server configuration and agent skills into a single install for your coding agent.
| Plugin | Description |
| ------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| [aws-core](plugins/aws-core/) | Core AWS skills and MCP Server configuration. Covers service selection, CDK/CloudFormation, serverless, containers, storage, observability, billing, SDK usage, and deployment. **Start here.** |
| [aws-agents](plugins/aws-agents/) | Skills for building AI agents on AWS with Amazon Bedrock and AgentCore. |
| [aws-data-analytics](plugins/aws-data-analytics/) | Skills for data lake, analytics, and ETL workflows with S3 Tables, AWS Glue, and Athena. |
| [aws-agents-for-devsecops](plugins/aws-agents-for-devsecops/) | Investigate incidents, review code and execute UAT for release readiness, scan code for vulnerabilities, and run penetration tests with [AWS DevOps Agent](https://aws.amazon.com/devops-agent/?trk=7b4b0d25-1409-441c-b914-c5d08677c376&sc_channel=ghr) and [AWS Security Agent](https://aws.amazon.com/security-agent/?trk=7b4b0d25-1409-441c-b914-c5d08677c376&sc_channel=ghr). |
Plugins are currently available for Claude Code, Codex, and Cursor. For other agents, configure the AWS MCP Server directly and install skills from this repository.
### Skills
Agent skills are curated packages of instructions and reference materials that help agents complete specific AWS tasks. Skills are loaded on demand — agents discover and retrieve only what's relevant to the current task.
```
npx skills add aws/agent-toolkit-for-aws/skills
```
Browse the [`skills/`](skills/) directory to see all available skills.
### Rules files
Recommended project-level configuration files that tell agents how to use AWS most effectively — for example, by using the AWS MCP Server, discovering available skills, or searching documentation before acting.
See [`rules/`](rules/) for details.
### AWS MCP Server
The [AWS MCP Server](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/understanding-mcp-server-tools.html) is a managed server that gives agents access to AWS through the Model Context Protocol. It provides:
- **Full AWS API coverage** — Interact with any of the 300+ AWS services through a single authenticated endpoint.
- **Sandboxed script execution** — Agents can run Python scripts in an isolated environment for complex multi-step operations.
- **Real-time documentation access** — Search and retrieve current AWS documentation, API references, and service capabilities without authentication.
- **Enterprise controls** — Amazon CloudWatch metrics, IAM context keys for agent-specific policies, and AWS CloudTrail audit logging.
For details on operation, available tools, authentication, and supported Regions, see the [AWS MCP Server documentation](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/understanding-mcp-server-tools.html).
## Documentation
- [User guide](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/) — Setup, configuration, and reference documentation.
- [AWS MCP Server tools](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/understanding-mcp-server-tools.html) — Reference for all available MCP tools.
## How the Agent Toolkit relates to the MCP servers, skills, and plugins in AWS Labs
In 2025, AWS began releasing MCP servers, skills, and plugins as part of [AWS Labs](https://github.com/awslabs). The Agent Toolkit for AWS is the successor to those tools. We recommend using the Agent Toolkit for AWS, because it offers key features including:
- IAM condition keys that distinguish between agent actions and human actions, so you can write policies that apply only to agents. For example, you can write policies that only allow read-only actions through the MCP server, even if the user’s underlying IAM role can take write actions).
- CloudWatch metrics and CloudTrail audit logging for every request, so you can monitor and audit coding agent activity.
- Agent skills that have undergone thorough end-to-end evaluations, so you can be confident that workflows will complete successfully.
[AWS Labs](https://github.com/awslabs) MCP servers, skills, and plugins will continue to work and accept contributions, and over time the best of AWS Labs will be transitioned to the Agent Toolkit for AWS to ensure that customers can access the broadest array of tooling and guidance for their agents.
## License
This project is licensed under the Apache-2.0 License. See [LICENSE](LICENSE) for details.
README.wehub.md
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# WeHub 来源说明
- 原始项目：`aws/agent-toolkit-for-aws`
- 原始仓库：https://github.com/aws/agent-toolkit-for-aws
- 导入方式：上游默认分支的最新快照
- 原作者、版权和许可证信息以原始仓库及本仓库 LICENSE 为准
- 本文件仅用于记录来源，不代表 WeHub 是原项目作者
SUPPORT.md
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# Support
Thank you for using the Agent Toolkit for AWS. Here's how to get help.
## Documentation
- [User guide](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/) — Setup, configuration, and reference documentation.
- [AWS MCP Server tools](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/understanding-mcp-server-tools.html) — Reference for all available MCP tools.
## Bug reports
Please open a [bug report](https://github.com/aws/agent-toolkit-for-aws/issues/new?template=bug_report.yml) on GitHub. Include your AI assistant (and version), operating system, and steps to reproduce.
## Feature requests
Please open a [feature request](https://github.com/aws/agent-toolkit-for-aws/issues/new?template=feature_request.yml) on GitHub with a clear description of the feature and your use case.
## Security vulnerabilities
Do **not** open a public GitHub issue for security concerns. Report vulnerabilities through the [AWS vulnerability reporting page](https://aws.amazon.com/security/vulnerability-reporting/) or email aws-security@amazon.com. See [SECURITY.md](https://github.com/aws/agent-toolkit-for-aws/security/policy) for details.
## AWS support
For issues with AWS services themselves (as opposed to this toolkit), use your existing AWS support channels:
- [AWS Support Center](https://console.aws.amazon.com/support/home) if you have an AWS Support plan.
- [AWS re:Post](https://repost.aws/) for community-driven Q&A.
mise.toml
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@@ -0,0 +1,30 @@
[tools]
node = "24"
gitleaks = "latest"
[tools."npm:markdownlint-cli2"]
version = "0.18"
[tasks.lint]
description = "Run all linters"
depends = ["lint:md", "lint:manifests"]
[tasks."lint:md"]
description = "Lint markdown files"
run = "npx markdownlint-cli2 '**/*.md' '#node_modules'"
[tasks."lint:manifests"]
description = "Validate all manifests and skill frontmatter"
run = "python3 tools/validate.py"
[tasks.validate]
description = "Run all validation"
depends = ["lint:manifests"]
[tasks.security]
description = "Run security scans"
run = "gitleaks detect --source . --verbose"
[tasks.build]
description = "Full build: lint + validate + security"
depends = ["lint", "validate", "security"]
plugins/aws-agents-for-devsecops/.claude-plugin/plugin.json
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{
"name": "aws-agents-for-devsecops",
"description": "Investigate incidents, review code and execute UAT for release readiness, scan code for vulnerabilities, and run penetration tests with AWS DevOps Agent and AWS Security Agent.",
"version": "1.0.0",
"author": {
"name": "Amazon Web Services"
},
"homepage": "https://docs.aws.amazon.com/devopsagent/latest/userguide/",
"repository": "https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-agents-for-devsecops",
"license": "Apache-2.0",
"keywords": [
"Amazon Web Services",
"agentspace",
"api-testing",
"appsec",
"architecture-review",
"aws",
"cloudwatch",
"code-review",
"cost-optimization",
"devops",
"devsecops",
"incident",
"investigation",
"is-my-code-secure",
"mcp",
"observability",
"operations",
"penetration-test",
"pentest",
"pre-merge",
"qa",
"release",
"release-analysis",
"release-readiness",
"release-testing",
"reliability",
"remediation",
"risk-analysis",
"security",
"security-scan",
"security-vulnerabilities",
"testing",
"threat-model",
"topology",
"uat",
"ui-testing"
]
}
plugins/aws-agents-for-devsecops/.codex-plugin/plugin.json
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@@ -0,0 +1,72 @@
{
"name": "aws-agents-for-devsecops",
"version": "1.0.0",
"description": "Investigate incidents, review code and execute UAT for release readiness, scan code for vulnerabilities, and run penetration tests with AWS DevOps Agent and AWS Security Agent.",
"author": {
"name": "Amazon Web Services",
"url": "https://github.com/aws/agent-toolkit-for-aws"
},
"homepage": "https://docs.aws.amazon.com/devopsagent/latest/userguide/",
"repository": "https://github.com/aws/agent-toolkit-for-aws",
"license": "Apache-2.0",
"keywords": [
"Amazon Web Services",
"agentspace",
"api-testing",
"appsec",
"architecture-review",
"aws",
"cloudwatch",
"code-review",
"cost-optimization",
"devops",
"devsecops",
"incident",
"investigation",
"is-my-code-secure",
"mcp",
"observability",
"operations",
"penetration-test",
"pentest",
"pre-merge",
"qa",
"release",
"release-analysis",
"release-readiness",
"release-testing",
"reliability",
"remediation",
"risk-analysis",
"security",
"security-scan",
"security-vulnerabilities",
"testing",
"threat-model",
"topology",
"uat",
"ui-testing"
],
"skills": "./skills/",
"mcpServers": "./.mcp.json",
"interface": {
"displayName": "AWS Agents for DevSecOps",
"shortDescription": "Investigate incidents, scan code for vulnerabilities, and run penetration tests with AWS DevOps Agent and AWS Security Agent.",
"longDescription": "Bring your AWS DevOps Agent and AWS Security Agent capabilities into your coding agent. Investigate production incidents by querying logs, metrics, and traces across your connected observability tools. Scan source code for vulnerabilities, run penetration tests against live endpoints, and apply auto-generated fixes \u2014 all through conversational prompts without leaving your editor.\n\nTwo agents power the experience: AWS DevOps Agent for release readiness review of code changes, automated release testing of web and API based applications, and operational incident response, topology exploration, and telemetry queries across Grafana, Datadog, Splunk, New Relic, and CloudWatch; and AWS Security Agent for automated code security scanning, penetration testing, and vulnerability remediation. The plugin connects to your existing Agent Spaces and respects all configured IAM policies, tool allowlists, and integration settings.\n\nHow to use: After installing, configure your AWS credentials. Then use natural language prompts such as investigate why my service is returning 500 errors, scan this directory for security vulnerabilities, run a pentest against my staging endpoint, or what's alarming in my Grafana dashboards.",
"defaultPrompt": [
"investigate why my service is returning 500 errors.",
"scan this directory for security vulnerabilities.",
"run a pentest against my staging endpoint."
],
"developerName": "Amazon Web Services",
"category": "Cloud",
"capabilities": [
"Read",
"Write"
],
"websiteURL": "https://github.com/aws/agent-toolkit-for-aws",
"privacyPolicyURL": "https://aws.amazon.com/privacy/",
"termsOfServiceURL": "https://aws.amazon.com/service-terms/",
"brandColor": "#FF9900"
}
}
plugins/aws-agents-for-devsecops/.cursor-plugin/plugin.json
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@@ -0,0 +1,53 @@
{
"name": "aws-agents-for-devsecops",
"displayName": "AWS Agents for DevSecOps",
"description": "Investigate incidents, review code and execute UAT for release readiness, scan code for vulnerabilities, and run penetration tests with AWS DevOps Agent and AWS Security Agent.",
"version": "1.0.0",
"author": {
"name": "Amazon Web Services"
},
"homepage": "https://github.com/aws/agent-toolkit-for-aws",
"repository": "https://github.com/aws/agent-toolkit-for-aws/tree/main/plugins/aws-agents-for-devsecops",
"license": "Apache-2.0",
"category": "developer-tools",
"keywords": [
"Amazon Web Services",
"agentspace",
"api-testing",
"appsec",
"architecture-review",
"aws",
"cloudwatch",
"code-review",
"cost-optimization",
"devops",
"devsecops",
"incident",
"investigation",
"is-my-code-secure",
"mcp",
"observability",
"operations",
"penetration-test",
"pentest",
"pre-merge",
"qa",
"release",
"release-analysis",
"release-readiness",
"release-testing",
"reliability",
"remediation",
"risk-analysis",
"security",
"security-scan",
"security-vulnerabilities",
"testing",
"threat-model",
"topology",
"uat",
"ui-testing"
],
"skills": "./skills/",
"mcpServers": "./.mcp.json"
}
plugins/aws-agents-for-devsecops/.mcp.json
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{
"mcpServers": {
"aws-devops-agent": {
"type": "http",
"url": "https://connect.aidevops.${DEVOPS_AGENT_REGION:-us-east-1}.api.aws/mcp",
"headers": {
"Authorization": "Bearer ${DEVOPS_AGENT_TOKEN}"
},
"timeout": 120000
}
}
}
plugins/aws-agents-for-devsecops/README.md
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# aws-agents-for-devsecops — Claude Code plugin
Investigate incidents, review code and execute UAT for release readiness, scan code for vulnerabilities, and run penetration tests with [AWS DevOps Agent](https://aws.amazon.com/devops-agent/?trk=7b4b0d25-1409-441c-b914-c5d08677c376&sc_channel=ghr) and [AWS Security Agent](https://aws.amazon.com/security-agent/?trk=7b4b0d25-1409-441c-b914-c5d08677c376&sc_channel=ghr).
## What's inside
| Component | Path | Trigger |
|-----------|------|---------|
| **Skill** `setup` | `skills/setup/` | Explicit invocation to setup both agents |
| **Skill** `setup-devops-agent` | `skills/setup-devops-agent/` | Model auto-invokes on first-time setup or credential errors |
| **Skill** `setup-security-agent` | `skills/setup-security-agent/` | Model auto-invokes for Security Agent workspace setup (agent space, role, bucket) |
| **Skill** `investigating-incidents-with-aws-devops-agent` | `skills/investigating-incidents-with-aws-devops-agent/` | Model auto-invokes on incident keywords (5xx, OOM, alarm, sev1, "investigate", "root cause"...) |
| **Skill** `chatting-with-aws-devops-agent` | `skills/chatting-with-aws-devops-agent/` | Model auto-invokes for cost / architecture / topology / knowledge questions |
| **Skill** `running-release-tests` | `skills/running-release-tests/` | Model auto-invokes for release testing (run tests, test profile, UI test, API test, QA, regression) |
| **Skill** `analyzing-release-readiness` | `skills/analyzing-release-readiness/` | Model auto-invokes for pre-merge release readiness reviews (review PR, risk analysis, safe to ship, ready to merge) |
| **Skill** `coordinating-multi-space-devops-agent` | `skills/coordinating-multi-space-devops-agent/` | Model auto-invokes when the user has more than one AgentSpace or asks across accounts |
| **Skill** `scanning-with-aws-security-agent` | `skills/scanning-with-aws-security-agent/` | Model auto-invokes for full code security scans |
| **Skill** `diff-scanning-with-aws-security-agent` | `skills/diff-scanning-with-aws-security-agent/` | Model auto-invokes for diff-only security scans (pre-commit, pre-PR) |
| **Skill** `pentesting-with-aws-security-agent` | `skills/pentesting-with-aws-security-agent/` | Model auto-invokes for penetration testing against live endpoints |
| **Skill** `threat-modeling-with-aws-security-agent` | `skills/threat-modeling-with-aws-security-agent/` | Model auto-invokes for STRIDE threat model reviews on design docs |
| **Skill** `remediating-with-aws-security-agent` | `skills/remediating-with-aws-security-agent/` | Model auto-invokes for fetching, triaging, and fixing security findings |
| **Command** `/aws-agents-for-devsecops:setup` | `commands/setup.md` | User and model invokes |
| **Command** `/aws-agents-for-devsecops:setup-devops-agent` | `commands/setup-devops-agent.md` | User and model invokes |
| **Command** `/aws-agents-for-devsecops:setup-security-agent` | `commands/setup-security-agent.md` | User and model invokes |
| **Command** `/aws-agents-for-devsecops:chat` | `commands/chat.md` | User types it explicitly |
| **Command** `/aws-agents-for-devsecops:investigate` | `commands/investigate.md` | User types it explicitly |
| **Command** `/aws-agents-for-devsecops:release-testing` | `commands/release-testing.md` | User types it explicitly |
| **Command** `/aws-agents-for-devsecops:release-readiness` | `commands/release-readiness.md` | User types it explicitly |
| **Command** `/aws-agents-for-devsecops:spaces` | `commands/spaces.md` | User types it explicitly |
| **Command** `/aws-agents-for-devsecops:cost` | `commands/cost.md` | User types it explicitly |
| **MCP server** `aws-devops-agent` | `.mcp.json` (written by setup) | Remote MCP server, Bearer or SigV4 |
## Available tools (remote server)
| Category | Tools |
|----------|-------|
| **Chat** | `chat`, `create_chat`, `send_message`, `list_chats` |
| **Investigation** | `investigate`, `create_investigation`, `get_task`, `list_tasks`, `list_journal_records`, `list_executions` |
| **Recommendations** | `list_recommendations`, `get_recommendation`, `update_recommendation` |
| **Release Testing** | `create_release_testing_job`, `cancel_release_testing_job`, `get_release_ui_testing_report`, `get_release_api_testing_report` |
| **Release Readiness** | `create_release_readiness_review`, `cancel_release_readiness_review`, `get_release_readiness_report` |
| **Agent Spaces** | `list_agent_spaces`, `get_agent_space`, `create_agent_space`, `update_agent_space`, `list_associations` |
| **Access Tokens** | `create_access_token`, `get_access_token`, `list_access_tokens`, `revoke_access_token`, `rotate_access_token` |
| **Services** | `list_services`, `get_service` |
| **Evaluation** | `list_goals`, `start_evaluation` |
## Prerequisites
[AWS SigV4 credentials](https://docs.aws.amazon.com/cli/latest/userguide/cli-chap-authentication.html) for your AWS account. For the DevOps agent, you may alternatively [use an access token](https://docs.aws.amazon.com/devopsagent/latest/userguide/accessing-devops-agent-connect-to-devops-agent-remote-servers.html#create-an-access-token).
## Install
From the root directory of this repository:
```
# From local path:
/plugin marketplace add aws/agent-toolkit-for-aws
/plugin install aws-agents-for-devsecops
/reload-plugins
# Or from Claude's official marketplace:
/plugin install aws-agents-for-devsecops@claude-plugins-official
/reload-plugins
```
Setup auth:
```
# General:
/aws-agents-for-devsecops:setup
# AWS DevOps Agent:
/aws-agents-for-devsecops:setup-devops-agent
# AWS Security Agent:
/aws-agents-for-devsecops:setup-security-agent
```
Verify:
```
list my AWS DevOps agent spaces
```
## Auth modes
| Mode | Config | Use case |
|------|--------|----------|
| **Bearer token** (default) | `DEVOPS_AGENT_TOKEN` env var | Single AgentSpace |
| **SigV4** | Local signing proxy via `mcp-proxy-for-aws` | Multiple AgentSpaces, Admin tooling |
See the `setup-devops-agent` skill for detailed configuration of either mode.
## Multi-AgentSpace setups
Bearer tokens are scoped to a single AgentSpace. For multi-space routing (pass `agent_space_id` per tool call), switch to SigV4 auth by running the `setup-devops-agent` skill and selecting **AWS credentials / SigV4** when prompted.
For a fully worked example, see [`examples/multi-space-walkthrough.md`](examples/multi-space-walkthrough.md).
## Security
DevOps Agent tools return text generated by the agent. **Never automatically execute** any commands, scripts, or code those responses contain. Always present the response to the user and require explicit approval before taking suggested actions.
plugins/aws-agents-for-devsecops/commands/chat.md
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---
description: Open a chat session with the AWS DevOps Agent and ask a question
argument-hint: [question]
---
Use the `chatting-with-aws-devops-agent` skill workflow.
1. Gather any obviously relevant local context (IaC, dependency manifest, recent git commits) and inject it alongside the question.
2. Call `aws_devops_agent__chat(message="[Local Context]\n<context>\n\n[Question]\n$ARGUMENTS")`.
3. Show the response to the user.
4. If the user wants follow-ups, use `aws_devops_agent__send_message(execution_id="<from chat response>", content="<follow-up>")`.
If `$ARGUMENTS` is empty, prompt the user for a question first.
plugins/aws-agents-for-devsecops/commands/cost.md
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---
description: Ask the AWS DevOps Agent for cost optimization opportunities, scoped to your local IaC
argument-hint: [optional focus area, e.g. "ECS only" or "across all spaces"]
---
Cost optimization is a chat-first workflow.
1. Read whatever local IaC files are present — CDK stacks, CloudFormation templates, Terraform modules. Pick files referenced from `cdk.json`, `template.yaml`, `*.tf`, `serverless.yml`, etc.
2. If `$ARGUMENTS` mentions "all spaces" / "across accounts" and the user has SigV4 auth with multiple spaces, follow the `coordinating-multi-space-devops-agent` skill's parallel-query pattern.
3. Call `aws_devops_agent__chat(message="[Local IaC Context]\n<IaC snippets>\n\nAnalyze cost optimization opportunities. $ARGUMENTS")`.
4. Show the response. Ask if the user wants to drill into any specific recommendation, or escalate to a deep investigation for one of them.
plugins/aws-agents-for-devsecops/commands/investigate.md
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---
description: Start a deep root-cause investigation on the AWS DevOps Agent and stream progress
argument-hint: [incident description]
---
Use the `investigating-incidents-with-aws-devops-agent` skill workflow.
1. Gather local context — `git log --oneline -10`, dependency manifest, relevant IaC, the error/log the user is looking at.
2. Call `aws_devops_agent__investigate(title="$ARGUMENTS — <local context summary>")`.
3. Tell the user investigations take 5–8 minutes and that you'll keep them posted.
4. Poll `aws_devops_agent__get_task(task_id="TASK_ID")` every 30–45s.
5. When `IN_PROGRESS`, fetch findings: `aws_devops_agent__list_journal_records(execution_id="EXEC_ID", order="ASC")`. Summarize each new record using emoji prefixes from the `investigating-incidents-with-aws-devops-agent` skill.
6. On `COMPLETED`: pull final findings, then call `aws_devops_agent__list_recommendations(task_id="TASK_ID")` for mitigations. Show the user the proposed fix — **do not** auto-apply.
If `$ARGUMENTS` is empty, ask the user for a one-line incident description first.
plugins/aws-agents-for-devsecops/commands/release-readiness.md
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---
description: Trigger a pre-merge release readiness review on a GitHub PR, GitLab MR, or local branch
argument-hint: [PR/MR URL or repo name]
---
Read and follow the `analyzing-release-readiness` skill for full execution details.
**IMPORTANT: NEVER use `gh` CLI, `glab` CLI, `curl`, or any external tool to fetch PR/MR details. All required fields (repository, prNumber/mergeRequestIid, hostname) MUST be parsed directly from the URL string. The DevOps Agent fetches the content itself.**
## Step 0 — Choose your execution path (DO THIS FIRST)
Check your available tools. Do you have ALL of these tools?
- `aws_devops_agent__create_release_readiness_review`
- `aws_devops_agent__get_task`
- `aws_devops_agent__list_journal_records`
- `aws_devops_agent__get_release_readiness_report`
These tools are NOT deferred/lazy-loaded — if they do not appear in your tool list, they are unavailable. Do NOT search for them via ToolSearch.
- **YES (all present)** → Use the "Remote Server" path below
- **NO** → Tell the user: "Remote server not configured." Then prompt the user with instructions from the `setup-devops-agent` skill if they intend to set up the connection. If not, mention that you are "proceeding with the AWS CLI fallback." Then use the Fallback (CLI) path below.
---
## Common to both paths (see skill: "Gathering execution parameters")
1. If `$ARGUMENTS` contains a URL (github.com or gitlab.com), parse the PR/MR details directly from the URL string — do NOT fetch or inspect the PR via any tool.
2. If `$ARGUMENTS` is a repo name or path, use the "Local GitHub/GitLab repo" flow below.
3. If `$ARGUMENTS` is empty, check the current git repository and use the local flow.
4. Build the `content` object following the skill's "Gathering execution parameters" section.
5. Ask the user about automated testing (static-only vs full analysis). Do NOT proceed until the user answers.
## Remote Server path (see skill: "Core workflow")
1. Call `aws_devops_agent__create_release_readiness_review(content={...}, skip_automated_testing=...)`.
2. Poll `aws_devops_agent__get_task(task_id=TASK_ID)` every 30s.
3. Stream progress via `aws_devops_agent__list_journal_records(execution_id=EXEC_ID, order="ASC")`.
4. On `COMPLETED`: call `aws_devops_agent__get_release_readiness_report(execution_id=EXEC_ID)`, save to file, and run the auto-fix flow from the skill.
## Fallback (CLI) path
Use this path when the remote server tools are unavailable.
1. List agent spaces with `aws devops-agent list-agent-spaces --region us-east-1` and ask the user which one to use. **Do NOT proceed until the user has selected one.**
2. Build the `content` object using the guidance from the `analyzing-release-readiness` skill's "Gathering execution parameters" section. Key rules: `githubPrContent`/`gitlabMrContent` MUST be an array, `prNumber`/`mergeRequestIid` MUST be strings.
3. Start the job (**CRITICAL:** `content` must be a single object, NOT wrapped in a list. Correct: `{"githubPrContent": [...]}`. Wrong: `[{"githubPrContent": [...]}]`):
```
aws devops-agent create-backlog-task \
--agent-space-id SPACE_ID \
--task-type RELEASE_READINESS_REVIEW \
--title 'Release Readiness Review' \
--priority MEDIUM \
--description '{"agentInput": {"content": <CONTENT_JSON>, "metadata": {"skipAutomatedTesting": true/false}}}' \
--region us-east-1
```
4. Poll for status every 30s:
```
aws devops-agent get-backlog-task \
--agent-space-id SPACE_ID \
--task-id TASK_ID \
--region us-east-1
```
5. Stream progress — once `IN_PROGRESS`, poll journal records and present updates to the user:
```
aws devops-agent list-journal-records \
--agent-space-id SPACE_ID \
--execution-id EXEC_ID \
--order ASC \
--region us-east-1
```
Use `next_token` from the response to fetch only new records on subsequent polls. **Wait 15 seconds** between each poll iteration. Keep polling until the task reaches a terminal status (`COMPLETED`, `FAILED`, `CANCELED`, `TIMED_OUT`).
6. On `COMPLETED`, retrieve the report:
```
aws devops-agent list-journal-records \
--agent-space-id SPACE_ID \
--execution-id EXEC_ID \
--record-type release_analysis_report \
--order ASC \
--region us-east-1
```
Save the report to `release-readiness-review-<YYYY-MM-DD-HHmmss>.md` and run the auto-fix flow from the skill.
On `FAILED` or `TIMED_OUT`: present the error and suggest next steps. On `CANCELED`: inform the user no report is available.
7. After analysis completes, clean up the review branch (if local flow was used — see below).
8. To cancel a running job:
```
aws devops-agent update-backlog-task \
--agent-space-id SPACE_ID \
--task-id TASK_ID \
--task-status CANCELED \
--region us-east-1
```
---
## Local GitHub/GitLab repo flow (no PR/MR URL provided)
When `$ARGUMENTS` is a repo name/path or empty (steps 2-3 above), execute this flow to prepare the content object. The review agent needs a pushed branch to read from — do NOT shortcut.
1. **Navigate to the repository directory**: `cd` to the repo root. Ask the user if needed.
2. **Determine the base branch**: Use `main` unless the user specifies otherwise. Verify:
```bash
BASE_BRANCH="main"
if ! git show-ref --verify --quiet refs/remotes/origin/$BASE_BRANCH; then
git fetch origin $BASE_BRANCH
fi
```
If fetch fails, ask the user to specify the base branch and stop.
3. **Check for local changes**: Run `git status --short` and `git rev-list --count origin/$BASE_BRANCH..HEAD`:
- **Clean AND not ahead**: Nothing to analyze — stop.
- **Has uncommitted changes**: Tell the user what will be committed and pushed. **Do NOT proceed until the user approves.**
- **Clean but ahead of remote**: Tell the user commits will be pushed. **Do NOT proceed until the user approves.**
4. **Stash uncommitted changes** (skip if clean):
```bash
git stash push --include-untracked -m "release-analysis: preserve working changes"
```
5. **Create review branch**:
```bash
ORIGINAL_BRANCH=$(git rev-parse --abbrev-ref HEAD)
BRANCH_NAME="feat/release-readiness-review"
git checkout -b $BRANCH_NAME 2>/dev/null || { BRANCH_NAME="feat/release-readiness-review-$(date +%Y%m%d-%H%M%S)"; git checkout -b $BRANCH_NAME; }
```
6. **Apply stash and commit** (skip if clean):
```bash
git stash apply
git add -A
git commit -m "chore: snapshot for release readiness review"
```
Check for sensitive files before staging — warn user if found.
7. **Push**:
```bash
git push -u origin HEAD
```
8. **Build the content**: Extract `owner/repo` and hostname from `git remote get-url origin | sed 's|://[^@]*@|://|'`. MANDATORY: Always use the sed command, we cannot expose PAT tokens in the context window!
9. Set `headBranch` to `$BRANCH_NAME`. Use `githubPrContent` (GitHub) or `gitlabMrContent` (GitLab) as an array.
10. **After analysis completes** — clean up:
```bash
git checkout $ORIGINAL_BRANCH
git push origin --delete $BRANCH_NAME 2>/dev/null || true
git branch -D $BRANCH_NAME 2>/dev/null || true
```
If stash was used: `git stash pop`.
**Important**: Do NOT create a PR/MR — only push the branch.
---
If `$ARGUMENTS` is empty and no git repo is detected, prompt the user for a PR/MR URL or repo name.
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---
description: Run automated UAT tests (UI or API) using a test profile on the AWS DevOps Agent
argument-hint: [test profile ID]
---
Read and follow the `running-release-tests` skill for full execution details.
## Step 0 — Choose your execution path (DO THIS FIRST)
Check your available tools. Do you have ALL of these tools?
- `aws_devops_agent__create_release_testing_job`
- `aws_devops_agent__get_task`
- `aws_devops_agent__list_journal_records`
- `aws_devops_agent__get_release_ui_testing_report`
- `aws_devops_agent__get_release_api_testing_report`
These tools are NOT deferred/lazy-loaded — if they do not appear in your tool list, they are unavailable. Do NOT search for them via ToolSearch.
- **YES (all present)** → Use the "Remote Server" path (steps 4-8 below)
- **NO** → Tell the user: "Remote server not configured." Then prompt the user with instructions from the `setup-devops-agent` skill if they intend to set up the connection. If not, mention that you are "proceeding with the AWS CLI fallback." Then use the Fallback (CLI) path below.
---
## Steps 1-3 — Common to both paths (see skill: "Gathering test parameters")
1. If `$ARGUMENTS` contains a test profile ID (e.g., `ki-12345`), use it directly.
2. If `$ARGUMENTS` is empty, ask the user which test profile to use.
3. Ask if the user has a specific test requirement or focus area.
## Steps 4-8 — Remote Server path (see skill: "Core workflow")
1. Call `aws_devops_agent__create_release_testing_job(test_profile_id="...", webhook_event_message="...")`.
2. Tell the user tests take 10+ minutes and you'll keep them posted.
3. Poll `aws_devops_agent__get_task(task_id=TASK_ID)` every 30s.
4. Stream progress via `aws_devops_agent__list_journal_records(execution_id=EXEC_ID, order="ASC")`.
5. On `COMPLETED`: call `aws_devops_agent__get_release_ui_testing_report(execution_id=EXEC_ID)` (UI) or `aws_devops_agent__get_release_api_testing_report(execution_id=EXEC_ID)` (API), and save to file.
## Steps 9-12 — Fallback (CLI) path
Use this path when the remote server tools are unavailable.
1. List agent spaces with `aws devops-agent list-agent-spaces --region us-east-1` and ask the user which one to use.
2. Start the job:
```
aws devops-agent create-backlog-task \
--agent-space-id SPACE_ID \
--task-type RELEASE_TESTING \
--title 'Release Testing' \
--priority MEDIUM \
--description '{"testProfileId": "<PROFILE_ID>", "webhookEventMessage": "<REQUIREMENT>"}' \
--region us-east-1
```
3. Poll for status every 30s:
```
aws devops-agent get-backlog-task \
--agent-space-id SPACE_ID \
--task-id TASK_ID \
--region us-east-1
```
4. On completion, retrieve the report. For UI testing, use --record-type qa_ui_testing_report, and for API testing, use --record-type qa_api_testing_report:
```
aws devops-agent list-journal-records \
--agent-space-id SPACE_ID \
--execution-id EXEC_ID \
--record-type qa_ui_testing_report \
--order ASC \
--region us-east-1
```
Save to file.
---
If `$ARGUMENTS` is empty and no test profile ID is provided, prompt the user.
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---
description: Set up the AWS DevOps Agent MCP connection
---
Invoke the `setup-devops-agent` skill to configure Bearer token or SigV4 credentials for the DevOps Agent.
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---
description: Set up the AWS Security Agent workspace
---
Invoke the `setup-security-agent` skill to configure the Security Agent workspace (agent space, IAM role, S3 bucket).
plugins/aws-agents-for-devsecops/commands/setup.md
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---
description: Set up both AWS DevOps Agent and AWS Security Agent connections
---
Invoke the `setup` skill to walk the user through configuring credentials for both agents.
plugins/aws-agents-for-devsecops/commands/spaces.md
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---
description: List configured AgentSpaces and summarize each one's accounts and capabilities
---
1. Call `aws_devops_agent__list_agent_spaces()` — get all spaces accessible with current auth.
- **Bearer token auth:** Returns only the single space the token is scoped to.
- **SigV4 auth:** Returns all spaces in the account.
2. For each space, call `aws_devops_agent__list_associations(agent_space_id="SPACE_ID")` to see attached AWS accounts.
3. For each space, probe its knowledge: `aws_devops_agent__chat(message="Summarize the AWS services and runbooks you have access to. One-paragraph answer.", agent_space_id="SPACE_ID")`.
4. Print a table: name, agentSpaceId, attached account IDs, one-line capability summary.
5. If more than one space exists and no routing guide in the workspace (e.g. `.claude/aws-agents-for-devsecops.md`, `AGENTS.md`, or per-project notes), offer to write one.
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# Multi-Space Walkthrough: Production Incident with Staging Comparison
This example shows how to use multiple AgentSpaces during a real incident — investigating production, comparing staging, and pulling runbooks from a knowledge space.
## Scenario
Your checkout-service is throwing 503 errors in production. You have three AgentSpaces:
- **prod** (as-prod-001) — production account
- **stage** (as-stage-002) — staging account
- **kb** (as-kb-003) — knowledge base with runbooks
## Steps
### Step 1 — Discover and pick the right spaces
```
aws devops-agent list-agent-spaces --region us-east-1
```
This returns all spaces. Pick the one matching the incident scope (production).
### Step 2 — Open the prod investigation in parallel with the staging check
Don't serialize — the investigation takes 5–8 minutes; the staging chat takes seconds. Fire both, then keep both progressing.
**Prod (deep investigation):**
```
aws devops-agent create-backlog-task --agent-space-id as-prod-001 --task-type INVESTIGATION --title 'ECS 503 errors on checkout-service (prod)' --priority HIGH --description '<local context>' --region us-east-1
```
Save `taskId`. Poll with `get-backlog-task` every 30-45s.
**Staging (fast chat):**
```
aws devops-agent create-chat --agent-space-id as-stage-002 --user-id USER_ID --user-type IAM --region us-east-1
→ executionId
aws devops-agent send-message --agent-space-id "as-stage-002" --execution-id exec_stage --user-id USER_ID --content 'Is the checkout-service healthy in staging? Any 503s or error spikes in the last hour?' --region us-east-1
```
### Step 3 — Pull runbooks from the knowledge space
While the investigation runs, check the knowledge base for existing runbooks:
```
aws devops-agent create-chat --agent-space-id as-kb-003 --user-id USER_ID --user-type IAM --region us-east-1
→ exec_kb
aws devops-agent send-message --agent-space-id "as-kb-003" --execution-id exec_kb --user-id USER_ID --content "What's our standard runbook for ECS 503 errors?" --region us-east-1
```
### Step 4 — Stream investigation progress
```
aws devops-agent get-backlog-task --agent-space-id as-prod-001 --task-id TASK_ID --region us-east-1
→ When status=IN_PROGRESS and executionId available:
aws devops-agent list-journal-records --agent-space-id as-prod-001 --execution-id EXEC_ID --region us-east-1
```
Update the user after each poll:
> 🔍 **2 min in:** Agent querying CloudWatch for error rate across AZs...
> 🎯 **5 min in:** Root cause — memory limit reduced from 512MB to 256MB in last deploy.
### Step 5 — Synthesize and present
Once the investigation completes:
```
aws devops-agent update-backlog-task --agent-space-id as-prod-001 --task-id TASK_ID --task-status PENDING_START --region us-east-1
```
Poll until `COMPLETED`, then retrieve the mitigation plan:
```
aws devops-agent list-executions --agent-space-id as-prod-001 --task-id TASK_ID --region us-east-1
aws devops-agent list-journal-records --agent-space-id as-prod-001 --execution-id EXEC_ID --record-type mitigation_summary_md --region us-east-1
```
Combine findings:
- **Prod investigation**: Root cause + mitigation plan
- **Staging comparison**: "Staging is healthy — confirms this is a prod-only deploy issue"
- **KB runbook**: Standard ECS 503 runbook for reference
Present a unified summary with the remediation plan. **Never auto-execute** — show the diff and let the user approve.
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---
name: analyzing-release-readiness
description: >-
Trigger a pre-merge release readiness review on a GitHub PR, GitLab MR, or local branch.
Use when the user wants to analyze code changes for risk, correctness, and potential
rollback issues before merging. Trigger words include release readiness, analyze PR,
analyze MR, review PR, risk analysis, pre-merge, safe to ship, ready to merge,
ready to commit, any risks, before merging, validate changes, release management.
---
# Release Readiness Review
> **AgentSpace routing (SigV4 only):** If `list_agent_spaces` is available in your tool list and the multi-space orchestration skill has NOT been invoked yet this session, invoke it first to determine which `agent_space_id` to use. Then pass `agent_space_id` on all tool calls below. For bearer token auth this is unnecessary — the token is already scoped to one space.
Run a release readiness review via the AWS DevOps Agent. Analyzes a code change for risk, correctness, and potential rollback issues. Returns a structured report with actionable findings.
**Rules:**
- If a **PR/MR URL** is provided: Extract ALL fields from the URL. Do NOT inspect the local workspace or git state.
- **NEVER use `gh` CLI, `glab` CLI, or any external tool to fetch PR/MR details.** All required fields (repository, prNumber/mergeRequestIid, hostname) MUST be parsed directly from the URL or user input. The DevOps Agent fetches the content itself — you only need to pass identifiers.
- **Only** use the local workspace flows when the user references a repository or package **without** a PR/MR link.
## Gathering execution parameters
Infer everything automatically from the user's request — do not ask for parameters that can be derived.
**Input source decision tree:**
```
Has the user provided a pull request/merge request link or ID?
├── Yes: github.com PR URL → use "GitHub PR" flow below
├── Yes: gitlab.com MR URL → use "GitLab MR" flow below
└── No link provided — repo name only → use "Local GitHub/GitLab repo" flow below
```
---
### GitHub PR (github.com URL or PR reference)
- Parse the input to extract fields — do NOT attempt a web fetch unless fields cannot be determined from the input.
- `repository` (required): `owner/repo` from the PR URL
- At least one of the following is required: `headSha` (commit SHA), `headBranch` (branch name), `prNumber` (PR number as a **string**, e.g. `"8"` not `8`)
- `hostname`: Extract from the URL (e.g., `github.com` or a self-hosted hostname)
- Pass these fields to `create_release_readiness_review` under `content.githubPrContent` as an **array of objects** (even for a single PR).
**Example:**
```json
{
"content": {
"githubPrContent": [
{
"repository": "owner/repo",
"prNumber": "8",
"hostname": "github.com"
}
]
}
}
```
> **Critical format rules**: `githubPrContent` MUST be an array (not a single object). `prNumber` MUST be a string (not an integer).
---
### GitLab MR (gitlab.com URL)
- Parse the input to extract fields — do NOT attempt a web fetch unless fields cannot be determined from the input.
- `repository` (required): `owner/repo` from the MR URL
- At least one of the following is required: `headSha` (commit SHA), `headBranch` (branch name), `mergeRequestIid` (MR number as a **string**, e.g. `"1"` not `1`)
- `hostname`: Extract from the URL (e.g., `gitlab.com` or a self-hosted hostname)
- Pass these fields to `create_release_readiness_review` under `content.gitlabMrContent` as an **array of objects** (even for a single MR).
**Example:**
```json
{
"content": {
"gitlabMrContent": [
{
"repository": "namespace/repo",
"mergeRequestIid": "1",
"hostname": "gitlab.com"
}
]
}
}
```
> **Critical format rules**: `gitlabMrContent` MUST be an array (not a single object). `mergeRequestIid` MUST be a string (not an integer). Violating either causes immediate task failure with no journal records.
---
### Local GitHub/GitLab repo (no PR/MR URL provided — local workspace ONLY)
**MANDATORY**: When the user references a repository or branch without a PR/MR link, you MUST execute every step below in order. Do NOT shortcut by grabbing the remote URL and SHA directly — the review agent needs a pushed branch to read from. Skipping the push step will cause the analysis to fail or produce incomplete results.
1. **Navigate to the repository directory**: `cd` to the repo root (e.g., the clone directory). Ask the user if needed.
2. **Determine the base branch**: Use `main` unless the user specifies a different branch. Verify the remote tracking branch exists:
```bash
BASE_BRANCH="main"
if ! git show-ref --verify --quiet refs/remotes/origin/$BASE_BRANCH; then
git fetch origin $BASE_BRANCH
fi
```
If the fetch fails (e.g., "couldn't find remote ref"), ask the user to specify the base branch and stop.
3. **Check for local changes**: Run `git status --short` and `git rev-list --count origin/$BASE_BRANCH..HEAD` to determine the state and communicate accordingly:
- **Clean AND not ahead**: Inform the user there's nothing new to analyze and stop.
- **Has uncommitted changes (with or without unpushed commits)**:
- If there are one or more unpushed commits (rev-list count >= 1), tell the user:
> "You have uncommitted changes and N unpushed commits. I'll commit your uncommitted changes on top, then push all N+1 commits to a new branch for analysis. All changes will appear as a single diff against the base branch. Shall I proceed?"
- If there are no other unpushed commits (rev-list count = 0), tell the user:
> "I'll commit your uncommitted changes and push them to a new branch for release readiness review. Shall I proceed?"
- **Do NOT proceed until the user approves.** If they decline, stop.
- **Clean but ahead of remote (rev-list count > 0, no uncommitted changes)**:
- If ahead by more than 1 commit, tell the user:
> "You have N unpushed commits. I'll push all of them to a new branch for analysis. All changes will appear as a single diff against the base branch. Shall I proceed?"
- If ahead by exactly 1 commit, tell the user:
> "I'll push your latest commit to a new branch for release readiness review. Shall I proceed?"
- **Do NOT proceed until the user approves.** If they decline, stop.
4. **Stash uncommitted changes** (skip this step if working directory is clean):
```bash
git stash push --include-untracked -m "release-analysis: preserve working changes"
```
5. **Create review branch** (do this BEFORE committing so the snapshot commit only lives on the disposable branch):
```bash
ORIGINAL_BRANCH=$(git rev-parse --abbrev-ref HEAD)
BRANCH_NAME="feat/release-readiness-review"
git checkout -b $BRANCH_NAME 2>/dev/null || { BRANCH_NAME="feat/release-readiness-review-$(date +%Y%m%d-%H%M%S)"; git checkout -b $BRANCH_NAME; }
```
6. **Apply stashed changes and commit on the review branch** (skip this step if working directory was clean — go straight to step 7):
```bash
git stash apply
```
Before staging, check for sensitive files:
```bash
git status --short | grep -iE '\.(env|pem|key|p12|pfx|credentials|secret)'
```
If sensitive files are detected, warn the user and ask for confirmation before proceeding. If the user declines, abort:
```bash
git checkout $ORIGINAL_BRANCH && git branch -D $BRANCH_NAME && git stash drop
```
Once confirmed (or no sensitive files found):
```bash
git add -A
git commit -m "chore: snapshot for release readiness review"
```
7. **Push all unpushed commits** (requires prior user approval):
If the user already approved the push in step 3, proceed directly. Otherwise (e.g., the flow reached here without an explicit approval prompt), confirm before pushing:
> "I'm about to push branch `$BRANCH_NAME` to `origin`. This is a prerequisite step, can I proceed?"
**Do NOT push until the user approves.** If they decline, abort and skip to step 11.
Once approved (or if already approved in step 3):
```bash
git push -u origin HEAD
```
8. **Determine the repository identifier and hostname**: Run `git remote get-url origin | sed 's|://[^@]*@|://|'` to extract the `owner/repo` and hostname.
- GitHub URLs (github.com or self-hosted) → use `githubPrContent`, hostname from URL
- GitLab URLs (gitlab.com or self-hosted) → use `gitlabMrContent`, hostname from URL
9. **Build the content**: Set `headBranch` to `$BRANCH_NAME`, `repository` to the extracted `owner/repo`, and `hostname` to the value from step 8. Wrap the object in an array:
- GitHub: `{"githubPrContent": [{"repository": "owner/repo", "headBranch": "feat/release-readiness-review", "hostname": "github.com"}]}`
- GitLab: `{"gitlabMrContent": [{"repository": "namespace/repo", "headBranch": "feat/release-readiness-review", "hostname": "gitlab.com"}]}`
10. **Inform the user**: Tell them which branch was created and pushed, then proceed with the core workflow below.
11. **After analysis completes**: Clean up and restore working state:
```bash
git checkout $ORIGINAL_BRANCH
git push origin --delete $BRANCH_NAME 2>/dev/null || true
git branch -D $BRANCH_NAME 2>/dev/null || true
```
If step 4 was executed (uncommitted changes were stashed), also run:
```bash
git stash pop
```
**Important**: Do NOT create a PR/MR — only push the branch. The release readiness review agent will read the branch directly.
## Core workflow
> **STRICT SEQUENCING**: Steps below are numbered. You MUST complete each step before moving to the next. In particular, step 1 (automated testing prompt) MUST NOT happen until the entire "Gathering execution parameters" flow above is fully complete — all git operations done, branch pushed (if local flow), content object built, and user informed of the branch. Only THEN proceed to step 1.
### 1. Determine `skip_automated_testing` (ask ONLY after content is ready)
The `skip_automated_testing` parameter controls whether the agent runs automated testing (automated verification tests) or only static analysis.
| Value | Behavior |
|-------|----------|
| `true` | Skip automated testing, run static analysis only (fast — code review, risk assessment, dependency checks) |
| `false` | Full analysis including automated testing (longer — spins up a testing environment, builds code, runs automated verification tests) |
Present the choice and wait for a response:
> "Would you like a quick static analysis (code review, risk assessment, dependency checks), or a full analysis that also includes automated testing? Automated testing spins up a testing environment, builds your code, and runs automated verification tests — it's more thorough but takes longer."
**Do NOT proceed until the user answers.**
- If the user says "yes" / "include testing" / "full analysis" / "run tests" → use `skip_automated_testing=false`
- If the user says "no" / "static only" / "skip testing" / "quick" / declines → use `skip_automated_testing=true`
- If the response is ambiguous (e.g., "go ahead", "sure", "proceed") → ask the user to clarify which option they prefer.
### 2. Check tool availability
Verify that the following tools are available: `aws_devops_agent__create_release_readiness_review`, `aws_devops_agent__get_task`, `aws_devops_agent__list_journal_records`, `aws_devops_agent__get_release_readiness_report`. These tools are NOT deferred/lazy-loaded — if they do not appear in your tool list, they are unavailable. Do NOT search for them via ToolSearch. If any are missing, skip the remaining steps in this section and use the "Fallback (aws-mcp)" path below instead. Tell the user: "Remote server unavailable — using direct aws-mcp server fallback."
### 3. Start the Job
```
aws_devops_agent__create_release_readiness_review(
content={...},
skip_automated_testing=true/false
)
→ {"taskId": "...", "executionId": "...", "status": "started"}
```
Record the **taskId** and **executionId** from the response.
### 4. Poll for Status
Call `aws_devops_agent__get_task(task_id=TASK_ID)` every **30 seconds** until the status transitions to `IN_PROGRESS` or a terminal state (`COMPLETED`, `FAILED`, `CANCELED`, `TIMED_OUT`).
### 5. Monitor Until Completion
Once `IN_PROGRESS`, poll for progress in a loop:
1. Call `aws_devops_agent__list_journal_records(execution_id=EXEC_ID, order="ASC")` to fetch new findings.
2. Present each record to the user with a friendly progress update.
3. Use `next_token` from the response to fetch only new records on subsequent polls.
4. **Wait 15 seconds** between each poll iteration.
5. Check `aws_devops_agent__get_task(task_id=TASK_ID)` periodically — stop when terminal status.
### 6. Present Results
Once the job reaches a terminal status:
- If `COMPLETED`:
1. Call `aws_devops_agent__get_release_readiness_report(execution_id=EXEC_ID)` to retrieve the full report.
2. Write the report contents to a markdown file:
```
release-readiness-review-<YYYY-MM-DD-HHmmss>.md
```
3. Inform the user that the report was saved, including the file path.
4. **Auto-fix flow (MANDATORY)**: After saving the report, you MUST attempt to generate and present fixes for all actionable risks — this is the primary value of the review workflow, not an optional step.
- First, locate the analyzed repository in the current workspace:
1. Run `ls` to list available directories in the workspace.
2. Match by repo name (the last segment of `owner/repo` or `namespace/repo`). For example, `testgroupadthiru/repo1updated` → look for a directory named `repo1updated`.
3. If a single match is found, confirm with the user: "I found `<match>` — is this the correct local copy of `<namespace/repo>`?"
4. If multiple matches are found, ask the user which one is correct.
5. If no obvious match exists, ask the user: "I couldn't find a local directory matching `<repo-name>`. Is it available locally under a different name, or should I just show the suggested fixes?"
- If **found locally**:
- **Verify branch**: Run `git -C <repo-directory> branch --show-current` to confirm you're on the expected branch. If not on the expected branch, check out the correct one before proceeding.
- Scan the relevant code, interpret the risks/issues from the report. Then tell the user:
> "The report identified N actionable issues. I can generate the fixes in your local repository, and push them to a new branch `feat/release-readiness-fix`. Shall I proceed?"
- **Do NOT proceed until the user approves.** If they decline, stop.
- Once approved, generate the fixes. Then:
```bash
cd <repo-directory>
git checkout -b feat/release-readiness-fix 2>/dev/null || { git checkout -b "feat/release-readiness-fix-$(date +%Y%m%d-%H%M%S)"; }
# Apply the fixes
git add -A
git commit -m "fix: Address issues identified by release readiness review"
```
- **Before pushing, verify branch again**: Run `git branch --show-current` and confirm it shows `feat/release-readiness-fix*`. Do NOT push if you're on any other branch.
```bash
git push -u origin HEAD
```
Inform the user: which issues were fixed, what branch was created, and that the fix has been pushed.
- If **NOT found locally**: Present the suggested fixes from the report as concrete, ready-to-apply code patches. Use the `suggestedFix` field from each risk. Format them as code blocks the user can copy-paste directly. Walk through each actionable risk: explain the issue, show the exact fix, and state which file/line it targets.
- If the report finds **no risks or issues**: Inform the user the analysis completed with no actionable findings.
- If `FAILED` or `TIMED_OUT`: Present the error information and suggest next steps.
- If `CANCELED`: Inform the user the job was canceled and no report is available.
## Cancelling a job
```
aws_devops_agent__cancel_release_readiness_review(task_id=TASK_ID)
```
## Error handling
1. If `FAILED` or `TIMED_OUT` — stop and present the error. If the job failed quickly (within the first poll or two), call `aws_devops_agent__list_associations()` to check whether the target repository's hosting service (GitHub/GitLab hostname) is associated with the agent space.
2. If job does not reach `IN_PROGRESS` within 5 minutes — cancel with `cancel_release_readiness_review`.
3. If throttled (`429` or `ThrottlingException`) — wait 30 seconds, retry up to 3 times.
4. If the error does not match any known pattern above, present the raw error output to the user.
## Fallback (aws-mcp)
If the `aws-devops-agent` remote server is unavailable, use the AWS CLI directly:
Tell the user: "Remote server unavailable — using the aws-mcp server fallback."
### 1. Select Agent Space
List available agent spaces:
```
aws devops-agent list-agent-spaces --region us-east-1
```
Present the list to the user and ask which agent space they'd like to use. **Do NOT proceed until the user has selected one.** Use the selected `agentSpaceId` as `SPACE_ID` in all subsequent calls.
### 2. Start the Job
```
aws devops-agent create-backlog-task \
--agent-space-id SPACE_ID \
--task-type RELEASE_READINESS_REVIEW \
--title 'Release Readiness Review' \
--priority MEDIUM \
--description '{\"agentInput\": {\"content\": <CONTENT_JSON>, \"metadata\": {\"skipAutomatedTesting\": true}}}' \
--region us-east-1
```
> **CRITICAL:** The `content` value must be a single object — NOT wrapped in a list. Correct: `"content": {"githubPrContent": [...]}`. Incorrect: `"content": [{"githubPrContent": [...]}]`. Wrapping in a list causes a Pydantic validation failure on the backend. The values in the content should all be of string format e.g. the PR number should be a string.
Default is `"skipAutomatedTesting": true` (static only). Set to `false` only if user explicitly opted into automated testing.
### 3. Poll for Status
```
aws devops-agent get-backlog-task \
--agent-space-id SPACE_ID \
--task-id TASK_ID \
--region us-east-1
```
Poll every **30 seconds** until the status transitions to `IN_PROGRESS` or a terminal state (`COMPLETED`, `FAILED`, `CANCELED`, `TIMED_OUT`).
### 4. Monitor Until Completion
Once `IN_PROGRESS`, poll for progress in a loop:
```
aws devops-agent list-journal-records \
--agent-space-id SPACE_ID \
--execution-id EXEC_ID \
--order ASC \
--region us-east-1
```
1. Present each record to the user with a friendly progress update.
2. Use `next_token` from the response to fetch only new records on subsequent polls.
3. **Wait 15 seconds** between each poll iteration.
4. Check `get-backlog-task` periodically — stop when terminal status.
### 5. Present Results
Once the job reaches a terminal status:
- If `COMPLETED`:
1. Retrieve the report:
```
aws devops-agent list-journal-records \
--agent-space-id SPACE_ID \
--execution-id EXEC_ID \
--record-type release_analysis_report \
--order ASC \
--region us-east-1
```
2. Write the report contents to a markdown file:
```
release-readiness-review-<YYYY-MM-DD-HHmmss>.md
```
3. Inform the user that the report was saved, including the file path.
4. **Auto-fix flow (MANDATORY)**: After saving the report, you MUST attempt to generate and present fixes for all actionable risks — this is the primary value of the review workflow, not an optional step. Follow the same auto-fix flow described in the Core workflow section above (locate repo, verify branch, generate fixes, push to `feat/release-readiness-fix`).
- If `FAILED` or `TIMED_OUT`: Present the error information and suggest next steps.
- If `CANCELED`: Inform the user the job was canceled and no report is available.
#### Cancelling (fallback)
```
aws devops-agent update-backlog-task \
--agent-space-id SPACE_ID \
--task-id TASK_ID \
--task-status CANCELED \
--region us-east-1
```
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---
name: chatting-with-aws-devops-agent
description: >-
Have a fast, conversational analysis with the AWS DevOps Agent. Use for cost
optimization, architecture review, topology mapping, knowledge / runbook
discovery, security audits, dependency questions, and quick diagnostics —
anything that needs a 5-30 second answer rather than a 5-8 minute deep
investigation. Trigger words include cost, optimize, review, architecture,
topology, what runbooks, show me, compare, audit, what if.
---
# Chat with the AWS DevOps Agent
> **AgentSpace routing (SigV4 only):** If `list_agent_spaces` is available in your tool list and the multi-space orchestration skill has NOT been invoked yet this session, invoke it first to determine which `agent_space_id` to use. Then pass `agent_space_id` on all tool calls below. For bearer token auth this is unnecessary — the token is already scoped to one space.
Chat is the **default**. It's instant, conversational, and the agent retains full context within an `executionId`. Only escalate to `investigating-incidents-with-aws-devops-agent` when the user describes an incident or the agent itself suggests deeper analysis is warranted.
## How to send messages
**Primary — use the `chat` tool:**
```
aws_devops_agent__chat(message="What's causing the 503 errors on checkout-service?")
→ {"executionId": "uuid", "answer": "Based on my analysis..."}
```
One call, full answer. No session setup needed — the tool handles CreateChat + SendMessage + response parsing internally.
**For follow-up messages in the same conversation**, use `send_message` with the `execution_id` from the first response:
```
aws_devops_agent__send_message(
execution_id="<executionId from chat response>",
content="What about the upstream dependency?"
)
→ "The upstream service shows..."
```
The agent retains full context within an `executionId`. Reuse it for follow-ups — don't call `chat` again for the same conversation.
**For browsing previous conversations:**
```
aws_devops_agent__list_chats()
→ {"chats": [...]}
```
## Injecting local context
Pack local workspace knowledge into the `message` parameter. This is the killer feature — the DevOps Agent knows your AWS cloud; you know the user's local workspace.
```
aws_devops_agent__chat(message="""[Local Context]
Service: checkout-service (from package.json)
Last deploy: commit abc1234 — 2h ago
CDK Stack: lib/checkout-stack.ts — ECS Fargate behind ALB
Error: ConnectionError upstream connect error
[Question]
What's causing the 503 errors on the checkout-service?""")
```
Tailor by intent:
- **Cost questions** — include IaC files (CDK / CFN / Terraform), instance types, scaling policies
- **Architecture review** — IaC files + dependency manifest + public API surface
- **Topology mapping** — service name + key resources (cluster, ALB, RDS instance)
- **Knowledge / runbook discovery** — no local context needed, just ask
- **Quick diagnostics** — alarm/metric/error + `git log --oneline -10`
## Phrasing matters
The DevOps Agent's intent detection is keyword-based:
| Phrasing | Response time |
|----------|---------------|
| "Analyze...", "Review...", "Compare...", "What if...", "Show topology..." | 5–30s (chat) |
| "List...", "Show me...", "What is..." | instant (discovery) |
| "Investigate...", "Root cause of...", "What's wrong with..." | 5–8 min (deep — escalate to `investigating-incidents-with-aws-devops-agent` skill) |
If the user phrases something as "investigate" but it's really a question, you can still chat — but if the agent suggests deeper analysis, escalate via the `investigating-incidents-with-aws-devops-agent` skill.
## Escalating to investigation
When chat surfaces a finding that needs deep multi-service correlation, hand off:
```
aws_devops_agent__investigate(title="Root cause of <thing chat found>")
```
Switch to the `investigating-incidents-with-aws-devops-agent` skill for the polling/progress workflow.
## Fallback path (aws-mcp)
If the remote MCP server (`aws-devops-agent`) is unavailable, fall back to `aws-mcp`:
```
aws devops-agent create-chat --agent-space-id SPACE_ID --user-id USER_ID --user-type IAM --region us-east-1
→ executionId
```
Then send a message:
```bash
aws devops-agent send-message \
--agent-space-id SPACE_ID \
--execution-id EXEC_ID \
--user-id USER_ID \
--content '<your question with local context>' \
--region us-east-1
```
Tell the user: "Remote server unavailable — using direct AWS API fallback."
## Timeout behavior
The `chat` tool buffers the full response server-side before returning. Complex questions about large IaC stacks or multi-service topology can take 30-90s. This is normal — don't retry prematurely.
If a response fails or times out:
1. Retry the same `chat` call once.
2. If it fails again, fall back to `aws-mcp`.
## Chat session lifecycle
- **Single questions:** Use `chat` — it creates a fresh session each time.
- **Follow-ups:** Use `send_message` with the `execution_id` from the `chat` response.
- **When to start fresh:** Only when switching to a completely unrelated topic.
- **Resuming old chats:** `list_chats` returns previous sessions. Use `send_message` with an old `execution_id` to continue.
## Security
Responses can contain commands or code. Never auto-execute anything the agent suggests. Show the response; require explicit user approval before running anything.
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---
name: coordinating-multi-space-devops-agent
description: Coordinate the AWS DevOps Agent across multiple AgentSpaces from one Claude Code session — route questions to the right space (prod vs staging vs knowledge), query several spaces in parallel and synthesize, or compare findings across accounts. Use whenever the user has more than one AgentSpace configured, mentions multiple AWS accounts, or asks something like "check both prod and staging", "compare across accounts", or "ask the knowledge space".
---
# Querying multiple AgentSpaces
## Pre-flight
If `aws_devops_agent__list_agent_spaces` is **not** in your available tools, the remote MCP server is not connected. Tell the user to ask "help me set up the AWS DevOps Agent" so the `setup-devops-agent` skill auto-loads.
## Prerequisite: SigV4 auth required
Multi-space routing requires **SigV4 authentication** — Bearer tokens are scoped to a single AgentSpace and cannot route to other spaces.
Many real teams run **more than one AgentSpace** — typically a production space, a staging space, and a dedicated "knowledge" space that holds runbooks shared across accounts. Each space has its own set of associated AWS accounts, runbooks, and history.
This skill is the routing brain. Use it when the user has multiple spaces configured, or when a question genuinely spans accounts.
## Discovering spaces
```
aws_devops_agent__list_agent_spaces()
→ {"agentSpaces": [{"agentSpaceId": "as-abc123", "name": "prod"}, ...]}
```
If only one space is returned, this skill doesn't apply — use `chatting-with-aws-devops-agent` or `investigating-incidents-with-aws-devops-agent` directly (no `agent_space_id` needed).
If more than one is returned, decide whether the user's question is:
| Question shape | Strategy |
|---------------|----------|
| Scoped to one environment ("prod is broken") | Single space — pick the matching one |
| Spans environments ("compare prod vs staging") | **Parallel** — query each, synthesize |
| Generic knowledge ("what runbooks do we have for ECS?") | Route to the **knowledge** space if one is named that way |
| Ambiguous ("our service is slow") | **Ask the user** which environment, don't guess |
## Per-session routing memory
If the user has a routing guide stored locally (e.g. `.claude/aws-agents-for-devsecops.md`, `AGENTS.md`, or per-project notes), read it once at the start of the session and use it as the routing table for the rest of the conversation. Format expected:
```markdown
| Space | AWS Profile | Agent Space ID | Purpose |
|-------|-------------|----------------|---------|
| prod | acme-prod | as-abc123 | Production incidents, customer-facing services |
| stage | acme-stage | as-def456 | Pre-prod validation, integration testing |
| kb | acme-shared | as-ghi789 | Shared runbooks, cross-account knowledge |
```
If no guide exists, run discovery:
1. `aws_devops_agent__list_agent_spaces()` → get all spaces.
2. For each space: `aws_devops_agent__chat(message="Summarize the AWS accounts, services, and runbooks you have access to.", agent_space_id="<SPACE_ID>")` → get a one-paragraph summary.
3. Offer to write the routing guide to the project (e.g. `.claude/aws-agents-for-devsecops.md`, `AGENTS.md`, or per-project notes) so future sessions skip discovery.
## Pattern A — Parallel queries, one synthesized answer
Use when the user wants a comparison: "compare prod and staging error rates", "is this issue happening in both accounts?", "audit costs across all our environments".
```
# 1. Query each space in parallel with environment-specific context
aws_devops_agent__chat(message="<question> | env=prod | <prod IaC context>", agent_space_id="PROD_ID")
→ {"executionId": "...", "answer": "..."}
aws_devops_agent__chat(message="<question> | env=stage | <stage IaC context>", agent_space_id="STAGE_ID")
→ {"executionId": "...", "answer": "..."}
# 2. Synthesize locally — present a side-by-side summary, not two separate dumps
```
**Don't just paste both responses.** Read both, identify what's the same vs. different, and tell the user the *delta* — that's the value.
## Pattern B — Knowledge lookup, then per-space action
Use when one space holds runbooks/knowledge that informs work in another space.
```
# 1. Ask the knowledge space first
aws_devops_agent__chat(
message="What's our standard runbook for ECS 503 errors?",
agent_space_id="KB_ID"
)
→ {"answer": "<runbook text>"}
# 2. Apply that runbook in the target environment
aws_devops_agent__investigate(
title="ECS 503 errors on checkout-service. [Runbook from knowledge space] <runbook text> [Local context] ...",
agent_space_id="PROD_ID",
priority="HIGH"
)
```
The DevOps Agent doesn't share state between spaces — you bridge it by quoting the knowledge space's response into the investigation's `title`.
## Pattern C — Targeted single-space query
Use when the user explicitly names a space or environment.
```
# Pick the matching agentSpaceId from your routing memory, pass it on the call
aws_devops_agent__chat(message="<question>", agent_space_id="<matched_space_id>")
```
If the routing is ambiguous and the user doesn't say, **ask once** — better than firing into the wrong account.
## Pattern D — Investigations don't share state
Investigations are per-space. If an issue spans accounts, you may need *two* investigations:
```
aws_devops_agent__investigate(title="Latency spike — prod side", agent_space_id="PROD_ID", priority="HIGH")
aws_devops_agent__investigate(title="Latency spike — stage side", agent_space_id="STAGE_ID", priority="HIGH")
```
Track both `taskId`s. Poll both. Surface findings together.
This is rare — usually one space owns the problem. Don't fan out by default.
## What NOT to do
- **Don't blast every space with every question.** It's slow, expensive, and the user has to read 3× as much output.
- **Don't fan out without verifying scope.** If a space's `description` or recorded coverage doesn't mention the relevant service, skip it — sending a question into a scope-mismatched space typically hangs rather than returning "I don't know."
- **Don't fire investigations in parallel by default.** They take 5–8 minutes each. Pick the one space that owns the incident.
- **Don't silently switch spaces mid-conversation.** If a follow-up needs a different space, tell the user: "Switching to the knowledge space to look up the runbook."
## Timeout guidance
The `chat` tool buffers the full response server-side before returning. Complex cross-account queries can take 30-90s per space. If a space doesn't respond within 90s, it's likely a scope mismatch — surface a message like "Space X did not respond within 90s — skipping (likely scope mismatch)" and move on rather than hanging.
## See also
- `examples/multi-space-walkthrough.md` for a fully worked scenario (prod incident with staging comparison and knowledge-space runbook lookup).
- The `setup-devops-agent` skill for first-time configuration of multiple AgentSpaces, AWS profiles, and shell wrappers.
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---
name: diff-scanning-with-aws-security-agent
description: Run a fast AWS Security Agent diff scan on only the changed code since a git ref. Use when the user asks to scan changes, run a diff scan, check what changed for security issues, scan before committing, scan before PR, or any pre-commit/pre-push security check.
---
# AWS Security Agent — Diff Scan
Scan only the code that changed since a git ref. Faster than a full scan — focuses findings on the diff. No prior full scan needed.
## Local state
Read `.security-agent/config.json` for `agent_space_id` and `region`. If missing, run the `setup-security-agent` workflow inline first.
Track scans in `.security-agent/scans.json`.
### Resolving the values you need
| Placeholder | How to resolve |
|-------------|----------------|
| `<id>` (agent space) | `config.agent_space_id` |
| `<region>` | `config.region` (default `us-east-1`) |
| `<account>` | `aws sts get-caller-identity --query Account --output text` |
| `<role-arn>` | `arn:aws:iam::<account>:role/SecurityAgentScanRole` |
| `<bucket>` | `security-agent-scans-<account>-<region>` |
| `<WORKSPACE_ID>` | `printf '%s' "$(pwd)" \| md5sum \| cut -c1-12` |
---
## Workflow
1. **Pre-scan checks.** Same as full scan — read config, verify agent space, resolve values, generate workspace ID.
2. **Ask what to scan against:**
- Uncommitted changes → `BASE_REF=HEAD` (default)
- Branch vs main → `BASE_REF=main`
- Custom ref → user provides
3. **Generate diff (fail fast if empty):**
```bash
cd <absolute-workspace-path>
if [ "$BASE_REF" = "HEAD" ]; then
git diff HEAD > /tmp/diff.patch
else
git diff "$BASE_REF..HEAD" > /tmp/diff.patch
fi
[ -s /tmp/diff.patch ] || { echo "No changes vs $BASE_REF"; exit 1; }
```
4. **Zip the workspace** (same exclusions as full scan, 2 GB limit):
```bash
cd <absolute-workspace-path>
zip -r /tmp/source.zip . \
-x ".git/*" -x ".security-agent/*" -x "node_modules/*" \
-x "__pycache__/*" -x ".venv/*" -x "venv/*" \
-x "dist/*" -x "build/*" -x "target/*" \
-x ".mypy_cache/*" -x ".pytest_cache/*" -x ".tox/*" \
-x ".next/*" -x "cdk.out/*" -x ".DS_Store" -x "*.pyc"
```
5. **Upload both source zip and diff patch:**
```bash
SCAN_ID="diff-$(date +%s)-$(openssl rand -hex 3)"
aws s3 cp /tmp/source.zip s3://<bucket>/security-scans/source/<WORKSPACE_ID>/source.zip
aws s3 cp /tmp/diff.patch s3://<bucket>/security-scans/diffs/${SCAN_ID}/diff.patch
```
6. **Get or create per-workspace CodeReview** (same logic as full scan — lookup `config.json → code_reviews[<abs_path>]`, create if absent):
```bash
aws securityagent create-code-review --agent-space-id <id> --title <title> \
--service-role <role-arn> \
--assets sourceCode=[{s3Location=s3://<bucket>/security-scans/source/<WORKSPACE_ID>/source.zip}]
```
7. **Start the diff job:**
```bash
aws securityagent start-code-review-job --agent-space-id <id> --code-review-id <cr-id> \
--diff-source s3Uri=s3://<bucket>/security-scans/diffs/${SCAN_ID}/diff.patch
```
If `ResourceNotFoundException`: recreate CodeReview and retry.
8. Capture `codeReviewJobId`. Persist to `scans.json` with `scan_type: "DIFF"` and `base_ref`.
9. Tell user: "Diff scan started. Takes a few minutes. I'll check every 2 minutes — say 'stop polling' to opt out."
10. **Poll** every 2 minutes:
```bash
aws securityagent batch-get-code-review-jobs --agent-space-id <id> --code-review-job-ids <job_id>
```
Only respond when status changes. On COMPLETED → fetch findings.
11. **Findings:** same presentation as full scan — grouped by severity, report written to `.security-agent/findings-{scan_id}.md`.
---
## Rules
- Diff scans are standalone — no prior full scan needed
- Poll every 2 minutes, not faster
- Default to `BASE_REF=HEAD` if user doesn't specify
- Title: `diff-<git-branch>-<timestamp>` (no spaces)
- If diff is empty, tell user and stop — don't start a scan
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# Investigation reference
## Journal record types
| Type | Emoji | Meaning |
|------|-------|---------|
| `PLANNING` | 📋 | Agent is planning its approach |
| `SEARCHING` | 🔍 | Agent is querying CloudWatch, X-Ray, logs, IAM, etc. |
| `ANALYSIS` | 🔬 | Agent is analyzing collected data |
| `FINDING` | 🎯 | Key discovery — surface this prominently |
| `ACTION` | 🔧 | Agent is performing a read-only action |
| `SUMMARY` | 📊 | Investigation summary with root cause |
| `SUGGESTION` | 💡 | Recommended fix |
## Polling cadence
| Status | Action |
|--------|--------|
| `CREATED` | Poll every 30s. Wait up to 60s — if still CREATED, keep waiting. |
| `IN_PROGRESS` | Poll every 30–45s. Fetch journal records with pagination. |
| `COMPLETED` | Stop polling. Fetch full journal `--order DESC --max-items 10`. If the user approves, trigger mitigation (2-5 min) via `update-backlog-task --task-status PENDING_START`. |
| `FAILED` | Stop polling. Fetch journal — partial findings often exist. |
Never poll faster than 30s — you'll hit throttling.
## Pagination
`aws devops-agent list-journal-records` returns `nextToken` when there are more records. Save it and pass `--next-token TOKEN` on the next poll so you only fetch *new* records each cycle. Re-fetching the full journal on every poll is wasteful and slow.
## Error recovery
| Error | Cause | Action |
|-------|-------|--------|
| `ResourceNotFoundException` | Wrong agent_space_id | `aws devops-agent list-agent-spaces --region us-east-1` to verify |
| `ThrottlingException` | Polling too fast | Back off — 60s, then 90s, then 120s |
| `ValidationException` | Missing required field on `create-backlog-task` | `--title`, `--task-type`, and `--priority` are required |
| `AccessDeniedException` | Missing IAM permissions | User needs `AIDevOpsAgentFullAccess` |
| `ExpiredTokenException` | AWS credentials expired | `aws sso login` or refresh access keys |
## Priority guide
| Priority | Use for |
|----------|---------|
| `CRITICAL` | Active sev1, customer-facing outage |
| `HIGH` | Active production incident, error rate elevated |
| `MEDIUM` | Recurring issue, performance degradation |
| `LOW` | Postmortem, follow-up mitigation generation |
| `MINIMAL` | Exploratory analysis, no time pressure |
## Common patterns
### Parallel triage + investigation
When the user reports an incident, fire **both** in sequence so they get instant guidance while the deep investigation runs:
```
# Instant triage (2-10s)
aws devops-agent create-chat --agent-space-id SPACE_ID --user-id USER_ID --user-type IAM --region us-east-1 → executionId
aws devops-agent send-message --agent-space-id SPACE_ID --execution-id EXEC_ID --user-id USER_ID --content '<incident> + <local context>' --region us-east-1
# Deep investigation (5-8 min)
aws devops-agent create-backlog-task --agent-space-id SPACE_ID --task-type INVESTIGATION --title '<incident>' --priority HIGH --description '<local context>' --region us-east-1 → taskId
aws devops-agent get-backlog-task ... → poll for executionId
aws devops-agent list-journal-records ... → stream findings
```
Show the chat response immediately. Update the user with investigation progress as journal records come in.
### Trigger mitigation on a completed investigation
If a previous investigation completed without recommendations, trigger mitigation (2-5 min):
```
aws devops-agent update-backlog-task \
--agent-space-id SPACE_ID \
--task-id TASK_ID \
--task-status PENDING_START \
--region us-east-1
```
Poll `get-backlog-task` until `COMPLETED`, then retrieve the mitigation plan:
```
aws devops-agent list-executions \
--agent-space-id SPACE_ID \
--task-id TASK_ID \
--region us-east-1
```
Find the newest execution_id, then:
```
aws devops-agent list-journal-records \
--agent-space-id SPACE_ID \
--execution-id EXEC_ID \
--record-type mitigation_summary_md \
--region us-east-1
```
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---
name: investigating-incidents-with-aws-devops-agent
description: Run a deep root-cause investigation on the AWS DevOps Agent. Use when the user describes an incident, alarm, outage, or unexplained behavior — keywords like "5xx", "503", "OOM", "latency spike", "deployment failure", "rollback", "sev1", "investigate", "root cause", "debug", "alarm fired", "service down". Polls and streams progress, then surfaces recommendations.
---
# Investigate an AWS incident
> **AgentSpace routing (SigV4 only):** If `list_agent_spaces` is available in your tool list and the multi-space orchestration skill has NOT been invoked yet this session, invoke it first to determine which `agent_space_id` to use. Then pass `agent_space_id` on all tool calls below. For bearer token auth this is unnecessary — the token is already scoped to one space.
Use this when the user is reporting or describing an operational problem that needs deep async analysis (5–8 minutes of agent work). For fast questions about cost, architecture, or topology, use the `chatting-with-aws-devops-agent` skill instead.
## Pre-flight
Before starting an investigation, gather **local context** and pack it into the `title` parameter. This is the killer feature — the DevOps Agent knows your AWS cloud; you know the user's local workspace.
Always collect:
- Service identity from `package.json` / `pom.xml` / `Cargo.toml` / `requirements.txt` / `Makefile`
- `git log --oneline -10` (recent commits — agent correlates deploys to incidents)
- `git diff --stat` (uncommitted work that might be relevant)
When investigating errors, also include:
- The full stack trace or relevant log excerpt
- Any IaC files relevant to the failing resource (CDK / CloudFormation / Terraform / ECS task def)
## Start the investigation
```
aws_devops_agent__investigate(
title="ECS 503 errors on checkout-service since commit abc1234 deployed 2h ago. CDK: ECS Fargate behind ALB. Error: upstream connect error."
)
→ {"status": "investigation_started", "taskId": "...", "executionId": "...", "message": "...", "next_steps": "..."}
```
Save the `taskId` and `executionId`.
> **Tip:** Pack as much context as possible into the `title` — service name, error type, time window, recent deploys. The agent uses this to scope its analysis.
## Stream progress — never silently poll
**Investigations take 5–8 minutes. Tell the user up front, then keep them informed.**
Loop every 30–45 seconds:
### 1. Check status
```
aws_devops_agent__get_task(task_id="TASK_ID")
→ {"task": {"taskId": "...", "status": "IN_PROGRESS", ...}}
```
### 2. Fetch new findings
```
aws_devops_agent__list_journal_records(execution_id="EXEC_ID", order="ASC")
→ {"records": [...]}
```
Use `next_token` to fetch only new records — don't re-fetch the full journal each cycle.
### 3. Summarize progress to the user
Map record types to emoji prefixes:
- `PLANNING` → 📋 planning approach
- `SEARCHING` → 🔍 querying CloudWatch / X-Ray / logs
- `ANALYSIS` → 🔬 analyzing
- `FINDING` → 🎯 key discovery (highlight this)
- `ACTION` → 🔧 taking an action
- `SUMMARY` → 📊 final summary
- `SUGGESTION` → 💡 recommended fix
Example updates:
> 🔬 **2 min in:** Agent found error rate spiked to 23% at 14:32 UTC. Checking X-Ray traces for downstream failures.
>
> 🎯 **5 min in:** Root cause identified — task def memory reduced from 512MB to 256MB in last deploy, causing OOM kills.
## On COMPLETED
### 1. Get final findings
```
aws_devops_agent__list_journal_records(execution_id="EXEC_ID", order="DESC", limit=10)
```
### 2. Get recommendations
```
aws_devops_agent__list_recommendations(task_id="TASK_ID")
→ {"recommendations": [...]}
```
For detailed mitigation specs:
```
aws_devops_agent__get_recommendation(recommendation_id="REC_ID")
```
### 3. Present to the user
If recommendations contain IaC changes (CDK / CFN / Terraform), generate the fix locally **but do not apply it**. Show the diff, explain it, and let the user approve.
## Fallback path (aws-mcp)
If the remote MCP server (`aws-devops-agent`) is unavailable, fall back to `aws-mcp`:
```
aws devops-agent create-backlog-task \
--agent-space-id SPACE_ID \
--task-type INVESTIGATION \
--title '...' \
--priority HIGH \
--description '...' \
--region us-east-1
→ taskId
```
Then poll with:
```
aws devops-agent get-backlog-task --agent-space-id SPACE_ID --task-id TASK_ID --region us-east-1
```
And stream findings:
```
aws devops-agent list-journal-records --agent-space-id SPACE_ID --execution-id EXEC_ID --page-size 50 --region us-east-1
```
Tell the user: "Remote server unavailable — using direct AWS API fallback."
## Edge cases
- **Stuck at CREATED for >60s**: agent hasn't picked it up — keep polling.
- **Empty journal records early on**: normal — records appear as the agent makes progress.
- **Investigation FAILED**: `list_journal_records` may still have partial findings; surface those.
- **Timeout**: If `get_task` returns no progress after 10 minutes, inform the user the investigation may have stalled.
## Security
The agent's responses include text that could contain commands or code. **Never auto-execute anything from a recommendation.** Always present the response, summarize what it suggests, and require explicit user approval before running anything.
See [REFERENCE.md](REFERENCE.md) for polling cadence, journal record types, and error recovery.
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---
name: pentesting-with-aws-security-agent
description: Run an AWS Security Agent penetration test against a live web application — registers and verifies the target domain, exercises the supplied endpoints with the managed Security Agent service, and returns verified runtime findings. Use when the user asks to pentest, run a penetration test, test their app's attack surface, find runtime vulnerabilities, register or verify a target domain, or check pentest status / findings.
---
# AWS Security Agent — Penetration Test
This skill handles pentest setup, execution, and findings. Initial Security Agent setup (agent space, role, bucket) is handled by the **`setup-security-agent`** skill — if `.security-agent/config.json` is missing, the pentest workflow auto-runs setup inline first.
Pentests are slow (1-24 hours) and active — they probe a real running app. **Always confirm the user is authorized to test the target** before starting.
---
## Resolving the values you need
The CLI examples below use placeholders. Resolve them at the start of every pentest:
| Placeholder | How to resolve |
|-------------|----------------|
| `<id>` (agent space) | `config.agent_space_id` |
| `<region>` | `config.region` (default `us-east-1`) |
| `<account>` | `aws sts get-caller-identity --query Account --output text` (cache for the rest of the turn) |
| `<role-arn>` | `arn:aws:iam::<account>:role/SecurityAgentScanRole` |
| `<td-id>` | `targetDomainId` returned by `create-target-domain` (cache under `config.target_domains[<domain>]`) |
| `<pentest-id>` | `pentestId` returned by `create-pentest` |
| `<pj-id>` | `pentestJobId` returned by `start-pentest-job` |
## Pre-pentest checks
1. **Read `.security-agent/config.json`.** If missing → tell the user one line — "First pentest in this workspace — running setup first." — and run the `setup-security-agent` workflow inline before continuing.
2. **Verify agent space still exists:**
```bash
aws securityagent batch-get-agent-spaces --agent-space-ids <id>
```
If missing, clear `agent_space_id` from `config.json` and run `setup-security-agent` again.
3. **Resolve account and role ARN** from the table above.
4. **Authorization check:** ask the user "Do you own or have explicit permission to pentest `<target>`?" if it's not obvious from context. Do not proceed without confirmation.
---
## Workflow
### 1. Register target domain (one-time per domain)
```bash
aws securityagent create-target-domain --agent-space-id <id> \
--target-domain-name <domain> --verification-method HTTP_ROUTE
```
The response includes a verification token / route. Tell the user what to put on their server (typically a `.well-known/...` file or HTTP route returning a token), then:
```bash
aws securityagent verify-target-domain --agent-space-id <id> --target-domain-id <td-id>
```
Persist the verified `target_domain_id` in `.security-agent/config.json` under `target_domains: { "<domain>": "<td-id>" }` so future pentests can reuse it.
### 2. Create a pentest
Ask the user for:
- **Title** (no spaces — use hyphens; default `pentest-<timestamp>`)
- **Endpoints** to test (one or more URIs under the verified domain)
```bash
aws securityagent create-pentest --agent-space-id <id> --title <title> \
--service-role <role-arn> \
--assets endpoints=[{uri=https://example.com/api/login},{uri=https://example.com/api/upload}]
```
Capture `pentestId`.
### 3. Start the pentest job
```bash
aws securityagent start-pentest-job --agent-space-id <id> --pentest-id <pentest-id>
```
Capture `pentestJobId`. Append to `.security-agent/pentests.json` (create as `[]` if it doesn't exist yet — the directory itself is already created by setup):
```json
{
"pentest_id": "p-...",
"pentest_job_id": "pj-...",
"agent_space_id": "as-...",
"title": "pentest-...",
"endpoints": ["https://..."],
"started_at": "2026-06-01T20:00:00Z",
"status": "IN_PROGRESS"
}
```
Tell user: "Pentest started ({pentest_job_id}). Pentests typically run 1-24 hours depending on scope. I'll check every 15 minutes — say 'stop polling' to opt out."
### 4. Polling loop
1. `sleep 900` (15 minutes) between checks. Do not poll faster.
2. Status:
```bash
aws securityagent batch-get-pentest-jobs --agent-space-id <id> --pentest-job-ids <pj-id>
```
3. Only respond when `status` changes or on terminal state (`COMPLETED`, `FAILED`, `STOPPED`).
4. On `COMPLETED` → run the Findings workflow.
### 5. Findings
```bash
aws securityagent list-findings --agent-space-id <id> --pentest-job-id <pj-id>
```
If `nextToken` is returned, call again with `--next-token <token>` until empty.
```bash
aws securityagent batch-get-findings --agent-space-id <id> --finding-ids <id1> <id2> ...
```
Present in chat grouped by severity (same icons + format as code scans):
```
🟣 CRITICAL: {name}
Endpoint: {endpoint}
{description}
```
Write a full report to `.security-agent/pentest-{pentest_job_id}.md` with every field returned (findingId, name, description, riskLevel, riskType, confidence, status, endpoint, request/response samples if present, and remediationCode if present).
Tell user: "Full details written to `.security-agent/pentest-{pentest_job_id}.md`"
### 6. Stop a pentest
```bash
aws securityagent stop-pentest-job --agent-space-id <id> --pentest-job-id <pj-id>
```
---
## Rules
- **Always confirm authorization** to test the target before starting
- Verify the target domain before creating a pentest — `create-pentest` will fail otherwise
- Reuse a verified `target_domain_id` from `config.json` instead of re-verifying
- Pentest titles must not contain spaces — use hyphens
- Poll every 15 minutes max — pentests are long-running
- Don't auto-restart a failed pentest — show the failure to the user first
---
## Troubleshooting
- **`ValidationException` on `verify-target-domain`** → the verification route isn't responding correctly yet. Ask user to confirm the route is live and serving the expected token.
- **`target domain not verified`** → run verify-target-domain (step 1) again.
- **Pentest stuck in `IN_PROGRESS` for >24 hours** → likely a backend issue or the target is unreachable. Stop and inspect.
- **`AccessDenied` on the service role** → the service role doesn't have the network/runtime permissions a pentest needs. The default `SecurityAgentScanRole` is for code scans only — pentests against AWS resources may need broader permissions. Direct user to the AWS Security Agent console to configure a pentest-specific role.
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---
name: remediating-with-aws-security-agent
description: >-
Pull AWS Security Agent findings (penetration tests and code reviews) and drive
remediation. Use this whenever the user mentions Security Agent, security findings,
pentest or penetration test results, code review findings, vulnerabilities found in
their AWS account, "what did the security scan find", remediating or triaging security
risks, or wants to start fixing reported vulnerabilities — even if they don't name the
service explicitly. Trigger it for phrases like "get my security findings", "what
vulnerabilities do we have", "let's fix the pentest results", or "triage the security
report". The skill discovers scans, exports findings to a gitignored local directory
(so sensitive exploit detail is never committed), produces a prioritized triage
summary, and offers to start fixing the highest-risk issues.
---
# Security Agent Remediation
AWS Security Agent is a frontier agent that runs on-demand penetration tests and code
reviews against a customer's applications and reports verified security risks. This skill
takes you from "I have findings somewhere in AWS" to "I'm actively fixing the most
important ones," while keeping the sensitive exploit detail out of source control.
The flow has four stages, and they matter in order:
1. **Discover** which scans exist and how the account is configured (live, read-only).
2. **Export** the findings to a local gitignored directory.
3. **Triage** the findings into a prioritized, human-readable plan.
4. **Remediate** by offering to fix the highest-risk issues.
## Why the ordering and the guardrails matter
Findings contain working attack scripts, reproduction steps, file paths, and sometimes
leaked secrets or environment details. If that lands in a Git repo, a customer can
accidentally commit and publish a step-by-step exploit for their own production system.
So the non-negotiable rule is: **findings are written only to `.security-agent/`, and that
path is gitignored before anything is written.**
## Stage 1: Discover scans (live, read-only)
Find out what the account has. All commands are read-only `list-*` operations.
AWS Security Agent organizes data as a hierarchy — work down it:
```
Application (account + Region)
└── Agent Space (workspace for design review, code review, and pentests)
├── Penetration test → Pentest job → Findings
└── Code review → Code review job → Findings
```
Run these to orient yourself and show the user what exists:
```bash
aws securityagent list-agent-spaces
aws securityagent list-pentests --agent-space-id <as-...>
aws securityagent list-code-reviews --agent-space-id <as-...>
aws securityagent list-pentest-jobs-for-pentest --agent-space-id <as-...> --pentest-id <pt-...>
aws securityagent list-code-review-jobs-for-code-review --agent-space-id <as-...> --code-review-id <cr-...>
```
Job `status` is one of `IN_PROGRESS`, `STOPPING`, `STOPPED`, `FAILED`, `COMPLETED`. Only
`COMPLETED` jobs have a stable, full set of findings.
### Match the codebase to a scan, then confirm
Agent spaces, pentests, and code reviews are named after the application they target.
Before asking the user to pick from a raw list, make an informed guess about which scan
corresponds to *this* repository — the user is working in a codebase for a reason, and
the relevant findings are almost always for the app in front of them.
Infer the app identity from the workspace using cheap, high-signal sources:
- The repository / root directory name and the Git remote URL (`git remote -v`).
- Project manifests and their `name`/`description` (`package.json`, `pyproject.toml`,
`*.csproj`, `go.mod`, `Cargo.toml`).
- README titles, product/steering docs, and any obvious product or company name.
- Distinctive frameworks or domains that match a scan title.
Compare those signals against the agent space / scan names (case-insensitive, allow
partial and fuzzy matches).
Then **always confirm before exporting** — present your best guess and your reasoning, and
let the user correct it:
> "This repo looks like **`<product>`** (from `<signal>`), which matches the **<name>** agent
> space. Use that, or pick another? [Other Agent Space names, ...]"
If nothing matches with reasonable confidence, say so plainly and show the full list rather
than forcing a wrong guess. Never export from a guessed scan without the user's confirmation.
## Stage 2: Export findings to `.security-agent/` (gitignored)
Pull findings using AWS CLI commands. Write everything into `.security-agent/` in the repo —
never to chat or stdout — because findings include working attack scripts, reproduction
steps, and sometimes leaked secrets.
### 1. Lock down the output directory before pulling anything
```bash
mkdir -p .security-agent
echo '*' > .security-agent/.gitignore
```
### 2. Resolve the latest COMPLETED job
You should already have the `agentSpaceId` and the pentest/code-review id from Stage 1.
List jobs for the chosen scan:
```bash
# Pentest jobs:
aws securityagent list-pentest-jobs-for-pentest \
--agent-space-id <as-...> --pentest-id <pt-...>
# Code review jobs:
aws securityagent list-code-review-jobs-for-code-review \
--agent-space-id <as-...> --code-review-id <cr-...>
```
Paginate by passing `--next-token` from the previous response until absent. Filter the
job summaries to `status == "COMPLETED"`. If none are COMPLETED, stop and tell the user
"No completed jobs found. Please wait for a job to complete or check job statuses."
Otherwise, pick the COMPLETED job with the greatest `createdAt` timestamp.
### 3. List finding summaries and filter by confidence
```bash
# Pentest findings:
aws securityagent list-findings \
--agent-space-id <as-...> --pentest-job-id <pj-...>
# Code review findings:
aws securityagent list-findings \
--agent-space-id <as-...> --code-review-job-id <cj-...>
```
Paginate on `--next-token` until exhausted. Confidence values from weakest to strongest:
`FALSE_POSITIVE`, `UNCONFIRMED`, `LOW`, `MEDIUM`, `HIGH`.
**Keep only `HIGH` and `MEDIUM` by default.** Widen only when the user explicitly asks.
### 4. Fetch full detail in batches of 25
`batch-get-findings` accepts at most 25 ids per call. Chunk the filtered finding ids into
groups of 25:
```bash
aws securityagent batch-get-findings \
--agent-space-id <as-...> \
--finding-ids <fid-1> <fid-2> ... <fid-25>
```
Tag each returned finding with its source (`pentest` or `code-review`) before writing,
so triage in Stage 3 can tell them apart.
### 5. Write findings into `.security-agent/`
Group findings by job id. For each job, write a full markdown report to
`.security-agent/findings_<jobId>.md` with ALL fields returned by the API (findingId,
name, description, riskLevel, riskType, confidence, status, codeLocations, remediationCode,
and any other fields). Do not leave off any fields.
### Edge cases
- **No agent space, scan, or COMPLETED job** — stop and surface that to the user rather
than retrying.
- **Credentials or service unavailable** — confirm with `aws sts get-caller-identity` and
check the Region (default `us-east-1`; Security Agent is regional).
- **Don't paste finding contents into chat** beyond short titles and counts. The detail
belongs in the gitignored files.
## Stage 3: Triage into a prioritized plan
Rank by risk, because remediation time is finite and a CRITICAL unauthenticated RCE
outranks a LOW informational finding every time. Read the exported `findings_*.md`
files from `.security-agent/` and sort them deterministically.
### Ranking rules
Sort ascending by this composite key (lower wins, i.e. more urgent first):
1. **Risk level**, in this order:
`CRITICAL` (0) → `HIGH` (1) → `MEDIUM` (2) → `LOW` (3) → `INFORMATIONAL` (4) →
`UNKNOWN` / missing (5).
2. **Risk score**, highest first. `riskScore` is a numeric string on pentest findings
(e.g. `"10.0"`), often absent on code-review findings — treat missing as the lowest
possible score so it sorts after scored findings of the same level.
3. **Confidence**, in this order:
`HIGH` (0) → `MEDIUM` (1) → `LOW` (2) → `UNCONFIRMED` (3) → `FALSE_POSITIVE` (4).
Also compute a severity-count summary across all findings (e.g. `2 CRITICAL · 5 HIGH ·
3 MEDIUM`) for the header of the report.
### Pulling the code location
For each finding, derive a single short `location` string:
- If `filePath` is set, use it as-is.
- Otherwise, take `codeLocations[0]`. Strip the scanner's sandbox prefix from `filePath`
(everything up to and including that marker) so the path is repo-relative; if that
marker isn't present, fall back to the basename. Append `:<lineStart>` when present.
- If neither is available (typical for some pentest findings), leave it blank and
describe the affected endpoint or attack chain in the impact line instead.
### Summary format
Write a compact summary for the user:
```
## Security Agent triage — <agent space name>
<N> findings exported (<P pentest, C code review>) · confidence: <levels> · severity: <counts>
### Priority order
1. [CRITICAL · score 10.0 · HIGH confidence] <finding name>
- Type: <riskType> · Source: <pentest|code-review>
- Where: <file:line or endpoint, if present>
- Impact: <one-line plain-language summary>
2. [HIGH · ...] ...
### Recommended remediation order
<short rationale: which to fix first and why — e.g. "1 and 3 are both
unauthenticated RCE on internet-facing endpoints; fix those before the
stored-XSS issues.">
```
If more than ~10 findings, show the top N in detail and summarize the rest as a count
by severity at the bottom.
### What to keep out of chat
The full `description`, `reasoning`, and `attackScript` stay in the gitignored files —
they contain working exploit detail. In the chat summary keep impact lines to one line
each, in plain language. Code-review findings usually carry a `filePath`/location and a
`suggestedFix`; call those out since they map directly to repo changes. Pentest findings
describe endpoints and attack chains; map them to the responsible code where you can.
Look for findings that corroborate each other (a pentest and a code review flagging the
same root cause) — those are strong signals for what to fix first.
## Stage 4: Offer to remediate
After presenting the triage, offer to start fixing — don't silently begin editing code.
Ask the user something like: "Want me to start fixing the top finding(s)? I'd recommend
starting with #1 (<name>)." If they agree, work top-down by priority:
1. Read the finding detail from the gitignored export file (location, description, suggested fix).
2. Open the affected file and apply the fix via the editor.
3. Report one line per fix: "Fixed {name} in `{filePath}:{lineStart}`."
If the user wants to handle several findings, fix one at a time (or one cluster of related
findings) so each change stays reviewable, and proceed in the priority order from Stage 3.
## Notes and edge cases
- **No completed jobs**: a scan may still be `IN_PROGRESS`. Tell the user; offer to re-check
later rather than exporting a partial job.
- **Re-running**: each run overwrites the files for that job id. The directory is safe to
delete; it only holds exported copies, not source-of-truth data.
- **Multiple accounts/Regions**: findings are Region-scoped. If the user expected results
and got none, confirm the region matches where Security Agent is configured.
- **Data handling**: treat exported findings as sensitive. They are copies of verified
exploits against the user's own systems.
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---
name: running-release-tests
description: >-
Run automated release testing (UI or API) via the AWS DevOps Agent using a
pre-configured test profile. Use when the user wants to validate multi-step
workflows, verify features, check for regressions, or test API endpoints.
Trigger words include run tests, UAT, test my app, test profile, UI test,
API test, automated testing, regression test, QA, end-to-end test, run the QA agent.
---
# Release Testing
> **AgentSpace routing (SigV4 only):** If `list_agent_spaces` is available in your tool list and the multi-space orchestration skill has NOT been invoked yet this session, invoke it first to determine which `agent_space_id` to use. Then pass `agent_space_id` on all tool calls below. For bearer token auth this is unnecessary — the token is already scoped to one space.
Run automated release testing in the cloud via the AWS DevOps Agent's Release Testing Agent. Supports UI testing (browser-based) and API testing (OpenAPI spec-based). Uses pre-existing test profiles that define target URL, agent type, personas, and credentials.
**Input is a test profile** — the test profile already contains the target URL, agent type (UI or API), test personas, and credentials. Do NOT ask the user for a URL directly; the URL is defined in the test profile.
## Prerequisites
- A pre-existing test profile (Knowledge Item ID like `ki-12345`) created from the AWS DevOps Agent console
## Gathering test parameters
Before starting any workflow, you MUST gather the following parameters. Do NOT proceed to job creation until answered.
### Step 1 — Test profile (required)
Ask the user which test profile to use. The test profile already contains the target URL, agent type (UI or API), test personas, and credentials configuration — these do NOT need to be gathered separately.
**Note:** A pre-existing test profile is a prerequisite. Test profiles are created using the AWS DevOps Agent console or API, not through this tool. If the user asks whether one can be created here, inform them it must already exist.
### Step 2 — Test requirement (optional)
If the user has not already mentioned a test focus, ask:
> "Do you have a specific test requirement or focus area? If not, I'll run a full exploratory test."
Wait for the user's response. If they provide one, use it as the `test_requirement`. If they say no or skip, proceed without it.
**IMPORTANT: You MUST wait for the user to respond before proceeding to job creation.**
## Core workflow
### 1. Select Agent Space
List available agent spaces:
```
aws devops-agent list-agent-spaces --region us-east-1
```
Present the list to the user and ask which agent space they'd like to use. **Do NOT proceed until the user has selected one.** Use the selected `agentSpaceId` as `SPACE_ID` in all subsequent calls.
### 2. Check tool availability
Verify that the following tools are available: `aws_devops_agent__create_release_testing_job`, `aws_devops_agent__get_task`, `aws_devops_agent__list_journal_records`, `aws_devops_agent__get_release_ui_testing_report`, `aws_devops_agent__get_release_api_testing_report`. These tools are NOT deferred/lazy-loaded — if they do not appear in your tool list, they are unavailable. Do NOT search for them via ToolSearch. If any are missing, skip the remaining steps in this section and use the "Fallback (aws-mcp)" path below instead.
### 3. Start the Job
```
aws_devops_agent__create_release_testing_job(
test_profile_id="ki-12345",
webhook_event_message="<optional test requirement>"
)
→ {"taskId": "...", "executionId": "...", "status": "started"}
```
Record the **taskId** and **executionId** from the response.
### 4. Poll for Status
Call `aws_devops_agent__get_task(task_id=TASK_ID)` every **30 seconds** until the status transitions to `IN_PROGRESS` or a terminal state.
### 5. Monitor Until Completion
Once `IN_PROGRESS`, poll for progress in a loop:
1. Call `aws_devops_agent__list_journal_records(execution_id=EXEC_ID, order="ASC")` to fetch new findings.
2. Present each record to the user with a friendly progress update.
3. Use `next_token` from the response to fetch only new records on subsequent polls.
4. **Wait 20 seconds** between each poll iteration.
5. Check `aws_devops_agent__get_task(task_id=TASK_ID)` periodically — stop when terminal status (`COMPLETED`, `FAILED`, `CANCELED`, `TIMED_OUT`).
### 6. Present Results
Once the job reaches a terminal status:
- If `COMPLETED`:
1. Determine the report type from the test profile's agent type (UI or API). Call `aws_devops_agent__get_release_ui_testing_report(execution_id=EXEC_ID)` for UI profiles or `aws_devops_agent__get_release_api_testing_report(execution_id=EXEC_ID)` for API profiles.
2. Write the report contents to a markdown file:
```
release-testing-report-<YYYY-MM-DD-HHmmss>.md
```
3. Inform the user that the report was saved, including the file path.
- If `FAILED` or `TIMED_OUT`: Present the error information and suggest next steps.
- If `CANCELED`: Inform the user the job was canceled and no report is available.
## Cancelling a job
```
aws_devops_agent__cancel_release_testing_job(task_id=TASK_ID)
```
## Error handling
1. If the task status changes to `FAILED`, stop the workflow and report the error.
2. If the task does not reach `IN_PROGRESS` within 5 minutes, cancel it using `cancel_release_testing_job`.
3. If any output contains "NoCredentialsError", "ExpiredTokenException", or auth failures, suggest the user refresh their credentials or check the bearer token.
4. If throttled (`429` or `ThrottlingException`), wait 30 seconds before retrying. After 3 retries, inform the user.
## Fallback (aws-mcp)
If the `aws-devops-agent` remote server is unavailable, use the AWS CLI directly:
Tell the user: "Remote server unavailable — using direct AWS API fallback."
### 1. Select Agent Space
List available agent spaces:
```
aws devops-agent list-agent-spaces --region us-east-1
```
Present the list to the user and ask which agent space they'd like to use. **Do NOT proceed until the user has selected one.** Use the selected `agentSpaceId` as `SPACE_ID` in all subsequent calls.
### 2. Start the Job
```
aws devops-agent create-backlog-task \
--agent-space-id SPACE_ID \
--task-type RELEASE_TESTING \
--title 'Release Testing' \
--priority MEDIUM \
--description '{\"testProfileId\": \"<PROFILE_ID>\", \"webhookEventMessage\": \"<REQUIREMENT>\"}' \
--region us-east-1
```
If the user provided a test requirement, include it as `webhookEventMessage`. If not, omit the field or leave it empty.
### 3. Poll for Status
```
aws devops-agent get-backlog-task \
--agent-space-id SPACE_ID \
--task-id TASK_ID \
--region us-east-1
```
Poll every **30 seconds** until the status transitions to `IN_PROGRESS` or a terminal state (`COMPLETED`, `FAILED`, `CANCELED`, `TIMED_OUT`).
### 4. Monitor Until Completion
Once `IN_PROGRESS`, poll for progress in a loop:
```
aws devops-agent list-journal-records \
--agent-space-id SPACE_ID \
--execution-id EXEC_ID \
--order ASC \
--region us-east-1
```
1. Present each record to the user with a friendly progress update.
2. Use `next_token` from the response to fetch only new records on subsequent polls.
3. **Wait 20 seconds** between each poll iteration.
4. Check `get-backlog-task` periodically — stop when terminal status (`COMPLETED`, `FAILED`, `CANCELED`, `TIMED_OUT`).
### 5. Present Results
Once the job reaches a terminal status:
- If `COMPLETED`:
1. Retrieve the report using the appropriate record type:
- **UI testing**: `--record-type qa_ui_testing_report`
- **API testing**: `--record-type qa_api_testing_report`
```
aws devops-agent list-journal-records \
--agent-space-id SPACE_ID \
--execution-id EXEC_ID \
--record-type qa_ui_testing_report \
--order ASC \
--region us-east-1
```
2. Write the report contents to a markdown file:
```
release-testing-report-<YYYY-MM-DD-HHmmss>.md
```
3. Inform the user that the report was saved, including the file path.
- If `FAILED` or `TIMED_OUT`: Present the error information and suggest next steps.
- If `CANCELED`: Inform the user the job was canceled and no report is available.
#### Cancelling (fallback)
```
aws devops-agent update-backlog-task \
--agent-space-id SPACE_ID \
--task-id TASK_ID \
--task-status CANCELED \
--region us-east-1
```
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---
name: scanning-with-aws-security-agent
description: Run an AWS Security Agent scan on the workspace — uploads the source to AWS, scans it with the managed Security Agent service, and returns ranked, verified findings with code locations and remediations. Use when the user asks to scan code, find vulnerabilities, run a security scan or review, check security issues, check scan status, show findings, list recent scans, or stop a scan.
---
# AWS Security Agent — Code Scans
This skill handles full repository scans. Setup (agent space, role, bucket) is handled by the **`setup-security-agent`** skill — if `.security-agent/config.json` is missing, the scan workflow auto-runs setup inline first.
---
## Action mapping
| User intent | Workflow |
|-------------|----------|
| Direct scan request ("scan my code", "find vulnerabilities") | Full Scan |
| Scan status check ("how's the scan", "progress") | Status workflow |
| View findings ("what did it find", "show results") | Findings workflow |
| List scans ("recent scans", "show my scans") | Read `.security-agent/scans.json` |
| Stop a scan | `aws securityagent stop-code-review-job` |
### Rules for proactive suggestions
- Always ask before running — never auto-trigger scans
- Single-line suggestions, not multi-paragraph pitches
- If the user declines, do not bring it up again in the same session
---
## Local state
Read `.security-agent/config.json` for `agent_space_id` and `region`. If `config.json` is missing, tell the user one line — "First scan in this workspace — running setup first." — and run the **`setup-security-agent`** workflow inline (steps from that skill's SKILL.md) before continuing. First-time scans should "just work."
Track scans in `.security-agent/scans.json` (keep last 50 entries). The per-workspace CodeReview ID is stored in `config.json → code_reviews[<abs_path>]` so subsequent scans reuse the same CodeReview.
### Resolving the values you need
The CLI examples below use placeholders. Resolve them at the start of every scan:
| Placeholder | How to resolve |
|-------------|----------------|
| `<id>` (agent space) | `config.agent_space_id` |
| `<region>` | `config.region` (default `us-east-1`) |
| `<account>` | `aws sts get-caller-identity --query Account --output text` (cache for the rest of the turn) |
| `<role-arn>` | `arn:aws:iam::<account>:role/SecurityAgentScanRole` |
| `<bucket>` | `security-agent-scans-<account>-<region>` |
| `<cr-id>` | `code_review_id` from `config.json → code_reviews[<abs_path>]` |
| `<job_id>` | `codeReviewJobId` returned by `start-code-review-job` |
| `<WORKSPACE_ID>` | `printf '%s' "$(pwd)" \| md5sum \| cut -c1-12` |
These are derived rather than stored in config so they can never drift out of sync with reality.
---
## Pre-scan checks
1. **Read `config.json`.** If missing → run the `setup-security-agent` workflow inline first, then continue.
2. **Verify agent space still exists:**
```bash
aws securityagent batch-get-agent-spaces --agent-space-ids <id>
```
If response shows it doesn't exist, clear `agent_space_id` from `config.json` and run `setup-security-agent` again.
3. **Resolve account, role ARN, and bucket name** from the table above.
4. **Generate workspace ID:**
```bash
WORKSPACE_ID=$(printf '%s' "$(pwd)" | md5sum | cut -c1-12)
```
---
## Workflow: Full Scan (~45 min)
For scanning only changed code, use the `diff-scanning-with-aws-security-agent` skill instead. For threat modeling specs, use `threat-modeling-with-aws-security-agent`.
1. Run pre-scan checks above.
2. **Zip the workspace.** Exclude common build/cache directories. Honor `.gitignore`. Bail if zip > 2 GB.
```bash
cd <absolute-workspace-path>
zip -r /tmp/source.zip . \
-x ".git/*" \
-x ".security-agent/*" \
-x "node_modules/*" \
-x "__pycache__/*" \
-x ".venv/*" -x "venv/*" \
-x "dist/*" -x "build/*" -x "target/*" \
-x ".mypy_cache/*" -x ".pytest_cache/*" -x ".tox/*" \
-x ".next/*" -x "cdk.out/*" \
-x ".DS_Store" -x "Thumbs.db" \
-x "*.pyc" -x "*.pyo"
ZIP_BYTES=$(stat -f%z /tmp/source.zip 2>/dev/null || stat -c%s /tmp/source.zip)
if [ "$ZIP_BYTES" -gt 2147483648 ]; then echo "Zip too large (>2GB)"; exit 1; fi
```
3. **Upload** to the per-workspace stable key (overwrites any prior upload):
```bash
aws s3 cp /tmp/source.zip s3://<bucket>/security-scans/source/<WORKSPACE_ID>/source.zip
```
4. **Get or create the per-workspace CodeReview.** Look up `config.json → code_reviews[<abs_path>]`.
- If present, use that `code_review_id`.
- If absent, create:
```bash
aws securityagent create-code-review --agent-space-id <id> --title <title> \
--service-role <role-arn> \
--assets sourceCode=[{s3Location=s3://<bucket>/security-scans/source/<WORKSPACE_ID>/source.zip}]
```
Capture `codeReviewId` and persist to `config.json → code_reviews[<abs_path>]`.
- Title default: `pre-cr-<git-branch>` (use `git rev-parse --abbrev-ref HEAD`). Replace any spaces with hyphens.
5. **Start the job:**
```bash
aws securityagent start-code-review-job --agent-space-id <id> --code-review-id <cr-id>
```
- **If the response is `ResourceNotFoundException`**: the CodeReview was deleted externally. Recreate it (step 4) and retry.
6. Capture `codeReviewJobId`. Generate a local `scan_id` like `scan-<8-hex>`. Append to `scans.json`:
```json
{
"scan_id": "scan-...",
"code_review_id": "cr-...",
"job_id": "cj-...",
"agent_space_id": "as-...",
"scan_type": "FULL",
"title": "pre-cr-main",
"path": "/abs/path",
"started_at": "2026-06-01T20:00:00Z",
"status": "IN_PROGRESS"
}
```
7. Tell user: "Full scan started (scan_id: {id}). Takes ~45 minutes. I'll check every 5 minutes — say 'stop polling' to opt out."
8. Run the **Polling Loop** below with `sleep 300` between checks.
---
## Polling Loop
After starting a scan:
1. `sleep 300` (5 minutes). Do **not** poll faster than this.
2. Call status:
```bash
aws securityagent batch-get-code-review-jobs --agent-space-id <id> --code-review-job-ids <job_id>
```
3. Compare `status` to last seen status. Only respond to the user when status CHANGES (e.g., `IN_PROGRESS` → `COMPLETED`) or on terminal state (`COMPLETED`, `FAILED`, `STOPPED`).
4. Do not report "still in progress" multiple times — that's noise.
5. If user says "stop polling" or "check later" → stop the loop and tell them: "Say 'scan status' or 'show findings' anytime."
6. On `COMPLETED` → run the **Findings** workflow.
7. On `FAILED` → fetch the job's error info (`statusReason` if present), tell the user, write a brief failure note to `.security-agent/findings-{scan_id}.md`.
---
## Workflow: Status check (ad-hoc)
User says "scan status" / "how's the scan":
1. If user names a `scan_id`, use it. Otherwise use the most recent entry in `scans.json`.
2. Call `batch-get-code-review-jobs` once.
3. Update `scans.json` status field.
4. Report: status + elapsed time + current step (if any).
---
## Workflow: Findings
After a scan completes (or on user request):
### 1. Fetch findings (paginate)
```bash
aws securityagent list-findings --agent-space-id <id> --code-review-job-id <job-id>
```
If `nextToken` is returned, call again with `--next-token <token>` until exhausted.
### 2. Enrich with full details
```bash
aws securityagent batch-get-findings --agent-space-id <id> --finding-ids <id1> <id2> ...
```
### 3. Filter (optional)
If the user asked for a minimum severity (e.g., "high and above"), filter to that level:
- Severity order: CRITICAL > HIGH > MEDIUM > LOW > INFORMATIONAL.
### 4. Concise summary in chat
Group by severity. File path + line for each:
```
🟣 CRITICAL: {name}
File: {filePath}:{lineStart}
{description}
🔴 HIGH: {name}
File: {filePath}:{lineStart}
{description}
🟡 MEDIUM: {name}
File: {filePath}:{lineStart}
{description}
🟢 LOW: {name}
File: {filePath}:{lineStart}
{description}
```
### 5. Detailed report file
Write to `.security-agent/findings-{scan_id}.md`. Include EVERY field returned (findingId, name, description, riskLevel, riskType, confidence, status, codeLocations with filePath/lineStart/lineEnd, and remediationCode if present).
```markdown
# Security Scan Report — {scan_id}
**Scan type**: FULL
**Title**: {title}
**Started**: {started_at}
**Total findings**: {count}
## Summary
| Severity | Count |
|----------|-------|
| CRITICAL | N |
| HIGH | N |
| MEDIUM | N |
| LOW | N |
## Findings
### 🟣 CRITICAL: {name}
- **ID**: {findingId}
- **Risk type**: {riskType}
- **Confidence**: {confidence}
- **Status**: {status}
- **Location**: `{filePath}:{lineStart}-{lineEnd}`
**Description**: {description}
**Remediation**:
{remediationCode or remediation guidance from description}
(repeat for every finding)
```
Tell user: "Full details written to `.security-agent/findings-{scan_id}.md`"
### 6. Follow-ups
Ask:
- "Would you like to focus on the critical/high findings first?"
- "Should I explain any of these in more detail?"
- "Want me to fix these issues?"
For fixes: read the finding's description and code location, then synthesize and apply the fix via the Edit tool.
---
## Workflow: Stop a scan
User says "stop the scan":
```bash
aws securityagent stop-code-review-job --agent-space-id <id> --code-review-job-id <job_id>
```
Update `scans.json` status to `STOPPED`.
---
## Workflow: List recent scans
User asks "show my recent scans" / "list scans":
Read `.security-agent/scans.json`. Show in a compact table:
| scan_id | type | title | status | started |
|---------|------|-------|--------|---------|
| scan-abc | FULL | pre-cr-main | COMPLETED | 2h ago |
| scan-def | FULL | pre-cr-feature-x | FAILED | 1d ago |
---
## Rules
- Always run pre-scan checks (config exists + agent space verified) before any scan
- Scan APIs return immediately — poll status every 5 minutes
- Use the most recent scan in `scans.json` if the user doesn't name one
- Title must not contain spaces — use hyphens. Default to git branch name.
- Don't dump raw JSON — format with severity icons + file locations
- On `ResourceNotFoundException` from `start-code-review-job`, recreate the CodeReview and retry once
---
## Troubleshooting
- **"Not configured" / `config.json` missing** → run `setup-security-agent` skill first
- **`AccessDenied` on `s3 cp`** → bucket not registered on agent space, or trust policy wrong. Re-run setup.
- **`ResourceNotFoundException` on agent space** → it was deleted. Re-run setup.
- **Scan stuck in PREFLIGHT for >10 min** → backend issue, not client. Show `batch-get-code-review-jobs` output and tell user to escalate.
- **Code too large (zip > 2 GB)** → run on a subdirectory instead.
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---
name: setup-devops-agent
description: Setup and diagnostics for the AWS DevOps Agent MCP connection. Triggers when aws-devops-agent is missing from .mcp.json, when the connection is broken, or when the user says "set up devops agent" / "configure agent". Does NOT trigger if the MCP is already connected and working.
---
# AWS DevOps Agent — Claude Setup
The instructions below are specifically for setting up the AWS DevOps Agent plugin for Claude applications. For other clients, use this as a reference, but adjust the instructions based on the client's specific requirements.
## Step 0: Check if setup is needed
1/ Check if the "aws-devops-agent" MCP server is running. If it is, verify that it has a valid connection (see "Step 3: Verify connectivity").
If verification is successful, you should inform the user that the plugin is already setup using `SigV4 or Bearer Token`. Offer that you can switch the configuration to `Bearer Token or SigV4`, see "Step 2: Decide auth path" below for details.
If the user does not want to change their auth configuration, then you are DONE STOP HERE.
2/ Check for an MCP server config with a key "aws-devops-agent" in the following locations:
- Plugin scoped: `${CLAUDE_PLUGIN_ROOT}/.mcp.json`
- Project-scoped: .mcp.json (in your project directory, version-controlled)
- Project-specific: .claude/settings.local.json (in your project directory)
- User-specific local: ~/.claude/settings.local.json
- User-specific global: ~/.claude/settings.json
- Main Claude.json: ~/.claude.json
- Dedicated MCP file: ~/.claude/mcp_servers.json
Then:
- If `aws-devops-agent` key exists AND the server is connected (tools are available, see "Step 3: Verify connectivity") → Inform the user: "DevOps Agent is already configured and connected."; If Bearer Token is used in the MCP config, suggest that you can alternatively setup the plugin to use SigV4 credentials for the AWS DevOps Agent (multiple agent spaces, admin tooling). If SigV4 credentials are used in the MCP config, suggest that you can alternatively setup the plugin to use Bearer Token credentials for the AWS DevOps Agent (single agent space).
- If `aws-devops-agent` key exists but is failing → continue to "Step 1: Diagnose current state"
- If `aws-devops-agent` key does NOT exist → continue to "Step 1: Diagnose current state"
---
## Step 1: Diagnose current state
Run these checks:
```bash
# Bearer token
echo "DEVOPS_AGENT_TOKEN: $([ -n "$DEVOPS_AGENT_TOKEN" ] && echo 'set' || echo 'not set')"
echo "DEVOPS_AGENT_REGION: ${DEVOPS_AGENT_REGION:-not set}"
# SigV4 dependencies
uvx --version 2>&1
# AWS credentials
aws sts get-caller-identity 2>&1
```
Determine:
- `bearer_ready` = `DEVOPS_AGENT_TOKEN` is set AND `DEVOPS_AGENT_REGION` is set
- `sigv4_ready` = `aws sts get-caller-identity` succeeds AND `uvx` is installed
---
## Step 2: Decide auth path
After diagnostics, ALWAYS ask the user which path they want — even if only one is available. Present what you found and let them choose.
The user may want to use bearer token if they only have access to the operator app for an agent space.
The user may want to use SigV4 if they use multiple agent spaces and/or have admin permissions to manage agent spaces.
| Bearer ready | SigV4 ready | Action |
|:---:|:---:|--------|
| yes | yes | "You have both a bearer token and AWS credentials configured. Which would you prefer for the DevOps Agent? **Bearer token** (single agent space) or **AWS credentials / SigV4** (multiple agent spaces and admin tooling)?" |
| yes | no | "You have a bearer token configured. Would you like me to set up the DevOps Agent using your **Bearer token** (single agent space)? Or would you prefer to configure **AWS credentials / SigV4** instead (multiple agent spaces and admin tooling)?" |
| no | yes | "You have valid AWS credentials. Would you like me to set up the DevOps Agent using **SigV4** (multiple agent spaces and admin tooling)? Or would you prefer to set up a **Bearer token** instead (single agent space)?" |
| no | no | "Neither a bearer token nor AWS credentials are configured. Would you like to connect via **Bearer token** (single agent space) or **AWS credentials / SigV4** (multiple agent spaces and admin tooling)?" Then guide them through the chosen path. |
If the user would like to setup a bearer token, refer them to the AWS docs for [Connect to DevOps Agent remote servers](https://docs.aws.amazon.com/devopsagent/latest/userguide/accessing-devops-agent-connect-to-devops-agent-remote-servers.html#create-an-access-token)
or walk them through the steps to create a access token from this document.
**Do NOT proceed to Step 3 until the user confirms their choice.**
---
## Step 3: Verify connectivity
If the "aws-devops-agent" MCP server is already running, check if you can list tools. If you can, then you have verified the connection.
Otherwise, proceed.
Verify BEFORE writing `.mcp.json`. This confirms the credentials work against the live endpoint. Or use this to verify an existing MCP server config.
### Bearer verification
```bash
curl -s -w "\nHTTP_STATUS: %{http_code}" \
-X POST \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $DEVOPS_AGENT_TOKEN" \
-d '{"jsonrpc":"2.0","id":1,"method":"tools/list","params":{}}' \
"https://connect.aidevops.${DEVOPS_AGENT_REGION}.api.aws/mcp"
```
| Result | Meaning | Action |
|--------|---------|--------|
| HTTP 200 + `result.tools` array | Success | Proceed to Step 4 |
| HTTP 401 | Token invalid or expired | Tell user to create a new token in the Operator Web App |
| HTTP 403 | Token scope insufficient | Tell user token needs `agent:read` + `agent:operate` scopes |
| Connection refused / timeout | Endpoint unreachable | If SigV4 is available, offer fallback. Otherwise report unavailable. |
### SigV4 verification
```bash
timeout 30 bash -c '
{
echo "{\"jsonrpc\":\"2.0\",\"id\":1,\"method\":\"initialize\",\"params\":{\"protocolVersion\":\"2024-11-05\",\"capabilities\":{},\"clientInfo\":{\"name\":\"setup-check\",\"version\":\"1.0\"}}}"
sleep 0.5
echo "{\"jsonrpc\":\"2.0\",\"method\":\"notifications/initialized\"}"
sleep 0.5
echo "{\"jsonrpc\":\"2.0\",\"id\":2,\"method\":\"tools/list\",\"params\":{}}"
sleep 8
} | uvx mcp-proxy-for-aws@latest "https://connect.aidevops.${DEVOPS_AGENT_REGION}.api.aws/mcp" --service aidevops --region "$DEVOPS_AGENT_REGION"
'
```
> **Note:** The first run may take 10-15s as `uvx` downloads `mcp-proxy-for-aws` and its dependencies. Subsequent runs are near-instant.
| Result | Meaning | Action |
|--------|---------|--------|
| Second line contains `result.tools` | Success | Proceed to Step 4 |
| No output / timeout | Credentials invalid or endpoint unreachable | Check `aws sts get-caller-identity` again |
| `ExpiredTokenException` in stderr | AWS session expired | Tell user to re-authenticate (`aws sso login` or refresh creds) |
| `AccessDeniedException` | Missing IAM permissions | User needs DevOps Agent permissions on their role |
---
## Step 4: Confirm and write `.mcp.json`
Before writing, confirm with the user:
> "I've verified connectivity. I'll now add the **[Bearer token / SigV4]** MCP server to the plugin's `.mcp.json`. Proceed?"
Only write after the user confirms. Write ONE server entry — never both. Install the MCP config in `${CLAUDE_PLUGIN_ROOT}/.mcp.json`. You can also offer to install the MCP server at the workspace level. The installation options are:
- Plugin scoped: `${CLAUDE_PLUGIN_ROOT}/.mcp.json` (default)
- Project-scoped: .mcp.json (in your project directory, version-controlled)
- Project-specific: .claude/settings.local.json (in your project directory)
### Bearer config
```json
{
"mcpServers": {
"aws-devops-agent": {
"type": "http",
"url": "https://connect.aidevops.${DEVOPS_AGENT_REGION}.api.aws/mcp",
"headers": {
"Authorization": "Bearer ${DEVOPS_AGENT_TOKEN}"
},
"timeout": 120000
}
}
}
```
### SigV4 config
Replace `<REGION>` with the user's actual region:
```json
{
"mcpServers": {
"aws-devops-agent": {
"command": "uvx",
"timeout": 120000,
"args": [
"mcp-proxy-for-aws@latest",
"https://connect.aidevops.<REGION>.api.aws/mcp",
"--service", "aidevops",
"--region", "<REGION>"
]
}
}
}
```
### Fallback (aws-mcp)
Only add if the primary `aws-devops-agent` endpoint is unreachable AND SigV4 credentials are available:
```json
{
"mcpServers": {
"aws-mcp": {
"command": "uvx",
"timeout": 100000,
"args": [
"mcp-proxy-for-aws@latest",
"https://aws-mcp.us-east-1.api.aws/mcp",
"--metadata",
"AWS_REGION=us-east-1"
]
}
}
}
```
For Sigv4 only: After writing the new MCP config, inform the user that the MCP server has been written successfully. Proceed to the next step.
---
## Step 5: Multi-space routing (SigV4 only)
After successful SigV4 setup, discover and configure AgentSpace routing:
1. Call `list_agent_spaces` via the newly connected MCP to discover available spaces
2. Present the list to the user
3. If multiple spaces exist, write a routing guide to `.claude/aws-agents-for-devsecops.md`:
```markdown
# AWS DevOps Agent — Routing Guide
| Space | Agent Space ID | Purpose |
|-------|----------------|---------|
| <name> | <id> | <ask user> |
```
1. Instruct: pass `agent_space_id` on every tool call when targeting a specific space.
---
## Step 6: Reload plugin
Inform the user that they will need to run /reload-plugins to start the new MCP server. You may
need to prompt the user to run it. Also mention that after restarting the MCP server they should try the following prompts:
- setup multi-space routing (SigV4 only)
- <list skill and prompt suggestions from ${CLAUDE_PLUGIN_ROOT}/README.md>
---
## Bearer token guidance (for users who need to create one)
1. Open the AWS DevOps Agent **Operator Web App** for your AgentSpace
2. Navigate to **Settings → Access tokens → Generate token**
3. Create a token with Permissions: **`Operate`**
4. Set environment variables:
```bash
export DEVOPS_AGENT_TOKEN="<your-token>"
export DEVOPS_AGENT_REGION="<your-region>"
```
Available regions: https://docs.aws.amazon.com/devopsagent/latest/userguide/about-aws-devops-agent-supported-regions.html
5. Restart Claude Code (it reads env vars from the shell that launched it)
> **Important:** Without `Operate` permissions, the `chat` and `investigate` tools will be completely invisible — not just fail, but absent from the tool list.
---
## SigV4 guidance (for users who need to configure AWS credentials)
1. Install `uvx` if not present:
- macOS: `brew install uv`
- Linux: `curl -LsSf https://astral.sh/uv/install.sh | sh`
2. Configure AWS credentials:
```bash
aws configure sso --profile devops-agent
aws sso login --profile devops-agent
export AWS_PROFILE=devops-agent
```
3. Set the region:
```bash
export DEVOPS_AGENT_REGION="<your-region>"
```
4. Verify: `aws sts get-caller-identity`
5. The IAM role must have DevOps Agent permissions (e.g., managed policy with aidevops access)
> **Important:** Unset `DEVOPS_AGENT_TOKEN` when using SigV4. If both are set, clients may attempt bearer auth instead of the signing proxy.
---
## Troubleshooting
| Error | Cause | Fix |
|-------|-------|-----|
| No tools visible | Token not set or Claude Code not restarted | Set `DEVOPS_AGENT_TOKEN` + `DEVOPS_AGENT_REGION`, restart |
| HTTP 401 | Token invalid/expired | Create new token in Operator Web App |
| `chat`/`investigate` missing | Token scope is `agent:read` only | Create token with `agent:operate` scope |
| Connection refused / timeout | Endpoint unreachable | Check network; if SigV4 available, offer `aws-mcp` fallback |
| `ExpiredTokenException` | AWS session credentials expired | `aws sso login` or refresh credentials |
| `AccessDeniedException` (SigV4) | Missing IAM permissions | Use a role with DevOps Agent access |
| Proxy won't start | `uvx` not installed | `brew install uv` (macOS) or install per platform |
| Tools appear but calls timeout | Normal for `chat` (5-30s) | Ensure `"timeout": 120000` in mcp.json |
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---
name: setup-security-agent
description: Configure AWS Security Agent for the current workspace — provision or reuse an agent space, IAM service role, and S3 bucket. Use when the user asks to "set up security agent", "configure security scanner", "is security agent configured", or on first-time use before any scan or pentest.
---
# AWS Security Agent — Setup
This skill handles ONE thing: making sure the workspace has a working agent space, IAM service role, and S3 bucket linked together. Scans and pentests live in separate skills and assume this is done.
---
## Local state convention
All Security Agent skills share workspace-local state at `.security-agent/`:
- `config.json` — `{ "agent_space_id": "as-...", "region": "us-east-1", "code_reviews": { "<abs_path>": "cr-..." } }`. Account ID, role ARN, and bucket name are derived by convention. The `code_reviews` map lets scans reuse the same CodeReview for a workspace.
- `scans.json` — array of `{ scan_id, code_review_id, job_id, agent_space_id, scan_type, title, started_at, status, path }` (keep last 50)
- `pentests.json` — same shape, for pentest jobs
- `.gitignore` — contents `*` so this directory stays untracked
- `findings-{scan_id}.md` — written by the scan skill after each scan completes
This skill's job is to populate `config.json` and create `.gitignore`.
### Derived values (convention over config)
Other skills compute these on each invocation rather than reading them from `config.json`:
| Value | Convention |
|-------|------------|
| `ACCOUNT` | `aws sts get-caller-identity --query Account --output text` |
| `REGION` | `config.region` (default `us-east-1`) |
| `service_role_arn` | `arn:aws:iam::${ACCOUNT}:role/SecurityAgentScanRole` |
| `s3_bucket` | `security-agent-scans-${ACCOUNT}-${REGION}` |
Why minimal config: the role name and bucket name are deterministic, so storing them adds drift risk (a user re-creating a role manually would silently use a stale path). Only `agent_space_id` is stored because users may have multiple agent spaces and we don't want to ask which one every session.
---
## Workflow
1. **Check existing state:** read `.security-agent/config.json` if it exists.
2. **Caller identity + region:**
```bash
export ACCOUNT=$(aws sts get-caller-identity --query Account --output text)
export REGION="${AWS_REGION:-us-east-1}"
```
3. **Agent space:**
- If `config.agent_space_id` is set, verify with:
```bash
aws securityagent batch-get-agent-spaces --agent-space-ids <id>
```
If the response shows it doesn't exist, treat as missing.
- If missing, list existing:
```bash
aws securityagent list-agent-spaces
```
- If any exist → **show them to the user** with name + id and ask: "Would you like to reuse one of these, or should I create a new one?" Wait for the answer. **Do not auto-select.**
- If user picks one, use that `agentSpaceId`.
- If user wants new, or none exist:
```bash
aws securityagent create-agent-space --name security-scans
```
Capture returned `agentSpaceId`.
4. **Service role** (`SecurityAgentScanRole`, ARN `arn:aws:iam::$ACCOUNT:role/SecurityAgentScanRole`):
- Probe:
```bash
aws iam get-role --role-name SecurityAgentScanRole
```
- If `NoSuchEntity` is returned, create the role. **Idempotency note:** `create-role` will fail with `EntityAlreadyExists` if the role already exists. If that happens, fall through to `update-assume-role-policy` to ensure the trust policy is correct.
```bash
# Trust policy — includes aws:SourceAccount confused-deputy guard
cat > /tmp/sa-trust.json <<EOF
{"Version":"2012-10-17","Statement":[{"Effect":"Allow","Principal":{"Service":"securityagent.amazonaws.com"},"Action":"sts:AssumeRole","Condition":{"StringEquals":{"aws:SourceAccount":"${ACCOUNT}"}}}]}
EOF
# Permissions policy (S3 + CloudWatch Logs)
cat > /tmp/sa-perms.json <<EOF
{"Version":"2012-10-17","Statement":[
{"Effect":"Allow","Action":["s3:GetObject","s3:GetObjectVersion","s3:ListBucket"],"Resource":["arn:aws:s3:::security-agent-scans-${ACCOUNT}-${REGION}","arn:aws:s3:::security-agent-scans-${ACCOUNT}-${REGION}/*"]},
{"Effect":"Allow","Action":["logs:CreateLogGroup","logs:CreateLogStream","logs:PutLogEvents"],"Resource":"arn:aws:logs:*:${ACCOUNT}:log-group:/aws/securityagent/*"}
]}
EOF
aws iam create-role --role-name SecurityAgentScanRole --assume-role-policy-document file:///tmp/sa-trust.json
# if EntityAlreadyExists:
aws iam update-assume-role-policy --role-name SecurityAgentScanRole --policy-document file:///tmp/sa-trust.json
# always (re)apply permissions:
aws iam put-role-policy --role-name SecurityAgentScanRole --policy-name SecurityAgentCodeReviewAccess --policy-document file:///tmp/sa-perms.json
```
5. **S3 bucket** (`security-agent-scans-$ACCOUNT-$REGION`):
- Probe:
```bash
BUCKET="security-agent-scans-${ACCOUNT}-${REGION}"
aws s3api head-bucket --bucket "$BUCKET"
```
- If 404, create:
```bash
# us-east-1: no LocationConstraint
aws s3api create-bucket --bucket "$BUCKET"
# other regions:
aws s3api create-bucket --bucket "$BUCKET" --create-bucket-configuration LocationConstraint="$REGION"
```
- Always (re)apply public access block + 30-day lifecycle:
```bash
aws s3api put-public-access-block --bucket "$BUCKET" \
--public-access-block-configuration BlockPublicAcls=true,IgnorePublicAcls=true,BlockPublicPolicy=true,RestrictPublicBuckets=true
cat > /tmp/sa-lifecycle.json <<'EOF'
{"Rules":[{"ID":"AutoDeleteUploads","Status":"Enabled","Filter":{"Prefix":""},"Expiration":{"Days":30}}]}
EOF
aws s3api put-bucket-lifecycle-configuration --bucket "$BUCKET" --lifecycle-configuration file:///tmp/sa-lifecycle.json
```
6. **Register role + bucket on the agent space (idempotent):**
- Read existing resources:
```bash
aws securityagent batch-get-agent-spaces --agent-space-ids <id>
```
Look at `agentSpaces[0].awsResources.iamRoles` and `awsResources.s3Buckets`.
- If the role ARN or the bucket name is missing from those lists, merge and update:
```bash
aws securityagent update-agent-space --agent-space-id <id> --name <existing-name> \
--aws-resources iamRoles=[<arn1>,<arn2>...],s3Buckets=[<bucket1>,<bucket2>...]
```
7. **Persist** to `.security-agent/config.json` (minimal — account/role/bucket are derived):
```json
{
"agent_space_id": "as-xxxxx",
"region": "us-east-1"
}
```
8. **Create gitignore** if missing:
```bash
mkdir -p .security-agent
echo '*' > .security-agent/.gitignore
```
9. Confirm to user: "Setup complete. You can run security scans or pentests now."
---
## Rules
- Never auto-select an agent space when multiple exist — always ask the user
- Never disable safety protections (the public-access-block stays on)
- Trust policy must allow `securityagent.amazonaws.com` (production service principal) and include the `aws:SourceAccount` confused-deputy guard
- If the user provides their own role name or bucket name (different from the conventional defaults), tell them: this plugin uses convention-based defaults (`SecurityAgentScanRole` / `security-agent-scans-${ACCOUNT}-${REGION}`). Either accept those defaults or extend the skill — the other skills derive these names rather than reading them from config.
- The scan and pentest skills can call this skill inline if `config.json` is missing — first-time users don't need to run setup separately.
---
## Troubleshooting
- **`AccessDenied` calling `iam:CreateRole`** → user lacks IAM permissions. Ask them to run setup with their own role ARN, or to grant `iam:CreateRole` + `iam:PutRolePolicy`.
- **`AccessDenied` on `s3api create-bucket`** → either the bucket name is taken globally, or the user lacks `s3:CreateBucket`. Suggest using an existing bucket they own and pass it explicitly.
- **Role exists but trust policy is wrong** → `update-assume-role-policy` (step 4 fallback). If they don't want that role updated, ask them for a different role ARN.
- **Agent space exists but in a different region** → tell the user; suggest using the right region or creating a new space in the current region.
plugins/aws-agents-for-devsecops/skills/setup/SKILL.md
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---
name: setup
description: Set up the AWS DevOps Agent and AWS Security Agent connections. Use when the user says "set up", "configure", "connect", or when MCP tools are missing.
---
# Setup
Run these skills in order:
1. Invoke the `setup-devops-agent` skill to configure the DevOps Agent MCP connection.
2. Invoke the `setup-security-agent` skill to configure the Security Agent workspace (agent space, IAM role, S3 bucket).
If the user only needs one agent, run only the relevant skill.
plugins/aws-agents-for-devsecops/skills/threat-modeling-with-aws-security-agent/SKILL.md
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---
name: threat-modeling-with-aws-security-agent
description: Run an AWS Security Agent threat model review on spec/design documents. Use when the user asks to review a spec for security, run a threat model, check if a design introduces security risks, review requirements.md or design.md for security posture changes, or STRIDE analysis.
---
# AWS Security Agent — Threat Model Review
Analyze spec documents (`requirements.md`, `design.md`) against the source code to identify security-posture changes using STRIDE methodology. No prior scan needed.
## Local state
Read `.security-agent/config.json` for `agent_space_id` and `region`. If missing, run the `setup-security-agent` workflow inline first.
### Resolving the values you need
| Placeholder | How to resolve |
|-------------|----------------|
| `<id>` (agent space) | `config.agent_space_id` |
| `<region>` | `config.region` (default `us-east-1`) |
| `<account>` | `aws sts get-caller-identity --query Account --output text` |
| `<role-arn>` | `arn:aws:iam::<account>:role/SecurityAgentScanRole` |
| `<bucket>` | `security-agent-scans-<account>-<region>` |
---
## Workflow
1. **Pre-checks.** Read config, verify agent space, resolve values.
2. **Collect spec files.** Identify the `requirements.md` and/or `design.md` the user is working on. Use absolute paths. Ask if unclear which files to review.
3. **Zip the workspace** (same exclusions as code scan):
```bash
cd <absolute-workspace-path>
zip -r /tmp/source.zip . \
-x ".git/*" -x ".security-agent/*" -x "node_modules/*" \
-x "__pycache__/*" -x ".venv/*" -x "venv/*" \
-x "dist/*" -x "build/*" -x "target/*" \
-x ".mypy_cache/*" -x ".pytest_cache/*" -x ".tox/*" \
-x ".next/*" -x "cdk.out/*" -x ".DS_Store" -x "*.pyc"
```
4. **Upload source zip:**
```bash
SCAN_ID="tm-$(date +%s)-$(openssl rand -hex 3)"
WORKSPACE_ID=$(printf '%s' "$(pwd)" | md5sum | cut -c1-12)
aws s3 cp /tmp/source.zip s3://<bucket>/security-scans/source/${WORKSPACE_ID}/source.zip
```
5. **Upload spec files:**
```bash
aws s3 cp /path/to/requirements.md s3://<bucket>/security-scans/threat-models/${SCAN_ID}/specs/requirements.md
aws s3 cp /path/to/design.md s3://<bucket>/security-scans/threat-models/${SCAN_ID}/specs/design.md
```
6. **Create threat model:**
```bash
aws securityagent create-threat-model --agent-space-id <id> --title <title> \
--service-role <role-arn> \
--assets sourceCode=[{s3Location=s3://<bucket>/security-scans/source/${WORKSPACE_ID}/source.zip}] \
--scope-docs '[{"s3Location":"s3://<bucket>/security-scans/threat-models/'${SCAN_ID}'/specs/requirements.md"},{"s3Location":"s3://<bucket>/security-scans/threat-models/'${SCAN_ID}'/specs/design.md"}]'
```
Capture `threatModelId`.
7. **Start threat model job:**
```bash
aws securityagent start-threat-model-job --agent-space-id <id> --threat-model-id <tm-id>
```
Capture `threatJobId`.
8. Persist to `scans.json` with `scan_type: "THREAT_MODEL"`.
9. Tell user: "Threat model review started. Runtime varies with workspace size. I'll check every 2 minutes — say 'stop polling' to opt out."
10. **Poll** every 2 minutes:
```bash
aws securityagent batch-get-threat-model-jobs --agent-space-id <id> --threat-model-job-ids <tj-id>
```
Only respond when status changes.
11. **On COMPLETED** → fetch threats:
```bash
aws securityagent list-threats --agent-space-id <id> --threat-job-id <tj-id>
```
If `nextToken`, paginate with `--next-token`.
## Findings presentation
Each threat includes: `statement`, `severity`, `stride` category, `threatImpact`, `recommendation`, `impactedAssets`.
```
🟣 CRITICAL: {statement}
STRIDE: {stride}
Impact: {threatImpact}
Assets: {impactedAssets}
Recommendation: {recommendation}
🔴 HIGH: {statement}
...
```
Write full report to `.security-agent/findings-{scan_id}.md`. Call out any threat that represents a regression from the prior design.
---
## Rules
- Threat model reviews are standalone — no prior scan needed
- Poll every 2 minutes, not faster
- At least one spec file is required
- Use absolute paths for workspace and spec files
- Title: `threat-model-<feature-name>` (no spaces)
plugins/aws-agents/.claude-plugin/plugin.json
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@@ -0,0 +1,53 @@
{
"author": {
"name": "Amazon Web Services"
},
"description": "Build, deploy, and operate AI agents on AWS. Skills for scaffolding agents with Amazon Bedrock AgentCore (Strands, LangGraph), connecting tools via Gateway and MCP, multi-agent and A2A orchestration, memory, Cedar policies, evaluation, observability, debugging traces and logs, and production hardening (inbound auth, IAM, rate limiting, cold-start tuning).",
"homepage": "https://github.com/aws/agent-toolkit-for-aws",
"keywords": [
"aws",
"amazon",
"amazon-web-services",
"ai",
"ai-agents",
"agents",
"agentcore",
"bedrock",
"amazon-bedrock",
"iam",
"deploy",
"debug",
"memory",
"gateway",
"policy",
"cedar",
"evaluation",
"evals",
"strands",
"langgraph",
"mcp",
"a2a",
"multi-agent",
"tool-use",
"rag",
"vpc",
"observability",
"cloudwatch",
"tracing",
"x-ray",
"production-hardening",
"jwt",
"sigv4",
"oauth",
"openapi",
"code-interpreter",
"browser-tool",
"rate-limiting"
],
"license": "Apache-2.0",
"name": "aws-agents",
"repository": "https://github.com/aws/agent-toolkit-for-aws",
"skills": "./skills/",
"mcpServers": "./.mcp.json",
"version": "1.0.0"
}
plugins/aws-agents/.codex-plugin/plugin.json
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@@ -0,0 +1,75 @@
{
"name": "aws-agents",
"version": "1.0.0",
"description": "Build, deploy, and operate AI agents on AWS. Skills for scaffolding agents with Amazon Bedrock AgentCore (Strands, LangGraph), connecting tools via Gateway and MCP, multi-agent and A2A orchestration, memory, Cedar policies, evaluation, observability, debugging traces and logs, and production hardening (inbound auth, IAM, rate limiting, cold-start tuning).",
"author": {
"name": "Amazon Web Services",
"email": "aws-agent-plugins@amazon.com",
"url": "https://github.com/aws/agent-toolkit-for-aws"
},
"homepage": "https://aws.amazon.com/products/developer-tools/agent-toolkit-for-aws/",
"repository": "https://github.com/aws/agent-toolkit-for-aws",
"license": "Apache-2.0",
"keywords": [
"aws",
"amazon",
"amazon-web-services",
"ai",
"ai-agents",
"agents",
"agentcore",
"bedrock",
"amazon-bedrock",
"iam",
"deploy",
"debug",
"memory",
"gateway",
"policy",
"cedar",
"evaluation",
"evals",
"strands",
"langgraph",
"mcp",
"a2a",
"multi-agent",
"tool-use",
"rag",
"vpc",
"observability",
"cloudwatch",
"tracing",
"x-ray",
"production-hardening",
"jwt",
"sigv4",
"oauth",
"openapi",
"code-interpreter",
"browser-tool",
"rate-limiting"
],
"skills": "./skills/",
"mcpServers": "./.mcp.json",
"interface": {
"displayName": "AI Agents on AWS",
"shortDescription": "Build, deploy, and operate AI agents on AWS.",
"longDescription": "Complete developer workflow for building AI agents on AWS — scaffolding with Amazon Bedrock AgentCore (Strands, LangGraph), tool integration via Gateway and MCP, multi-agent and A2A orchestration, memory, Cedar policies, evaluation and observability, debugging traces and logs, VPC networking, and production hardening (inbound auth, IAM scoping, rate limiting, cold-start tuning).",
"defaultPrompt": [
"How do I build an agent on AWS?",
"Deploy my agent to AgentCore.",
"My agent isn't working, help me debug it."
],
"developerName": "Amazon Web Services",
"category": "Development",
"capabilities": [
"Read",
"Write"
],
"websiteURL": "https://github.com/aws/agent-toolkit-for-aws",
"privacyPolicyURL": "https://aws.amazon.com/privacy/",
"termsOfServiceURL": "https://aws.amazon.com/service-terms/",
"brandColor": "#FF9900"
}
}
plugins/aws-agents/.cursor-plugin/plugin.json
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@@ -0,0 +1,56 @@
{
"name": "aws-agents",
"displayName": "AI Agents on AWS",
"description": "Build, deploy, and operate AI agents on AWS. Skills for scaffolding agents with Amazon Bedrock AgentCore (Strands, LangGraph), connecting tools via Gateway and MCP, multi-agent and A2A orchestration, memory, Cedar policies, evaluation, observability, debugging traces and logs, and production hardening (inbound auth, IAM, rate limiting, cold-start tuning).",
"version": "1.0.0",
"author": {
"name": "Amazon Web Services",
"email": "aws-agent-plugins@amazon.com"
},
"homepage": "https://github.com/aws/agent-toolkit-for-aws",
"repository": "https://github.com/aws/agent-toolkit-for-aws",
"license": "Apache-2.0",
"category": "developer-tools",
"keywords": [
"aws",
"amazon",
"amazon-web-services",
"ai",
"ai-agents",
"agents",
"agentcore",
"bedrock",
"amazon-bedrock",
"iam",
"deploy",
"debug",
"memory",
"gateway",
"policy",
"cedar",
"evaluation",
"evals",
"strands",
"langgraph",
"mcp",
"a2a",
"multi-agent",
"tool-use",
"rag",
"vpc",
"observability",
"cloudwatch",
"tracing",
"x-ray",
"production-hardening",
"jwt",
"sigv4",
"oauth",
"openapi",
"code-interpreter",
"browser-tool",
"rate-limiting"
],
"skills": "./skills/",
"mcpServers": "./.mcp.json"
}
plugins/aws-agents/.mcp.json
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@@ -0,0 +1,8 @@
{
"mcpServers": {
"awsknowledge": {
"type": "http",
"url": "https://knowledge-mcp.global.api.aws"
}
}
}
plugins/aws-agents/README.md
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@@ -0,0 +1,74 @@
# AI Agents on AWS
Build, deploy, and operate AI agents on AWS with guided workflows for every stage of the developer journey. This plugin covers the full agent lifecycle using Amazon Bedrock AgentCore as the primary runtime.
## Overview
This plugin provides 7 skills covering the full agent lifecycle — from scaffolding a new project to production hardening. Skills use progressive disclosure to load detailed reference material on demand, keeping context lean while providing deep expertise when needed.
## Skills
| Skill | When to use | References |
|---|---|---|
| `agents-get-started` | "build an agent", "create an agent", "get started", "which framework" | example-support-agent |
| `agents-build` | "add memory", "remember across sessions", "call agent from app", "VPC", "multi-agent", "migrate from Bedrock" | memory, integrate, vpc, multi-agent, migrate, local-vs-deployed |
| `agents-connect` | "connect to API", "add gateway", "give my agent tools", "Cedar policy", "restrict tools" | policy |
| `agents-deploy` | "deploy my agent", "deploy failed", "CDK error", "rollback", "canary" | versioning |
| `agents-debug` | "agent not working", "check logs", "command not found", "check my setup" | doctor |
| `agents-optimize` | "evaluate my agent", "measure quality", "quality gate", "observability", "traces", "cost" | evals, observability, cost |
| `agents-harden` | "production checklist", "go to production", "secure agent", "before launch", "cold start" | limits |
## Routing guide
When in doubt about which skill to reach for:
- **Starting from nothing?** → `agents-get-started`
- **Environment/CLI broken?** → `agents-debug` (loads `references/doctor.md`)
- **Adding new capabilities to a working project?** → `agents-build`
- **Connecting to external tools/APIs or restricting access?** → `agents-connect`
- **Ready to ship?** → `agents-deploy`
- **Agent is broken?** → `agents-debug`
- **Measuring quality, observability, or cost?** → `agents-optimize`
- **Going to production?** → `agents-harden`
## MCP Servers
| Server | Purpose |
|---|---|
| `awsknowledge` | AWS documentation, architecture guidance, and service reference |
## Installation
### Claude Code
```
/plugin marketplace add aws/agent-toolkit-for-aws
/plugin install aws-agents@agent-toolkit-for-aws
```
### Codex
Discovered automatically from the marketplace manifest.
## Prerequisites
- AgentCore CLI v0.9.0+ (`npm install -g @aws/agentcore`)
- AWS CLI with configured credentials
- Node.js 20+
- Python 3.11+ with `uv`
## Examples
- "How do I build an agent on AWS?"
- "My agent keeps forgetting what I told it"
- "Deploy is failing with a CDK error"
- "I want to call my deployed agent from my React app"
- "Restrict my agent from making purchases over $1000"
- "How do I know if my agent is good?"
- "How much will this cost me?"
- "We're going live next week, what should I check?"
- "I need to roll back to yesterday's version"
## License
Apache-2.0
plugins/aws-agents/skills/agents-build/SKILL.md
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---
name: agents-build
description: >
Use when adding capabilities to an existing agent project — memory,
app integration, VPC, multi-agent, migration, model changes, browser,
code interpreter, or resource removal. Triggers on: "add memory",
"remember across sessions", "call agent from app", "invoke agent from
code", "auth to call agent", "streaming responses", "VPC", "VPC
connectivity", "VPC error", "can't reach from VPC", "multi-agent",
"A2A", "A2A auth", "orchestrator not delegating", "specialist not
called", "migrate Bedrock Agent", "after import", "migration issue",
"framework for migration", "change model", "browser tool", "code
interpreter", "delete agent", "tear down", "agentcore remove",
"cross-account memory", "resource-based policy on memory", "pay for
x402 content", "402 Payment Required", "microtransactions", "paid API
or tool".
Not for connecting to external APIs via Gateway — use agents-connect.
Not for scaffolding a new project — use agents-get-started.
Not for CLI/dev server errors — use agents-debug.
Strands vs LangGraph in a migration context routes here.
allowed-tools: Read Grep Glob Bash
metadata:
type: skill
version: "1.0.0"
author: aws-agentcore
requires-cli: ">=0.9.0"
---
# build
Add capabilities to your AgentCore agent project.
## When to use
- Adding cross-session memory to your agent
- Calling your deployed agent from a web app, mobile app, or backend service
- Configuring VPC networking for private resources (RDS, internal APIs)
- Building multi-agent systems with orchestrator/specialist patterns
- Migrating an existing Bedrock Agent to AgentCore
- Adding the Browser tool so the agent can navigate websites
- Adding the Code Interpreter so the agent can execute code in a sandbox
- Adding AgentCore Payments so the agent can pay for x402-protected APIs, tools, or content
- Removing resources from your project or tearing down a deployment
Do NOT use for:
- Connecting to external tools/APIs via Gateway (OpenAPI specs, Lambda, MCP servers, credentials, policies) → use `agents-connect`
- Scaffolding a new project → use `agents-get-started`
- Deploying → use `agents-deploy`
## Input
`$ARGUMENTS` can be:
- A capability: "memory", "integrate", "vpc", "multi-agent", "migrate", "browser", "code-interpreter", "payments", "teardown"
- A description of what they want: "remember user preferences", "call from React app", "scrape a website", "run pandas in the agent", "delete my agent", "clean up resources"
- Empty — the skill will determine the workflow from context
## Process
### Step 0: Verify CLI version
Run `agentcore --version`. This skill requires v0.9.0 or later.
If older: "Run `agentcore update` to get the latest version."
### Step 1: Read project context
Read `agentcore/agentcore.json` to understand the current project — framework, existing resources, agent configuration.
If `agentcore/agentcore.json` is not found:
1. **Check if the developer is in the wrong directory.** Look for `agentcore/agentcore.json` in parent directories (up to 3 levels). If found, tell them: "Found an AgentCore project at `<path>`. Are you working in that project?"
2. **If no project exists anywhere nearby**, ask what capability they wanted to add. Then offer two paths:
- "I can walk you through creating a project first and then adding CAPABILITY — want to do that?" (run the get-started flow inline, then continue with the build workflow)
- "If you already have a project elsewhere, `cd` into it and try again."
Do not just say "go use agents-get-started" and stop — that loses the developer's context about what they actually wanted to do.
### Step 2: Determine the workflow
**Important disambiguation** — before routing to a build reference, check if the prompt is actually a connect or debug concern:
- If the phrase mentions external APIs, Lambda functions, OpenAPI specs, gateways, credentials, MCP servers, or policies → this is `agents-connect`, not build
- If the developer says something is broken (wrong answers, errors, tool failures) → this is `agents-debug`, not build
- Build is for **adding new capabilities** to a working project, not fixing broken ones
Based on the developer's prompt and `$ARGUMENTS`, load the appropriate reference:
| Developer intent | Reference to load |
|---|---|
| Add memory, remember things, user preferences, cross-session | [`references/memory.md`](references/memory.md) |
| Call agent from app, invoke from code, streaming, SDK client, agent URL, execute shell in session | [`references/integrate.md`](references/integrate.md) |
| VPC, private network, RDS, internal API, subnet, security group | [`references/vpc.md`](references/vpc.md) |
| Multi-agent, orchestrator, specialist, A2A, delegation, agent handoff | [`references/multi-agent.md`](references/multi-agent.md) |
| Custom headers from caller to agent, header allowlist, tenant ID/correlation ID/trace propagation | [`references/request-headers.md`](references/request-headers.md) |
| Migrate Bedrock Agent, import agent, move to AgentCore | [`references/migrate.md`](references/migrate.md) |
| Browser tool, web navigation, form filling, scraping, Nova Act, Playwright, live view | [`references/browser.md`](references/browser.md) |
| Code Interpreter, execute code, sandbox, run Python/JS/TS, data analysis in agent, pandas | [`references/code-interpreter.md`](references/code-interpreter.md) |
| Payments, pay for x402 content, 402 Payment Required, microtransactions, paid API/tool, payment manager/connector | [`references/payments.md`](references/payments.md) |
| Delete agent, remove resource, tear down, clean up, destroy, start fresh | [`references/teardown.md`](references/teardown.md) |
| Change model, switch model, use Haiku/Sonnet/Nova, different model | Inline — see "Changing the model" below |
If the developer asks about the difference between local dev and deployed (e.g., "why does my memory work after deploy but not locally?"), load [`references/local-vs-deployed.md`](references/local-vs-deployed.md) alongside the specific workflow reference.
Read the matching file into context and follow its Process section step by step — do not summarize.
If the intent is ambiguous, ask the developer which capability they want to add.
### Changing the model
The model is configured in `app/<AgentName>/model/load.py` (scaffolded by `agentcore create`). To change it:
1. Open `app/<AgentName>/model/load.py`
2. Change the `model_id` parameter in the `BedrockModel()` constructor
```python
# Default (scaffolded by CLI)
return BedrockModel(model_id="global.anthropic.claude-sonnet-4-5-20250929-v1:0")
# Switch to Haiku for cost savings
return BedrockModel(model_id="us.anthropic.claude-3-5-haiku-20241022-v1:0")
# Switch to Nova Lite
return BedrockModel(model_id="amazon.nova-lite-v1:0")
```
Cross-region inference profile prefixes (`us.`, `eu.`, `apac.`, `global.`) control where inference runs. Use `global.` for maximum throughput, or a geographic prefix for data residency. Not all models support all prefixes — check the Bedrock inference profiles docs.
After changing the model:
- Verify the model is enabled in your region: AWS Console → Amazon Bedrock → Model access
- For cross-region profiles, enable in all destination regions
- If using `agents-harden`, update the IAM policy to scope to the new model ARN
- Run `agentcore dev` to test locally, then `agentcore deploy` to update the deployed agent
No `agentcore.json` change is needed — the model is configured in code, not in the project config.
### Pre-flight: validate any `--name` before generating the CLI command
Whichever reference you load, most end up producing an `agentcore add <resource> --name <something>` command. The CLI fails **late** on invalid names — you'll see the error after walking through prompts, not before running the command. Validate up front:
| Resource | Max chars | Allowed | Starts with |
|---|---|---|---|
| Agent (`add agent`) | 48 | alphanumeric + `_` | letter |
| Memory, gateway, gateway-target, credential, evaluator, online-eval, policy, policy-engine, payment-manager, payment-connector | 48 | alphanumeric + `_` | letter |
Count the characters before constructing the command. If the name is over the limit or contains hyphens, dots, or spaces, push back: "`<name>` is N characters / uses `-`, which the CLI rejects. How about `<suggestion>`?" Never run the command with an invalid name hoping the CLI message will be clear.
Note: `agentcore create --name` (the project name) has a **stricter 23-char limit** and does not allow underscores. That's covered in `agents-get-started`; if you see the developer re-running create, flag the 23-char limit specifically.
## Output
Depends on the workflow — see the loaded reference for specific outputs.
## Quality criteria
- The correct reference was loaded based on the developer's intent
- All output follows the loaded reference's quality criteria
- Cross-references to other skills (agents-connect, agents-deploy) are included where relevant
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# browser
Add the AgentCore Browser tool so your agent can navigate web pages, fill forms, and extract information.
## When to use
- Your agent needs to interact with a website that has no API
- Your agent needs to fill forms, scrape data, or drive a web app
- You want an isolated, session-scoped browser for the agent (not a shared one)
- You want live-view / recording / replay of what the browser did, for debugging or auditing
Do NOT use this reference for:
- Calling an API — use Gateway (`agents-connect`)
- Running code in a sandbox — see [`code-interpreter.md`](code-interpreter.md)
- Serving browser-based UIs to users — that's a different problem (the AGUI protocol, not the Browser tool)
## Mental model
The Browser tool is a **managed Chrome instance**, one per session, running in an isolated microVM. Your agent connects to it over WebSocket (via CDP — Chrome DevTools Protocol) and drives it with an automation framework. You pick the framework:
| Framework | When to use |
|---|---|
| **Strands `AgentCoreBrowser`** | Agent-driven browsing inside a Strands agent. Highest-level, tool-use-native. |
| **Nova Act** | You want an LLM to decide the next action at each step ("click the search box, type X, press enter"). Best for open-ended tasks. |
| **Playwright** | Deterministic scripted automation. Best when you know the exact steps — login flows, scraping a known page structure. |
If you're adding browsing to a Strands agent, use `AgentCoreBrowser` and skip the framework decision — it wraps Nova Act under the hood and fits the agent-tool mental model.
If you're not using Strands, pick between Nova Act (reasoning-driven) and Playwright (script-driven) based on whether the task is open-ended or well-defined.
Sessions are **ephemeral by default** (reset after each use). Default timeout is 15 minutes, max 8 hours. You can run multiple concurrent sessions.
## Prerequisites
- Python 3.10+
- `bedrock-agentcore` SDK installed
- IAM permissions for `bedrock-agentcore:*Browser*` actions (scope to your browser resource ARN in production)
- AWS region that supports Browser — check the docs for the current list
- For Strands path: model access for your chosen model (Claude Sonnet 4.x is the common default)
- For Nova Act path: a Nova Act API key from [nova.amazon.com/act](https://nova.amazon.com/act) (US-based amazon.com accounts only at time of writing)
IAM policy skeleton (attach to the caller identity — your user, role, or AgentCore Runtime execution role):
```json
{
"Version": "2012-10-17",
"Statement": [{
"Sid": "BrowserAccess",
"Effect": "Allow",
"Action": [
"bedrock-agentcore:CreateBrowser",
"bedrock-agentcore:GetBrowser",
"bedrock-agentcore:ListBrowsers",
"bedrock-agentcore:StartBrowserSession",
"bedrock-agentcore:StopBrowserSession",
"bedrock-agentcore:GetBrowserSession",
"bedrock-agentcore:ListBrowserSessions",
"bedrock-agentcore:ConnectBrowserAutomationStream",
"bedrock-agentcore:ConnectBrowserLiveViewStream"
],
"Resource": "arn:aws:bedrock-agentcore:<REGION>:<ACCOUNT_ID>:browser/*"
}]
}
```
Check current IAM action names against the docs — the list evolves.
## Path A — Strands agent with the Browser tool (recommended for most)
```python
from strands import Agent
from strands_tools.browser import AgentCoreBrowser
browser_tool = AgentCoreBrowser(region="<REGION>")
agent = Agent(tools=[browser_tool.browser])
result = agent("Find the release date of the latest AgentCore SDK on GitHub.")
print(result.message["content"][0]["text"])
```
Install: `pip install bedrock-agentcore strands-agents strands-agents-tools`
The agent decides when to use the browser, opens sessions on demand, and cleans them up. Under the hood, `AgentCoreBrowser` uses the AWS-managed `aws.browser.v1` resource — no resource creation needed.
**Dropping into an AgentCore Runtime entrypoint:**
```python
from bedrock_agentcore.runtime import BedrockAgentCoreApp
from strands import Agent
from strands_tools.browser import AgentCoreBrowser
from model.load import load_model # scaffolded by `agentcore create`
import os
app = BedrockAgentCoreApp()
REGION = os.getenv("AWS_REGION", "us-west-2")
@app.entrypoint
def invoke(payload, context):
browser_tool = AgentCoreBrowser(region=REGION)
agent = Agent(model=load_model(), tools=[browser_tool.browser])
result = agent(payload.get("prompt", ""))
return {"response": str(result)}
if __name__ == "__main__":
app.run()
```
## Path B — Nova Act for reasoning-driven tasks
Use when the task needs an LLM to decide each click/type step.
```python
from bedrock_agentcore.tools.browser_client import browser_session
from nova_act import NovaAct
def run_browser_task(prompt: str, starting_page: str, nova_act_key: str, region: str = "us-west-2"):
with browser_session(region) as client:
ws_url, headers = client.generate_ws_headers()
with NovaAct(
cdp_endpoint_url=ws_url,
cdp_headers=headers,
nova_act_api_key=nova_act_key,
starting_page=starting_page,
) as nova:
return nova.act(prompt)
```
Install: `pip install bedrock-agentcore nova-act boto3`
The `browser_session` context manager handles start/stop. Do not leak sessions — always use the context manager or wrap raw `BrowserClient` calls in try/finally.
**Credential handling:** the Nova Act API key is a secret. If this is running inside an AgentCore Runtime agent, register it as a credential (`agentcore add credential --name NovaAct --api-key ...`) and retrieve it with `@requires_api_key(provider_name="NovaAct")`. Do not put it in runtime env vars. See `agents-connect` Path D.
## Path C — Playwright for scripted automation
Use when the steps are fixed and you want deterministic behavior (logins, scrapes, automated tests).
```python
import asyncio
from bedrock_agentcore.tools.browser_client import browser_session
from playwright.async_api import async_playwright
async def scrape_title(url: str, region: str = "us-west-2") -> str:
async with async_playwright() as pw:
with browser_session(region) as client:
ws_url, headers = client.generate_ws_headers()
browser = await pw.chromium.connect_over_cdp(ws_url, headers=headers)
context = browser.contexts[0]
page = context.pages[0]
try:
await page.goto(url)
return await page.title()
finally:
await page.close()
await browser.close()
print(asyncio.run(scrape_title("https://example.com")))
```
Install: `pip install bedrock-agentcore playwright`
Sync variant (`sync_playwright`) is also supported — pick based on whether your agent code is async.
## Observability
Browser is observable by default:
- **Live view** — watch a running session in real time from the AWS console (Built-in tools → Browser → your session → "View live session"). You can take over control from the automation interactively.
- **CloudWatch logs** — `/aws/bedrock-agentcore/browser/*`
- **Metrics** — in `AWS/BedrockAgentCore` namespace
**Session recording** (DOM, clicks, console logs, network) is opt-in per browser. To enable:
1. Create a **custom browser** (not `aws.browser.v1`) with recording configured
2. Give its execution role `s3:PutObject` on your recording bucket
3. Recordings land in your S3 bucket and replay in the AWS console
The managed `aws.browser.v1` resource does **not** record. Use custom browsers when you need audit trails.
## Session lifecycle — always close
```python
# Right — context manager
with browser_session(region) as client:
ws_url, headers = client.generate_ws_headers()
...
# Also right — explicit try/finally
client = BrowserClient(region=region)
client.start()
try:
...
finally:
client.stop()
# Wrong — leaked session
client = BrowserClient(region=region)
client.start()
... # if this raises, the session sits idle until its 15-minute timeout
```
Sessions hold a microVM. Leaked sessions cost money until they time out. The context manager is non-negotiable for production.
## VPC mode
If your agent runs in VPC mode, the Browser tool can also run in VPC. See [`vpc.md`](vpc.md) for the subnet + security group pattern (the same service-linked role covers Browser ENIs). Browser in VPC requires a NAT gateway for public-internet sites — public subnets don't give Browser internet access.
## Common failures
**"Access denied" starting a session:** IAM is missing `StartBrowserSession` on the browser resource ARN. Check `aws sts get-caller-identity` matches the identity you attached the policy to.
**"Model access denied" from a Strands agent:** The browser tool itself is fine, but the agent's model isn't enabled. Go to Bedrock console → Model access → enable your model in the region.
**Nova Act errors about API key:** The key is US-amazon.com-accounts only at launch. If you're outside the US or using a work account, you can't use Nova Act yet — fall back to Playwright or Strands.
**Browser session times out mid-task:** Default is 15 minutes of idle time. Pass `sessionTimeoutSeconds` to `StartBrowserSession` (max 28800 = 8 hours). Don't use this to cover up agents that are slow — fix the agent or chunk the work.
**Live view doesn't show your session:** Live view requires `ConnectBrowserLiveViewStream` IAM permission. The session also has to be `Ready`, not `Starting` or `Stopping`.
## Output
- Which framework fits (Strands vs Nova Act vs Playwright)
- Working code with session lifecycle handled
- IAM policy scoped to the browser resource
- Observability setup if needed (live view, recording)
## Quality criteria
- Browser sessions are always wrapped in a context manager or try/finally — never leaked
- IAM is scoped to `browser/*` in the account, not `Resource: "*"`
- Nova Act API keys and other secrets use `agentcore add credential` + `@requires_api_key`, not env vars
- The code handles the case where the agent runs outside AgentCore Runtime (no `.env.local`, no credential provider) — typically by reading a local secret for development and the credential provider for production
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# code-interpreter
Add the AgentCore Code Interpreter tool so your agent can execute code in a sandboxed environment — Python, JavaScript, or TypeScript.
## When to use
- Your agent needs to run math, data analysis, or transform data where a calculation is more reliable than an LLM answer
- Your agent generates code as an answer and you want it executed (and its output verified) before returning
- Your agent needs to read/write files (CSV, JSON, plots) that should persist to S3
- You need an isolated, session-scoped code sandbox
Do NOT use this reference for:
- Interacting with web pages — see [`browser.md`](browser.md)
- Running arbitrary long-lived services — Code Interpreter is for short-lived code execution, not hosting servers
- Shell commands *inside your live agent session's own microVM* — that's `InvokeAgentRuntimeCommand`, covered in [`integrate.md`](integrate.md)
## Mental model
Code Interpreter is a **managed sandbox**, one per session, running in an isolated microVM. Your code can:
- Execute Python, JavaScript, or TypeScript
- Read/write files on a local filesystem (up to 100 MB inline upload, up to 5 GB via S3)
- Make network calls (if internet access is enabled on the resource)
- Use pre-installed libraries (pandas, numpy, scikit-learn, torch, etc. — see docs for the current list)
Sessions are **stateful within a session** (variables and files persist across `execute_code` calls in the same session) and **ephemeral across sessions** (start a new session and the filesystem is clean).
## Prerequisites
- Python 3.10+ in your agent environment
- `bedrock-agentcore` SDK
- IAM permissions for `bedrock-agentcore:*CodeInterpreter*` actions, scoped to the resource ARN
- Model access if calling via an agent framework (the framework calls a model to decide when to execute code)
IAM policy skeleton:
```json
{
"Version": "2012-10-17",
"Statement": [{
"Sid": "CodeInterpreterAccess",
"Effect": "Allow",
"Action": [
"bedrock-agentcore:CreateCodeInterpreter",
"bedrock-agentcore:GetCodeInterpreter",
"bedrock-agentcore:ListCodeInterpreters",
"bedrock-agentcore:StartCodeInterpreterSession",
"bedrock-agentcore:StopCodeInterpreterSession",
"bedrock-agentcore:InvokeCodeInterpreter",
"bedrock-agentcore:GetCodeInterpreterSession",
"bedrock-agentcore:ListCodeInterpreterSessions"
],
"Resource": "arn:aws:bedrock-agentcore:<REGION>:<ACCOUNT_ID>:code-interpreter/*"
}]
}
```
Check current action names against the docs — the list evolves.
## Path A — Strands agent with Code Interpreter (recommended for most)
```python
from strands import Agent
from strands_tools.code_interpreter import AgentCoreCodeInterpreter
tool = AgentCoreCodeInterpreter(region="<REGION>")
agent = Agent(
tools=[tool.code_interpreter],
system_prompt=(
"You are an assistant that validates claims with code. "
"When asked to compute, calculate, or analyze, write Python and run it."
),
)
result = agent("What are the first 10 Fibonacci numbers?")
print(result.message["content"][0]["text"])
```
Install: `pip install bedrock-agentcore strands-agents strands-agents-tools`
The agent decides when to execute code, starts sessions on demand, and stops them. Under the hood, the tool uses the AWS-managed `aws.codeinterpreter.v1` resource — no resource creation needed.
**Dropping into an AgentCore Runtime entrypoint:**
```python
from bedrock_agentcore.runtime import BedrockAgentCoreApp
from strands import Agent
from strands_tools.code_interpreter import AgentCoreCodeInterpreter
from model.load import load_model
import os
app = BedrockAgentCoreApp()
REGION = os.getenv("AWS_REGION", "us-east-1")
@app.entrypoint
def invoke(payload, context):
tool = AgentCoreCodeInterpreter(region=REGION)
agent = Agent(
model=load_model(),
tools=[tool.code_interpreter],
system_prompt="Validate computations with code.",
)
return {"response": str(agent(payload.get("prompt", "")))}
if __name__ == "__main__":
app.run()
```
## Path B — Direct SDK for programmatic execution
Use when your code — not an agent — decides what to run. Good for ETL, data transformation, and agent-internal validation.
```python
from bedrock_agentcore.tools.code_interpreter_client import code_interpreter_session
REGION = "us-east-1"
with code_interpreter_session(REGION) as session:
# Stateful: variables persist across calls within the session
session.execute_code("import pandas as pd")
session.execute_code("df = pd.DataFrame({'x': [1, 2, 3]})")
result = session.execute_code("df.describe().to_string()")
print(result.stdout)
```
The context manager handles start/stop. Do not leak sessions.
**Language selection** — default is Python. For JavaScript/TypeScript, pass `language="javascript"` or `language="typescript"` to `execute_code` (or the runtime setting at session start). See the runtime selection doc for the current supported runtimes.
## Path C — Custom Code Interpreter with S3 access
The managed `aws.codeinterpreter.v1` resource has no S3 write permissions. For agents that produce artifacts (plots, reports, processed datasets) you want to persist, create a **custom Code Interpreter** with an execution role that has S3 access.
This is a CreateCodeInterpreter call (SDK/API, not exposed via `agentcore` CLI at time of writing). The execution role's trust policy grants `bedrock-agentcore.amazonaws.com` the ability to assume it, and its permissions policy grants `s3:PutObject` and related actions on your artifact bucket. Check the docs for the current `CreateCodeInterpreter` shape and the exact trust policy format.
**Same-account S3 rule.** The S3 bucket must be in the **same AWS account** as the Code Interpreter resource. Cross-account buckets are not supported as targets even with the right bucket policy — `CreateCodeInterpreter` fails with a validation error. If you need the artifacts in another account, replicate from the same-account bucket afterward.
## Observability
- **CloudWatch logs** — stdout/stderr from executed code, plus session lifecycle events
- **CloudTrail** — every `StartCodeInterpreterSession`, `InvokeCodeInterpreter`, `StopCodeInterpreterSession` call
- **Metrics** — in `AWS/BedrockAgentCore` namespace
## Pre-installed libraries
The managed Python runtime includes: `pandas`, `numpy`, `scipy`, `matplotlib`, `plotly`, `scikit-learn`, `torch`, `torchvision`, `statsmodels`, and dozens more for data analysis / ML. Check the current list in the docs before telling a user "library X is preinstalled" — the list changes with platform updates.
For libraries not preinstalled, call `install_packages(["your-lib==1.2"])` in your session (or `!pip install ...` via `execute_command`). Installed packages last only for the session.
## Session lifecycle — always close
```python
# Right — context manager
with code_interpreter_session(region) as session:
session.execute_code("...")
# Right — try/finally with explicit client
client = CodeInterpreterClient(region=region)
client.start()
try:
client.execute_code("...")
finally:
client.stop()
# Wrong — leaked session sits until timeout
```
Default session timeout is 900 seconds (15 min), max 28800 seconds (8 hours). Leaked sessions cost money.
## VPC mode
Code Interpreter supports VPC — same pattern as Runtime and Browser (service-linked role, your subnets, your security group). See [`vpc.md`](vpc.md).
**Public internet from the sandbox** requires a NAT gateway on a private subnet, same as Runtime. Public subnets don't give Code Interpreter ENIs internet access. If the code needs `pip install` to reach PyPI, plan for NAT.
## Common failures
**"Access denied" on StartCodeInterpreterSession:** IAM missing the action on the resource ARN. Use `aws sts get-caller-identity` to confirm which identity you attached the policy to.
**"ValidationException: Role does not have access to required S3 buckets":** S3 bucket is in a different account. Move the bucket or replicate from an in-account staging bucket.
**Code times out:** Default execute timeout is short. Split long jobs into chunks, or use a custom Code Interpreter with extended timeouts. Don't try to run 30-minute training jobs in Code Interpreter — that's a SageMaker / Batch job.
**"Module not found" despite being listed as preinstalled:** The preinstalled list may differ between `python` and `nodejs` runtimes. Verify runtime selection and list matches.
## Output
- Which path fits (Strands tool vs direct SDK vs custom with S3)
- Working code with session lifecycle handled
- IAM policy scoped to the code-interpreter resource
## Quality criteria
- Sessions are always wrapped in a context manager or try/finally — never leaked
- IAM is scoped to `code-interpreter/*` in the account, not `Resource: "*"`
- S3 destination buckets are in the same account as the Code Interpreter resource
- Language / runtime selection is explicit when the code isn't Python
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# integrate
Help a developer call their deployed agent from an application.
## When to use
- Developer has a deployed agent and wants to call it from their app
- Developer needs the agent URL and auth credentials
- Developer wants to handle streaming responses from the agent
- Developer needs to manage conversation sessions across multiple calls
- Developer is building a frontend, backend service, or CLI that consumes the agent
- Caller and agent are in different AWS accounts (cross-account invocation)
Do NOT use for:
- Giving the agent tools to call external APIs → use `agents-connect`
- Deploying the agent → use `agents-deploy`
- Debugging agent responses → use `agents-debug`
- Securing the agent endpoint for production → use `agents-harden` (but this skill covers the client-side auth code)
## Input
`$ARGUMENTS` can be:
- A language or framework: "from React", "in Python", "Node.js backend"
- An auth preference: "using IAM", "with JWT"
- Empty — the skill will detect the project context and guide accordingly
## Process
### Step 1: Check deployment status
Read `agentcore/agentcore.json` to get the agent name. Then check if it's deployed:
```bash
agentcore status --type agent
```
**If not deployed:** "Your agent needs to be deployed before you can call it from an app. Run `agentcore deploy` first, or use the `agents-deploy` skill for guidance."
Do not proceed until the agent is deployed.
### Step 2: Get the agent endpoint
```bash
agentcore fetch access --name <AgentName> --type agent
```
This returns:
- **Agent Runtime ARN** — needed for SDK invocation
- **Endpoint URL** — for direct HTTPS calls
- **Auth configuration** — what auth method is configured
Note the auth type from the output. It determines how the client app authenticates.
### Step 3: Determine auth method
Read the agent's `authorizerType` field from `agentcore/agentcore.json` (it's a top-level field on the runtime entry; JWT details live in the separate `authorizerConfiguration` object on the same runtime).
| Auth type | How the client authenticates | Best for |
|---|---|---|
| **None** (default) | IAM SigV4 signing on the request | Backend services with AWS credentials |
| **AWS_IAM** | IAM SigV4 signing on the request | Backend services, Lambda-to-agent calls |
| **CUSTOM_JWT** | Bearer token in Authorization header | Web/mobile apps with an identity provider |
**If no authorizer is configured:** The agent uses IAM auth by default. The calling identity needs `bedrock-agentcore:InvokeAgentRuntime` permission.
**If CUSTOM_JWT:** The client sends a JWT from the configured identity provider. The agent validates it against the discovery URL, allowed audience, and allowed clients configured during setup.
### Step 4: Generate client code
Based on the developer's language preference (from `$ARGUMENTS` or ask), generate the appropriate client code.
#### Python (boto3) — IAM auth
```python
import boto3
import json
from botocore.exceptions import ClientError
client = boto3.client("bedrock-agentcore", region_name="<REGION>")
try:
response = client.invoke_agent_runtime(
agentRuntimeArn="<AGENT_RUNTIME_ARN>",
qualifier="DEFAULT", # or a specific version number
payload=json.dumps({
"prompt": "Hello, what can you do?"
}).encode(),
runtimeSessionId="session-123", # reuse for multi-turn conversations
)
# Handle streaming response — response["response"] is a StreamingBody
stream = response["response"]
if hasattr(stream, "iter_lines"):
for line in stream.iter_lines():
if line:
print(line.decode(), end="", flush=True)
else:
# Some SDK versions return raw bytes — read all at once
content = stream.read()
print(content.decode() if isinstance(content, bytes) else content)
except ClientError as e:
code = e.response["Error"]["Code"]
if code == "AccessDeniedException":
# Missing bedrock-agentcore:InvokeAgentRuntime permission
raise RuntimeError("Caller lacks InvokeAgentRuntime permission") from e
elif code == "ValidationException":
# Wrong ARN, bad payload format, invalid session ID
raise RuntimeError(f"Invalid request: {e}") from e
elif code == "ThrottlingException":
# Retry with exponential backoff
raise
else:
raise
```
#### Python (HTTPS) — JWT auth
```python
import requests
AGENT_URL = "<ENDPOINT_URL>"
JWT_TOKEN = "<TOKEN_FROM_YOUR_IDP>"
response = requests.post(
AGENT_URL,
headers={
"Authorization": f"Bearer {JWT_TOKEN}",
"Content-Type": "application/json",
},
json={"prompt": "Hello, what can you do?"},
stream=True,
)
for chunk in response.iter_content(chunk_size=None):
print(chunk.decode(), end="", flush=True)
```
#### JavaScript/TypeScript (AWS SDK) — IAM auth
```typescript
import {
BedrockAgentCoreClient,
InvokeAgentRuntimeCommand,
} from "@aws-sdk/client-bedrock-agentcore";
const client = new BedrockAgentCoreClient({ region: "<REGION>" });
const response = await client.send(
new InvokeAgentRuntimeCommand({
agentRuntimeArn: "<AGENT_RUNTIME_ARN>",
qualifier: "DEFAULT",
payload: new TextEncoder().encode(
JSON.stringify({ prompt: "Hello, what can you do?" })
),
runtimeSessionId: "session-123",
})
);
// response.response is the streaming body
const decoder = new TextDecoder();
for await (const chunk of response.response) {
process.stdout.write(decoder.decode(chunk));
}
```
#### JavaScript/TypeScript (fetch) — JWT auth
```typescript
const AGENT_URL = "<ENDPOINT_URL>";
const JWT_TOKEN = "<TOKEN_FROM_YOUR_IDP>";
const response = await fetch(AGENT_URL, {
method: "POST",
headers: {
Authorization: `Bearer ${JWT_TOKEN}`,
"Content-Type": "application/json",
},
body: JSON.stringify({ prompt: "Hello, what can you do?" }),
});
const reader = response.body.getReader();
const decoder = new TextDecoder();
while (true) {
const { done, value } = await reader.read();
if (done) break;
process.stdout.write(decoder.decode(value));
}
```
### Step 5: Session management
Explain how sessions work:
- **`runtimeSessionId`** — pass the same value across multiple calls to maintain conversation context
- Generate a unique session ID per user conversation (e.g., UUID)
- Sessions are server-side — the agent remembers the conversation history for that session ID
- If you omit the session ID, each call is stateless (no conversation memory)
```python
import uuid
# New conversation
session_id = str(uuid.uuid4())
# First turn
invoke(session_id, "What's the weather in Seattle?")
# Follow-up in same conversation
invoke(session_id, "What about tomorrow?")
# New conversation — new session
new_session_id = str(uuid.uuid4())
invoke(new_session_id, "Different topic entirely")
```
### Step 6: Protocol-specific guidance
Read the agent's `protocol` from `agentcore/agentcore.json`.
**If HTTP (default):** The patterns above apply directly.
**If MCP:** The agent exposes an MCP endpoint. Clients connect using the MCP protocol (Streamable HTTP). Point the developer to MCP client libraries for their language.
**If A2A:** The agent exposes an Agent-to-Agent endpoint with a card at `/.well-known/agent-card.json`. The calling agent discovers capabilities via the card and communicates over JSON-RPC 2.0. See [`references/multi-agent.md`](multi-agent.md) in this skill for A2A integration patterns.
### Step 7: Integration patterns that look right but fail
Two patterns come up often enough in support cases to call out directly.
**API Gateway `/{proxy+}` with a URL-encoded Runtime ARN.** Fronting AgentCore Runtime with an API Gateway REST API whose resource is `/{proxy+}` and whose integration URI is the encoded runtime ARN appears to work — the deploy succeeds and short requests return. Longer requests fail at around 2 minutes with `Integration closed connection prematurely` in the logs, regardless of `integrationTimeoutInMillis`. `HTTP_PROXY` is a generic forwarding integration; it doesn't handle SigV4, streaming, or session semantics the way the SDK client does.
Use one of these instead:
- Call Runtime directly from the client with the `bedrock-agentcore` SDK (Step 4 above). This is the intended path.
- Put a Lambda between API Gateway and Runtime if you need API Gateway for rate limiting, a public HTTPS endpoint, or other reasons. The Lambda receives the request, calls `invoke_agent_runtime`, and streams the response back. The Lambda's execution role needs `bedrock-agentcore:InvokeAgentRuntime`. Be aware that API Gateway has a 29-second hard ceiling on synchronous responses — this works only for fast agents. For anything multi-step, use the direct SDK path instead.
**Lambda-in-front for synchronous agent responses hits a short timeout ceiling.** A `Client → API Gateway → Lambda → Runtime` chain caps at ~29 seconds because of the API Gateway synchronous response limit. Any agent that reasons, calls multiple tools, or uses a non-trivial model will exceed it. If you're hitting timeouts on a Lambda wrapping Runtime, the fix is usually to drop the Lambda and let the client call Runtime directly — Runtime supports streaming responses natively, which is typically the reason teams add a Lambda in the first place.
### Step 8: Cross-account invocation
Calling an agent in a different AWS account than your caller uses standard AWS cross-account IAM patterns — no AgentCore-specific plumbing. The caller account assumes a role in the agent's account, gets temporary credentials, and uses them to sign the invoke request.
**Setup in the agent's account (Account B):**
Create an IAM role that trusts the caller account and has permission to invoke the runtime.
```json
// Trust policy — who can assume this role
{
"Version": "2012-10-17",
"Statement": [{
"Effect": "Allow",
"Principal": {"AWS": "arn:aws:iam::<CALLER_ACCOUNT_ID>:root"},
"Action": "sts:AssumeRole",
"Condition": {
"StringEquals": {"sts:ExternalId": "<unique-external-id>"}
}
}]
}
```
```json
// Permissions policy — what this role can do
{
"Version": "2012-10-17",
"Statement": [{
"Effect": "Allow",
"Action": "bedrock-agentcore:InvokeAgentRuntime",
"Resource": "arn:aws:bedrock-agentcore:<REGION>:<AGENT_ACCOUNT_ID>:runtime/<RUNTIME_NAME>-*"
}]
}
```
Scope the `Principal` in the trust policy as narrowly as possible (a specific role ARN in the caller account rather than `:root` for anything beyond proof-of-concept). Use an `ExternalId` to prevent the confused deputy problem.
**In the caller's app (Account A):**
```python
import boto3
import json
# Assume the role in Account B
sts = boto3.client("sts")
assumed = sts.assume_role(
RoleArn="arn:aws:iam::<AGENT_ACCOUNT_ID>:role/<ROLE_NAME>",
RoleSessionName="agent-invoker",
ExternalId="<unique-external-id>",
)
creds = assumed["Credentials"]
# Use the temporary credentials to invoke the runtime
agentcore = boto3.client(
"bedrock-agentcore",
region_name="<REGION>",
aws_access_key_id=creds["AccessKeyId"],
aws_secret_access_key=creds["SecretAccessKey"],
aws_session_token=creds["SessionToken"],
)
response = agentcore.invoke_agent_runtime(
agentRuntimeArn="arn:aws:bedrock-agentcore:<REGION>:<AGENT_ACCOUNT_ID>:runtime/<RUNTIME_NAME>",
qualifier="DEFAULT",
payload=json.dumps({"prompt": "hello"}).encode(),
runtimeSessionId="session-123",
)
```
**Production notes:**
- Cache the assumed-role credentials. They're valid for the session duration (default 1 hour). Re-assume when they're close to expiring, not on every request.
- Boto3's `Session` with a profile using `role_arn` and `source_profile` can automate this if your caller environment supports AWS config profiles. `assume_role` in code is the explicit version.
- If the caller is in a Lambda, ECS task, or EC2 instance, the execution/task role is what gets the AssumeRole permission. That role's trust policy is what gets listed in Account B's trust policy.
- The runtime's own resource policy (if any) is separate from IAM. Typically you don't need a resource policy for cross-account — the IAM role in Account B is what grants access.
## Running shell commands inside a live agent session (`InvokeAgentRuntimeCommand`)
Once an agent's session is running, you can execute shell commands inside that **same session's microVM** — same filesystem, same env, same network namespace — and stream the output back. This sits alongside `InvokeAgentRuntime` (which drives the agent's reasoning loop), not in place of it.
When this is useful:
- Coding/devops agents where your app runs deterministic ops (git pull, build, test, file system inspection) instead of asking the LLM to reason about them
- Seeding the session's filesystem before the agent runs (drop a dataset into `/tmp`, then invoke the agent to analyze it)
- Debugging a stuck or misbehaving session — run `ps`, `ls`, `cat /tmp/log` from outside without going through the agent
- Any workflow where you want the reliability of a scripted command and the context of a warm session
When it's the wrong tool:
- Spawning new sessions to run arbitrary code for users — use the [`code-interpreter.md`](code-interpreter.md) built-in tool instead; it's purpose-built, sandboxed differently, and doesn't consume an agent's session
- Running anything an unrelated caller shouldn't be able to do — commands execute with the runtime's execution role and filesystem
**IAM permission required:** `bedrock-agentcore:InvokeAgentRuntimeCommand` on the runtime ARN. This is a **separate** action from `InvokeAgentRuntime` — scope it explicitly to the callers who need it.
```python
import boto3
client = boto3.client("bedrock-agentcore", region_name="<REGION>")
response = client.invoke_agent_runtime_command(
agentRuntimeArn="<AGENT_RUNTIME_ARN>",
qualifier="DEFAULT",
runtimeSessionId="session-123", # must be an existing session
command="ls -la /tmp && cat /tmp/status.json",
)
# Output streams back over HTTP/2 on response["response"]
for chunk in response["response"].iter_chunks():
print(chunk.decode(), end="", flush=True)
```
**Session must exist.** `InvokeAgentRuntimeCommand` attaches to a running session; it won't create one. If the session has expired or never existed, the call fails. Invoke the agent first (to start the session), then use the session ID for subsequent command calls.
**Same microVM, same filesystem.** A file written by the command is visible to the agent on the next invoke, and vice versa. Use this to pre-load artifacts, then reason over them in the agent. Session isolation still applies — other sessions cannot see these files.
> [!WARNING]
> InvokeAgentRuntimeCommand executes arbitrary shell commands inside a live agent
> session with the runtime's full execution role. Never grant
> bedrock-agentcore:InvokeAgentRuntimeCommand to the same principals that have
> bedrock-agentcore:InvokeAgentRuntime unless they explicitly need shell access.
> Always create a separate IAM policy for command execution. Always enable CloudTrail
> logging for InvokeAgentRuntimeCommand calls. If commands are constructed from
> user-supplied input, validate and sanitize — this is a command injection surface.
**IAM separation:** `InvokeAgentRuntimeCommand` is a distinct IAM action from `InvokeAgentRuntime`. Grant it only to the callers that need shell access — not to every identity that can invoke the agent. Minimal example:
```json
{
"Effect": "Allow",
"Action": "bedrock-agentcore:InvokeAgentRuntimeCommand",
"Resource": "arn:aws:bedrock-agentcore:<REGION>:<YOUR_ACCOUNT_ID>:runtime/<RUNTIME_NAME>-*"
}
```
Keep this in a separate IAM policy from the one that grants `InvokeAgentRuntime`. Attach it only to roles that explicitly need to run commands inside agent sessions.
**Command injection:** The code example above uses a hardcoded command string — intentionally. If your real usage constructs commands from user-supplied input, validate before passing: reject strings containing `&&`, `;`, `$(...)`, backticks, `|`, or other shell metacharacters. Passing unsanitized user input to `InvokeAgentRuntimeCommand` is a direct code execution vulnerability.
**CloudTrail monitoring:** Enable an EventBridge rule to alert on unexpected `InvokeAgentRuntimeCommand` calls:
```bash
aws events put-rule \
--name AgentCoreCommandExecution \
--event-pattern '{"source":["aws.bedrock-agentcore"],"detail-type":["AWS API Call via CloudTrail"],"detail":{"eventName":["InvokeAgentRuntimeCommand"]}}' \
--state ENABLED
```
A compromised caller with this permission can read/write the agent's filesystem, reach any network resource the agent can reach, and use the execution role's credentials — CloudTrail logging is the minimum detection baseline.
## Reference integrations
Two common integration targets have published, reusable patterns you can start from instead of building the integration layer yourself.
**Slack.** [Integrating Amazon Bedrock AgentCore with Slack](https://aws.amazon.com/blogs/machine-learning/integrating-amazon-bedrock-agentcore-with-slack/) walks through a reusable integration layer that brings any AgentCore agent into a Slack workspace. The architecture (API Gateway → Lambda → SQS → AgentCore) handles Slack's 3-second webhook timeout via asynchronous processing: one Lambda validates the Slack signature and returns immediately, another posts a "Processing..." placeholder, and a third invokes the agent and replaces the placeholder with the real response. The pattern maps Slack thread timestamps to AgentCore Memory session IDs and Slack user IDs to actor IDs, so conversation context persists in the same thread over time. The integration layer is decoupled from the agent — you swap in any agent (FinOps, DevOps, incident response) without touching the Slack infrastructure. Deploys with one `cdk deploy`.
**Microsoft Teams.** The same async-processing architecture (API Gateway → Lambda → queue → AgentCore) applies to Teams. See [How Amazon Bedrock transforms Microsoft Teams conversations into actionable insights](https://aws.amazon.com/blogs/industries/how-amazon-bedrock-transforms-microsoft-teams-conversations-into-actionable-insights/) for Teams-specific setup (Bot Framework registration, bot channel configuration). If you've already built the Slack pattern above, the Teams version is primarily a different webhook validator and response formatter.
Both patterns handle the "webhook platform with short timeout" problem in the same way — the chat platform gets an immediate ack and a placeholder, the real agent call happens asynchronously, and the response replaces the placeholder when ready. If you're integrating a third chat platform not listed here, use either blog as a template.
## Output
- The agent's endpoint URL and ARN
- Auth method explanation with client-side code
- Working client code in the developer's preferred language
- Session management guidance
- Protocol-specific notes if applicable
## Quality criteria
- Client code uses the correct SDK client (`bedrock-agentcore`, not `bedrock-agent`)
- Auth method matches what's configured on the agent
- Streaming response handling is included (not just request/response)
- Session ID pattern is explained
- Code is complete and runnable — includes imports, error handling basics
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# Local vs. Deployed — What Works Where
AgentCore has a local dev server (`agentcore dev`) and a deployed runtime. They don't have feature parity. This reference tells you what works where so generated code and troubleshooting handle both environments correctly.
## Quick reference
| Feature | `agentcore dev` (local) | Deployed runtime |
|---|---|---|
| Agent invocation | ✅ via curl on localhost:8080 | ✅ via `invoke_agent_runtime` or HTTPS |
| Framework model calls | ✅ if Bedrock creds are available | ✅ |
| Python/JS function tools (framework-native) | ✅ | ✅ |
| Credentials (`@requires_api_key`, `@requires_access_token`) | ✅ from `agentcore/.env.local` | ✅ from Secrets Manager |
| Memory | ❌ env var not set locally | ✅ `MEMORY_<NAME>_ID` injected |
| Gateway | ❌ env var not set locally | ✅ `AGENTCORE_GATEWAY_<NAME>_URL` injected |
| Cedar policy evaluation | ❌ policies only enforced at gateway | ✅ |
| Traces (X-Ray) | ✅ `agentcore dev` emits OTEL to CloudWatch by default; disable with `--no-traces` | ✅ auto-enabled |
| CloudWatch logs | ✅ via ADOT / OTEL wiring (same path as traces) | ✅ if using `logging` module + OTEL |
| **Evaluator *definition*** (`agentcore add evaluator`, writing the instructions/code) | ✅ — writes to `agentcore.json`; custom code is unit-testable locally | ✅ |
| **`agentcore run eval`** (on-demand eval over traces) | ✅ — operates on CloudWatch spans; local-dev spans land there if OTEL is on (default) | ✅ |
| **`Evaluate` API with hand-constructed spans** (boto3) | ✅ — no runtime needed at all; submit `SessionSpans` directly | ✅ |
| **Dataset runner** (`OnDemandEvaluationDatasetRunner`) | ❌ invokes an AgentCore Runtime agent in its pipeline | ✅ |
| **Online eval monitoring** (`agentcore add online-eval`) | ❌ ingests traces continuously from deployed runtime | ✅ |
| Observability dashboards | ✅ once Transaction Search is on and local spans are flowing | ✅ in CloudWatch console |
| VPC networking | ❌ local always has internet | ✅ subject to `networkMode: VPC` |
| Inbound auth (AWS_IAM, CUSTOM_JWT) | ❌ no auth required locally | ✅ enforced on every request |
## Implications for generated code
**Always guard features that aren't available locally:**
```python
# Memory pattern
MEMORY_ID = os.getenv("MEMORY_MYMEMORY_ID")
if MEMORY_ID:
# deployed — wire up memory
session_manager = AgentCoreMemorySessionManager(...)
else:
# local — agent runs without memory
session_manager = None
```
```python
# Gateway pattern
GATEWAY_URL = os.getenv("AGENTCORE_GATEWAY_WEATHER_URL")
if GATEWAY_URL:
# deployed — use gateway tools
tools = get_gateway_tools(GATEWAY_URL)
else:
# local — agent runs without external tools or with local stubs
tools = []
```
**Credentials work in both, but read from different sources.** The `@requires_api_key` decorator handles this automatically — don't try to read env vars directly.
## Testing workflow
Because memory, gateway, and policies don't work locally, the realistic test loop is:
1. **Local:** `agentcore dev` to verify the agent's code structure, framework wiring, system prompt, and any in-code logic
2. **Deploy to a staging target:** `agentcore deploy --target staging` to test with real memory, gateway, and policies
3. **Production:** only after staging validation
Don't expect `agentcore dev` to reproduce a production failure involving memory recall, gateway tool calls, or policy denials — those require a deployed environment.
## Common "works locally, fails deployed" causes
- Missing `MEMORY_<NAME>_ID` guard — code crashes because the env var is unexpectedly present
- Hardcoded localhost URLs for gateway — replace with `AGENTCORE_GATEWAY_<NAME>_URL`
- IAM permissions that work for your dev credentials but not the execution role
- Region mismatch between `aws configure` (used locally) and `aws-targets.json` (used in deploy)
- Tool call auth that works with your personal credentials but not with gateway SigV4 from the execution role
## Common "works deployed, fails locally" causes
- Code that assumes memory/gateway env vars are always set
- Direct SDK calls that expect the deployed execution role's permissions
- Hardcoded deployed-only URLs or ARNs
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# memory
Add, configure, and debug AgentCore Memory — the managed service that lets your agent remember things across sessions.
## When to use
- You want your agent to remember user preferences, facts, or conversation history across separate sessions
- You added memory via `agentcore create` or `agentcore add memory` and need to wire it into your agent code
- Memory recall isn't working as expected
- You want to share memory across multiple agents
Do NOT use this skill for within-session conversation history. That's handled automatically by the runtime — no configuration needed.
## Input
`$ARGUMENTS` is optional. If provided, use it as the memory resource name:
```
/memory # uses name from agentcore.json, or prompts
/memory UserContext # targets a specific memory resource by name
```
## Process
### Step 1: Read the project
Read `agentcore/agentcore.json`. Look for:
- The `memories` array — is memory already configured?
- The `runtimes` array — what agents are in the project and what framework do they use?
- The project `name` — needed for env var construction
**If `agentcore/agentcore.json` does not exist**, check if there's any AgentCore project structure nearby (look for `agentcore/` directory). If none found, proceed with the most helpful answer possible based on what the developer asked — don't block on missing context. If the question is about strategy selection or code patterns, answer it directly. Only ask "which situation are you in?" if the answer genuinely depends on it (e.g., they need CLI commands that differ by setup type).
### Step 2: Determine the situation
**Case A — No memory configured yet**
The `memories` array is empty or missing. Proceed to Step 3 (strategy selection).
**Case B — Memory configured, needs wiring**
Memory exists in `agentcore.json` but the agent code doesn't use it yet. Skip to Step 5 (generate wiring code).
**Case C — Memory configured and wired, debugging recall**
Ask: "What's happening? What did you expect the agent to remember, and what did it actually do?"
Then diagnose using the patterns in the Debugging section below.
**Case D — Developer asking about memory without a project**
Answer the question directly. For strategy questions, explain the options. For code questions, show the pattern with a note that they'll need to substitute their actual memory ID.
### Step 3: Choose a strategy
Present the options and ask the developer which fits their use case. Don't skip this — the wrong strategy wastes money and produces worse results.
```
Which memory strategy fits your use case?
SEMANTIC
Best for: remembering facts about users across sessions
How it works: extracts facts and stores them as embeddings; retrieves
relevant context via similarity search at session start
Cost: higher (embedding model + vector search per session)
Example: "Remember that Alex prefers bullet points and works in fintech"
USER_PREFERENCE
Best for: remembering explicit settings and preferences
How it works: extracts structured preference data; optimized for
key-value retrieval
Cost: lower (structured extraction, no vector search)
Example: "Remember my preferred response format and language"
SUMMARIZATION
Best for: remembering what you talked about last time
How it works: compresses conversation history into summaries; injects
the summary at the start of each new session
Cost: medium (summarization model runs at session end)
Example: "Pick up where we left off last time"
EPISODIC
Best for: remembering sequences of events or interactions over time
How it works: stores episodic records of interactions with temporal
context
Cost: medium
Common combinations:
SEMANTIC + USER_PREFERENCE → facts + preferences (most common)
SEMANTIC + SUMMARIZATION → full episodic memory (highest capability, highest cost)
USER_PREFERENCE alone → lightweight preference store
Which strategy (or combination) do you want?
```
### Step 4: Add memory to agentcore.json
Run the CLI command to add memory to the project config:
```bash
agentcore add memory --name <MemoryName> --strategies <STRATEGY1,STRATEGY2> --expiry 30
```
This updates `agentcore/agentcore.json`. The memory resource is provisioned when you next run `agentcore deploy` — it takes 2–5 minutes to become active.
The resulting config entry looks like:
```json
{
"memories": [{
"type": "AgentCoreMemory",
"name": "MyMemory",
"eventExpiryDuration": 30,
"strategies": [
{"type": "SEMANTIC"},
{"type": "USER_PREFERENCE"}
]
}]
}
```
**Memory name rules:** alphanumeric + underscores, max 48 chars, starts with a letter.
**Env var injected at deploy time:** `MEMORY_<UPPERCASENAME>_ID`
Example: memory named `UserContext` → env var `MEMORY_USERCONTEXT_ID`
### Step 5: Generate wiring code
Read `app/<AgentName>/main.py` (or the equivalent entrypoint) to detect the framework. Each framework has its own integration pattern — pick the one that matches:
| Framework | Recommended integration | Source |
|---|---|---|
| Strands | `AgentCoreMemorySessionManager` (CLI template) | `bedrock_agentcore.memory.integrations.strands.*` |
| LangGraph | `AgentCoreMemorySaver` + `AgentCoreMemoryStore` | `langgraph-checkpoint-aws` (official AWS-maintained) |
| OpenAI Agents SDK | `MemoryClient` via `@function_tool` | `bedrock_agentcore.memory.MemoryClient` |
| Google ADK / Claude Agent SDK | BYO — use `MemoryClient` directly | Validate end-to-end before shipping |
> [!WARNING]
> Always check for the MEMORY_ID env var before initializing memory. Memory is NOT
> available during `agentcore dev` — the env var is only set after deploy. Code that
> assumes memory is always available will fail silently in local development.
#### Strands — Session Manager pattern (recommended for new projects)
```python
import os
from bedrock_agentcore.runtime import BedrockAgentCoreApp
from bedrock_agentcore.memory.integrations.strands.config import AgentCoreMemoryConfig, RetrievalConfig
from bedrock_agentcore.memory.integrations.strands.session_manager import AgentCoreMemorySessionManager
from strands import Agent
from model.load import load_model # scaffolded by `agentcore create`
app = BedrockAgentCoreApp()
# AgentCore injects this env var after deploy.
# Format: MEMORY_<UPPERCASENAME>_ID
MEMORY_ID = os.getenv("MEMORY_<UPPERCASENAME>_ID")
REGION = os.getenv("AWS_REGION", "us-east-1")
@app.entrypoint
def invoke(payload, context):
actor_id = payload.get("userId", "default-user")
session_id = getattr(context, "session_id", "default-session")
session_manager = None
if MEMORY_ID:
# RetrievalConfig parameters:
# top_k: max number of memory records to retrieve per namespace (SDK default: 10)
# relevance_score: similarity threshold, 0 = return anything, 1 = exact match (SDK default: 0.2)
# The CLI template deviates from SDK defaults to favor precision over recall:
# top_k=3 limits context window usage; relevance_score=0.5 filters low-quality matches.
# Tune these if retrieval misses relevant facts (lower) or surfaces irrelevant ones (raise).
memory_config = AgentCoreMemoryConfig(
memory_id=MEMORY_ID,
session_id=session_id,
actor_id=actor_id,
retrieval_config={
f"/users/{actor_id}/facts": RetrievalConfig(top_k=3, relevance_score=0.5),
f"/users/{actor_id}/preferences": RetrievalConfig(top_k=3, relevance_score=0.5),
}
)
session_manager = AgentCoreMemorySessionManager(memory_config, REGION)
agent = Agent(
model=load_model(),
session_manager=session_manager, # None is safe — agent runs without memory
system_prompt="You are a helpful assistant.",
)
result = agent(payload.get("prompt", ""))
return {"response": str(result)}
if __name__ == "__main__":
app.run()
```
#### Strands — Hook pattern (for adding memory to an existing agent)
```python
import os
from bedrock_agentcore.memory import MemoryClient
from strands.hooks import AgentInitializedEvent, HookProvider, MessageAddedEvent
MEMORY_ID = os.getenv("MEMORY_<UPPERCASENAME>_ID")
memory_client = MemoryClient(region_name=os.getenv("AWS_REGION", "us-east-1")) if MEMORY_ID else None
class MemoryHook(HookProvider):
def on_agent_initialized(self, event):
"""Load recent conversation turns into the agent's context."""
if not MEMORY_ID:
return
session_id = event.agent.state.get("session_id", "default")
turns = memory_client.get_last_k_turns(
memory_id=MEMORY_ID,
actor_id="user",
session_id=session_id,
k=3
)
if turns:
context = "\n".join([
f"{m['role']}: {m['content']['text']}"
for t in turns for m in t
])
event.agent.system_prompt += f"\n\nPrevious conversation:\n{context}"
def on_message_added(self, event):
"""Save each message to memory after it's processed."""
if not MEMORY_ID:
return
session_id = event.agent.state.get("session_id", "default")
msg = event.agent.messages[-1]
memory_client.create_event(
memory_id=MEMORY_ID,
actor_id="user",
session_id=session_id,
messages=[(str(msg["content"]), msg["role"])]
)
def register_hooks(self, registry):
registry.add_callback(AgentInitializedEvent, self.on_agent_initialized)
registry.add_callback(MessageAddedEvent, self.on_message_added)
# Add to your existing agent:
agent = Agent(
# ... your existing config ...
hooks=[MemoryHook()] if MEMORY_ID else [],
state={"session_id": "default"},
)
```
#### LangGraph — `langgraph-checkpoint-aws` (recommended)
LangGraph has an **official AWS-maintained integration** via the [`langgraph-checkpoint-aws`](https://pypi.org/project/langgraph-checkpoint-aws/) package. It provides two integrations that map cleanly to LangGraph's memory model:
- **`AgentCoreMemorySaver`** — persists LangGraph's checkpoint objects (conversation state, execution graph, metadata) to AgentCore Memory. This is LangGraph's short-term / session memory.
- **`AgentCoreMemoryStore`** — saves conversational messages for AgentCore's long-term extraction (facts, preferences, summaries) and lets the agent search those memories in future sessions.
Use these instead of wiring `MemoryClient` calls into your graph manually — they handle the protocol conversion, actor/session mapping, and retry logic for you.
**Install:**
```bash
pip install langgraph-checkpoint-aws
```
**Required IAM permissions** on the agent's execution role:
- `bedrock-agentcore:CreateEvent`
- `bedrock-agentcore:ListEvents`
- `bedrock-agentcore:RetrieveMemories`
**Basic pattern — short-term checkpointing only:**
```python
import os
from langgraph.prebuilt import create_react_agent
from langgraph_checkpoint_aws import AgentCoreMemorySaver
from bedrock_agentcore.runtime import BedrockAgentCoreApp
from model.load import load_model # scaffolded by `agentcore create`
app = BedrockAgentCoreApp()
MEMORY_ID = os.getenv("MEMORY_<UPPERCASENAME>_ID")
REGION = os.getenv("AWS_REGION", "us-east-1")
# Only wire checkpointing if memory is available (deployed)
checkpointer = AgentCoreMemorySaver(MEMORY_ID, region_name=REGION) if MEMORY_ID else None
@app.entrypoint
async def invoke(payload, context):
actor_id = payload.get("userId", "default-user")
session_id = getattr(context, "session_id", "default-session")
graph = create_react_agent(
model=load_model(),
tools=tools,
checkpointer=checkpointer, # None is safe — graph runs without persistence
)
# LangGraph's RunnableConfig maps thread_id → AgentCore session_id,
# actor_id → AgentCore actor_id under the hood
config = {
"configurable": {
"thread_id": session_id,
"actor_id": actor_id,
}
}
result = await graph.ainvoke(
{"messages": [("human", payload["prompt"])]},
config=config,
)
return {"response": result["messages"][-1].content}
```
**Full pattern — short-term + long-term retrieval:**
For long-term memory (facts, preferences, summaries extracted by AgentCore), add `AgentCoreMemoryStore` with a pre-model hook that saves messages for extraction and (optionally) retrieves relevant memories:
```python
import os
import uuid
from langchain_core.messages import HumanMessage
from langchain_core.runnables import RunnableConfig
from langgraph.prebuilt import create_react_agent
from langgraph.store.base import BaseStore
from langgraph_checkpoint_aws import AgentCoreMemorySaver, AgentCoreMemoryStore
MEMORY_ID = os.getenv("MEMORY_<UPPERCASENAME>_ID")
REGION = os.getenv("AWS_REGION", "us-east-1")
checkpointer = AgentCoreMemorySaver(MEMORY_ID, region_name=REGION) if MEMORY_ID else None
store = AgentCoreMemoryStore(MEMORY_ID, region_name=REGION) if MEMORY_ID else None
def pre_model_hook(state, config: RunnableConfig, *, store: BaseStore):
"""Save the latest human message for async extraction; optionally retrieve preferences."""
actor_id = config["configurable"]["actor_id"]
thread_id = config["configurable"]["thread_id"]
namespace = (actor_id, thread_id)
messages = state.get("messages", [])
for msg in reversed(messages):
if isinstance(msg, HumanMessage):
store.put(namespace, str(uuid.uuid4()), {"message": msg})
break
# Optional: retrieve user preferences to inject into context
# preferences_ns = ("preferences", actor_id)
# preferences = store.search(preferences_ns, query=msg.content, limit=5)
return {"llm_input_messages": messages}
graph = create_react_agent(
model=load_model(),
tools=tools,
checkpointer=checkpointer,
store=store,
pre_model_hook=pre_model_hook if store else None,
)
```
**Invoke with config:**
```python
config = {"configurable": {"thread_id": "session-1", "actor_id": "user-alice"}}
response = graph.invoke({"messages": [("human", "I prefer short answers.")]}, config=config)
# New session for the same actor — long-term memories are retrieved
new_config = {"configurable": {"thread_id": "session-2", "actor_id": "user-alice"}}
response = graph.invoke({"messages": [("human", "Summarize my latest report.")]}, config=new_config)
```
The agent remembers "I prefer short answers" across sessions because AgentCore Memory extracts it as a user preference. See the [AgentCore docs on LangGraph integration](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/memory-integrate-lang.html) for the full reference.
**If you need low-level control** (custom retrieval queries, direct event management), fall back to `MemoryClient`:
```python
from bedrock_agentcore.memory import MemoryClient
client = MemoryClient(region_name=REGION)
# client.create_event(...), client.retrieve_memories(...), client.get_last_k_turns(...)
```
Use `MemoryClient` directly only when the checkpoint/store abstractions don't fit your use case.
#### OpenAI Agents SDK — memory as function tools
The OpenAI Agents SDK pattern is to expose memory as `@function_tool` decorated functions. The agent decides when to read and write:
```python
import os
from agents import Agent, Runner, function_tool
from bedrock_agentcore.memory import MemoryClient
MEMORY_ID = os.getenv("MEMORY_<UPPERCASENAME>_ID")
REGION = os.getenv("AWS_REGION", "us-east-1")
_client = MemoryClient(region_name=REGION) if MEMORY_ID else None
def _build_memory_tools(actor_id: str, session_id: str):
"""Factory — binds actor/session into tool closures."""
@function_tool
def recall_context(query: str, top_k: int = 3) -> str:
"""Search long-term memory for facts or preferences about the user."""
if not _client or not MEMORY_ID:
return "Memory unavailable."
try:
memories = _client.retrieve_memories(
memory_id=MEMORY_ID,
namespace=f"/users/{actor_id}/facts",
query=query,
top_k=top_k,
)
return "\n".join(m.get("content", {}).get("text", "") for m in memories) or "No relevant memories."
except Exception as e:
return f"Memory error: {e}"
@function_tool
def save_fact(content: str) -> str:
"""Save a fact to long-term memory."""
if not _client or not MEMORY_ID:
return "Memory unavailable."
try:
_client.create_event(
memory_id=MEMORY_ID,
actor_id=actor_id,
session_id=session_id,
messages=[(content, "ASSISTANT")],
)
return "Saved."
except Exception as e:
return f"Error: {e}"
return [recall_context, save_fact]
@app.entrypoint
async def invoke(payload, context):
actor_id = payload.get("userId", "default-user")
session_id = getattr(context, "session_id", "default-session")
agent = Agent(
name="Assistant",
instructions="Use recall_context at the start of each session to check what you know about the user. Use save_fact when the user tells you something worth remembering.",
tools=_build_memory_tools(actor_id, session_id),
)
result = await Runner.run(agent, payload["prompt"])
return {"response": result.final_output}
```
#### Google ADK and Claude Agent SDK — bring your own memory integration
AgentCore Memory doesn't have a framework-specific integration for ADK or the Claude Agent SDK yet, and the samples repo doesn't contain a combined pattern we can point to. Use the general `MemoryClient` API and wire it into the framework's existing extension points:
- **Google ADK:** Expose memory operations as ADK tools (functions passed to `Agent(tools=[...])`). The ADK agent decides when to call them.
- **Claude Agent SDK:** Wrap `query()` with a pre-call memory load and a post-call memory save. The SDK's `ClaudeAgentOptions.system_prompt` is the injection point for retrieved context.
For both frameworks, follow the `MemoryClient` API shown in the OpenAI Agents pattern above — the client calls (`retrieve_memories`, `create_event`, `get_last_k_turns`) are identical. The framework-specific part is just where you call them.
Before shipping a memory integration for ADK or Claude SDK, validate the end-to-end flow against a deployed agent:
1. Deploy with memory enabled
2. Invoke the agent with facts to remember
3. Start a new session
4. Invoke again and verify the agent recalls those facts
5. Check `agentcore logs --runtime <AgentName> --query "memory" --since 1h --level error` for any memory errors
If you build a working pattern, consider contributing it to [`awslabs/agentcore-samples`](https://github.com/awslabs/agentcore-samples) so the next developer doesn't have to figure it out.
### Step 6: Explain the local dev gap and next steps
Always include this note:
```
⚠️ Memory is not available during local development (agentcore dev).
The MEMORY_<NAME>_ID env var is only injected after deploy. The code above
handles this gracefully — it runs without memory when the env var isn't set.
To test memory:
agentcore deploy -y
agentcore invoke "My name is Alex and I prefer concise answers"
agentcore invoke "What do you know about me?"
If using long-term memory (SEMANTIC or USER_PREFERENCE), wait 5–30 seconds
between the first and second invoke — extraction runs asynchronously after
each session ends.
Session ID note: use UUIDs (v4) for session IDs — they satisfy the platform's
minimum length requirement (33 characters) and are what `agentcore invoke`
generates by default. Short or sequential session IDs (e.g., "session-1",
"test") can cause long-term memory extraction to fail silently.
```
**If the developer is using the SDK directly (no CLI project)**, they need to create the memory resource first:
```python
from bedrock_agentcore.memory import MemoryClient
client = MemoryClient(region_name="us-east-1")
# Create memory and wait for it to become ACTIVE (takes 2-5 minutes)
memory = client.create_memory_and_wait(
name="UserMemory",
strategies=[
{"userPreferenceMemoryStrategy": {
"name": "prefs",
"namespaces": ["/user/preferences/"]
}},
{"semanticMemoryStrategy": {
"name": "facts",
"namespaces": ["/user/facts/"]
}}
],
event_expiry_days=30
)
MEMORY_ID = memory["id"]
print(f"Memory created: {MEMORY_ID}")
# Set this as an env var or hardcode for testing:
# export MEMORY_ID=<value>
```
Then use the same wiring code from Step 5, reading `MEMORY_ID` from the environment.
## Debugging memory recall
If memory was working and stopped, or never worked:
**Agent keeps forgetting things even with memory set up:**
Most common cause: the memory resource is configured but the code isn't reading from it at session start. Check that your entrypoint calls `get_last_k_turns` (or uses the session manager) before creating the agent, not after. Also verify the `MEMORY_<NAME>_ID` env var is set — it's only injected after deploy, not during `agentcore dev`.
**Memory not persisting across sessions:**
1. Check that LTM strategies (SEMANTIC, USER_PREFERENCE) are configured — not just SUMMARIZATION
2. Wait 5–30 seconds after a session ends before starting a new one — extraction is async
3. Verify the memory resource is ACTIVE: `agentcore status --type memory`
4. Use UUIDs (v4) for session IDs — the platform requires a minimum of 33 characters. Short IDs like "session-1" or "test" cause LTM to fail silently. `agentcore invoke` generates compliant IDs by default.
**Memory not loading at session start:**
1. Verify `MEMORY_<NAME>_ID` env var is set: `agentcore status --type memory --json`
2. Check the actor_id is consistent across sessions — memory is scoped per actor
3. Confirm the namespace paths in retrieval_config match the namespaces used when writing — the retrieval namespace must exactly match the namespace the strategy extracts into
4. CLI defaults use paths without trailing slashes (e.g., `/users/{actorId}/facts`). If you customized namespace templates when creating the memory resource, use whatever pattern you chose — consistency between writer and reader is what matters.
**Memory provisioning slow:**
Memory takes 2–5 minutes to become ACTIVE after `agentcore deploy`. Check status:
```bash
agentcore status --type memory
```
## S3 delivery / export buckets must be in the same account
If you're configuring S3 delivery for memory exports, session transcripts, or Browser recording output, the destination bucket must be in the **same AWS account** as the AgentCore resource. Cross-account S3 buckets are not supported as delivery destinations, even with correct bucket policies granting the service principal access.
Symptom of attempting a cross-account bucket: `CreateMemory` (or the relevant resource creation call) fails with `ValidationException: Role does not have access to required S3 buckets` — even when IAM and bucket policies are correctly configured for cross-account access.
**Workaround:** create a same-account bucket for the AgentCore resource to write to. If you need the data in a different account, replicate from the same-account bucket via S3 replication or a scheduled copy job.
## Sharing memory across agents
Memory is a top-level resource — not nested under a single agent. To share:
1. Create one memory resource: `agentcore add memory --name SharedMemory --strategies SEMANTIC`
2. In each agent's code, read the same env var: `MEMORY_SHAREDMEMORY_ID`
3. Use a consistent `actor_id` scheme across agents (e.g., the end user's ID)
## Cross-region inference (data residency)
Memory consolidation (extraction + summarization for long-term strategies) uses cross-region inference by default. Your memory **data stays in your primary region**, but the **inference call** that extracts facts or summarizes a session may execute in another AWS region within the same geography (e.g., `us-east-1` → `us-east-2` or `us-west-2`; EU stays in EU; etc.).
This matters when:
- You have a data-residency requirement that goes beyond storage — some regulations constrain where inference may run, not just where results land
- You're building for a customer whose contract pins processing to a single region
- Your audit trail needs to show which region handled each prompt
**There's no extra cost for cross-region inference, and CloudWatch/CloudTrail logs don't include the inference region.** Across the `Memory`, `Policy`, and `Evaluations` services, this is the default behavior.
**To opt out for Memory:** use a **built-in-with-overrides** strategy (see [`memory-custom-strategy`](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/memory-custom-strategy.html)) and pin the model to a specific region. The overrides strategy lets you specify the exact model ID used for extraction and consolidation, which gives you region control.
The supported geographies and inference-region mappings change as AgentCore expands — check [the cross-region inference docs](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/cross-region-inference.html) for the current list rather than baking it in here.
## Beyond the CLI: memory features that require the API
The CLI's `agentcore add memory` and `agentcore.json` cover strategy selection, expiry, and basic configuration. Some memory capabilities are API/SDK-only — the CLI doesn't expose them. When the developer needs one of these, the graduation path is: create the memory via CLI as usual, deploy, then apply the additional config via boto3 or AWS CLI.
**Resource-based policies** (cross-account access, principal-level restrictions):
```python
import boto3, json
client = boto3.client("bedrock-agentcore-control")
memory_id = "<MEMORY_ID>" # from: agentcore status --type memory --json
client.put_memory_resource_policy(
memoryId=memory_id,
policy=json.dumps({
"Version": "2012-10-17",
"Statement": [{
"Effect": "Allow",
"Principal": {"AWS": "arn:aws:iam::111122223333:root"},
"Action": [
"bedrock-agentcore:CreateEvent",
"bedrock-agentcore:RetrieveMemories",
"bedrock-agentcore:ListEvents"
],
"Resource": "*"
}]
})
)
```
**Custom extraction models** (pin the model used for LTM extraction — e.g., for data residency):
Use the "built-in with overrides" strategy type via `UpdateMemory`. See the [custom memory strategy docs](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/memory-custom-strategy.html) for the full configuration shape.
**Self-managed strategies** (bring your own extraction logic):
Also API-only. See the AgentCore memory docs for the `selfManagedMemoryStrategy` configuration.
**When you hit a memory capability not covered here**, use the `awsknowledge` MCP server if available — search for the specific API operation (e.g., "AgentCore PutMemoryResourcePolicy") to get the current parameter shapes. The API surface evolves between releases.
**General rule:** if `agentcore.json` has a field for it, use the CLI. If it doesn't, create the resource via CLI, deploy, then apply the additional config via boto3. Don't fight the CLI to do something it wasn't designed for.
## Output
- Updated `agentcore/agentcore.json` with memory resource (via CLI command)
- Wiring code for `app/<AgentName>/main.py` appropriate for the detected framework
- Explanation of the local dev gap and how to test after deploy
## Quality criteria
- Generated code handles `MEMORY_ID` being None (local dev) without crashing
- Env var name matches the memory resource name in `agentcore.json` (uppercase, underscores)
- Framework-specific pattern is used — never generate Strands hooks for a LangGraph project
- LTM extraction delay is communicated
- Session ID guidance recommends UUIDs (v4) when LTM strategies are used (minimum 33 characters)
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# migrate
Move an existing Amazon Bedrock Agent to AgentCore Runtime.
## When to use
- You have an existing Bedrock Agent (created via the Bedrock console or API) and want to run it on AgentCore Runtime
- You want to add AgentCore capabilities (Memory, Gateway, Observability) to an existing agent
- You want to move from the declarative Bedrock Agents model to a code-first framework
## Input
`$ARGUMENTS` is optional:
```
/migrate # interactive — walks through the migration
/migrate strands # migrate targeting Strands framework
/migrate langgraph # migrate targeting LangGraph framework
```
## What migration does
The `agentcore create --type import` command reads your existing Bedrock Agent's configuration and generates an AgentCore project that reproduces its behavior in a code-first framework. Specifically:
- **System prompt** → copied into the generated `main.py`
- **Action groups (Lambda-backed)** → converted to Gateway targets with `--type lambda-function-arn`
- **Knowledge bases** → referenced in the system prompt with a note to wire retrieval manually (AgentCore doesn't auto-import KB bindings)
- **Guardrails** → noted in comments but not auto-converted (AgentCore uses Cedar policies, not Bedrock Guardrails)
- **Agent alias / version** → the import targets a specific alias, not the draft
What migration does **not** do:
- It does not delete or modify the original Bedrock Agent — the source agent keeps running
- It does not migrate conversation history or session state
- It does not convert Bedrock Guardrails to Cedar policies (different authorization model)
- It does not auto-wire Knowledge Base retrieval — you'll need to add that as a tool or direct SDK call
## Prerequisites
1. The Bedrock Agent must exist and have at least one alias
2. Your AWS credentials must have `bedrock:GetAgent`, `bedrock:GetAgentAlias`, and `bedrock:ListAgentActionGroups` permissions
3. You need the agent ID, alias ID, and region
## Process
### Step 1: Run the import
```bash
agentcore create \
--type import \
--agent-id <AGENT_ID> \
--agent-alias-id <ALIAS_ID> \
--region <REGION> \
--name <ProjectName> \
--framework Strands
```
The `--framework` flag determines which code-first framework the generated project uses. Strands is recommended for the closest mapping to Bedrock Agent behavior.
**Project name rules apply:** max 23 characters, alphanumeric only, starts with a letter.
### Step 2: Review the generated project
```bash
cd <ProjectName>
cat app/<AgentName>/main.py
cat agentcore/agentcore.json
```
Check:
- The system prompt matches your original agent's instructions
- Action groups appear as Gateway targets in `agentcore.json` (under `agentCoreGateways`)
- The model ID is correct for your target region
### Step 3: Fill in what migration doesn't cover
**Knowledge Bases:** If your Bedrock Agent used Knowledge Bases, you have two options:
1. **Keep using the KB via boto3** — call `bedrock-agent-runtime:RetrieveAndGenerate` or `Retrieve` directly from your agent code as a tool
2. **Replace with AgentCore Memory** — if the KB was used for user-specific context, AgentCore Memory with SEMANTIC strategy may be a better fit. See [memory.md](memory.md).
**Guardrails → Cedar policies:** Bedrock Guardrails (content filters, denied topics, word filters) don't have a 1:1 mapping to Cedar policies. Cedar policies control *which tools the agent can call and with what parameters* — they're authorization rules, not content filters. If you need content filtering, keep the guardrail logic in your agent code (pre/post-processing) or use Bedrock Guardrails as a standalone API call.
**Custom orchestration:** If your Bedrock Agent used custom orchestration (return-of-control, custom Lambda orchestrators), you'll need to rebuild that logic in the framework's native patterns — Strands tool chains, LangGraph graph nodes, etc.
### Step 4: Test locally and deploy
```bash
# Test locally (memory and gateway won't be available yet)
agentcore dev
# Deploy when ready
agentcore deploy -y
# Verify
agentcore invoke "Hello, what can you do?"
agentcore status
```
### Step 5: Cut over traffic
Once the AgentCore agent is working correctly:
1. Update your application to invoke the AgentCore Runtime instead of the Bedrock Agent
2. See [integrate.md](integrate.md) for the invocation patterns (SigV4, JWT, SDK)
3. Keep the original Bedrock Agent running as a fallback until you're confident
4. Delete the Bedrock Agent only after the AgentCore agent has been stable in production
## Common migration issues
**"Model not available in target region"**
The imported agent may reference a model ID that isn't available in your AgentCore deployment region. Edit `model/load.py` to use a cross-region inference profile or a model available in your region.
**"Action group Lambda in a different region"**
Gateway targets can invoke Lambda functions cross-region, but latency increases. Consider deploying the Lambda in the same region as your AgentCore agent, or accept the latency trade-off.
**"Agent behavior differs after migration"**
The most common cause is prompt format differences between Bedrock Agent's orchestration and the code-first framework. Bedrock Agent injects structured XML around tool results; Strands/LangGraph use different formats. Tune the system prompt to compensate.
## Output
- A working AgentCore project that reproduces the Bedrock Agent's behavior
- A list of what was auto-converted and what needs manual work
- Guidance on cutting over traffic from the old agent to the new one
plugins/aws-agents/skills/agents-build/references/multi-agent.md
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# multi-agent
Build AgentCore systems where agents delegate work to other agents.
## When to use
- You want an orchestrator agent to delegate complex tasks to a specialist
- You're building a system where agents have different roles and capabilities
- You want agents to discover and communicate with each other via the A2A standard
- You want multiple agents to share the same memory
## Input
`$ARGUMENTS` is optional:
```
/multi-agent # interactive — asks which pattern you need
/multi-agent a2a # A2A protocol setup
/multi-agent direct # direct invocation pattern
/multi-agent memory # shared memory across agents
```
## Choosing a pattern
### Step 1: Deploy the specialist agent
The specialist is a standard AgentCore agent. Deploy it normally:
```bash
agentcore create --name SpecialistAgent --defaults
# ... add your specialist logic to app/SpecialistAgent/main.py ...
agentcore deploy -y
```
Get the specialist's runtime ARN after deploy:
```bash
agentcore status --runtime SpecialistAgent --json | jq -r '.runtimes[0].arn'
```
### Step 2: Add the specialist as a tool in the orchestrator
The orchestrator calls the specialist via `bedrock-agentcore:InvokeAgentRuntime`. Add this tool to your orchestrator's agent code:
```python
import os
import json
import boto3
from bedrock_agentcore.runtime import BedrockAgentCoreApp
app = BedrockAgentCoreApp()
# Set this env var in your orchestrator's deployment config
SPECIALIST_ARN = os.getenv("SPECIALIST_AGENT_ARN")
REGION = os.getenv("AWS_REGION", "us-east-1")
def call_specialist(prompt: str, session_id: str = None) -> str:
"""
Call the specialist agent and return its response.
The specialist runs in its own isolated session.
"""
client = boto3.client("bedrock-agentcore", region_name=REGION)
kwargs = {
"agentRuntimeArn": SPECIALIST_ARN,
"qualifier": "DEFAULT", # or a specific version number to pin
"payload": json.dumps({"prompt": prompt}).encode(),
}
if session_id:
kwargs["runtimeSessionId"] = session_id
response = client.invoke_agent_runtime(**kwargs)
# response["response"] is a StreamingBody — read, then parse JSON
body = response["response"].read()
result = json.loads(body.decode() if isinstance(body, bytes) else body)
return result.get("response", result.get("result", str(result)))
```
Passing `"DEFAULT"` as the qualifier calls the live version. To pin to a specific version (staging pin, canary, or rollback), pass a numeric version string instead — see [`agents-deploy/references/versioning.md`](../../agents-deploy/references/versioning.md) for the full workflow.
**For Strands**, register it as a `@tool`:
```python
from strands import Agent, tool
@tool
def delegate_to_specialist(task: str) -> str:
"""
Delegate a complex analysis task to the specialist agent.
Use this when the task requires deep domain expertise.
Args:
task: The specific task or question for the specialist.
Returns:
The specialist's detailed response.
"""
return call_specialist(task)
@app.entrypoint
def invoke(payload, context):
agent = Agent(
model=load_model(), # scaffolded by `agentcore create`
system_prompt="""You are an orchestrator. For complex analysis tasks,
delegate to the specialist using the delegate_to_specialist tool.
Synthesize the specialist's response for the user.""",
tools=[delegate_to_specialist],
)
result = agent(payload.get("prompt", ""))
return {"response": str(result)}
if __name__ == "__main__":
app.run()
```
**For LangGraph**, add it as a tool node:
```python
from langchain_core.tools import tool as lc_tool
@lc_tool
def delegate_to_specialist(task: str) -> str:
"""Delegate complex tasks to the specialist agent."""
return call_specialist(task)
# Add to your LangGraph tool node
tools = [delegate_to_specialist]
tool_node = ToolNode(tools)
llm_with_tools = llm.bind_tools(tools)
```
**For OpenAI Agents SDK**, register as a `@function_tool`:
```python
from agents import Agent, Runner, function_tool
@function_tool
def delegate_to_specialist(task: str) -> str:
"""Delegate a complex analysis task to the specialist agent.
Use when the task requires deep domain expertise."""
return call_specialist(task)
@app.entrypoint
async def invoke(payload, context):
agent = Agent(
name="Orchestrator",
instructions="For complex analysis, delegate to the specialist using delegate_to_specialist. Synthesize the response for the user.",
tools=[delegate_to_specialist],
)
result = await Runner.run(agent, payload["prompt"])
return {"response": result.final_output}
```
**For Google ADK**, pass as a plain function in the agent's `tools=[]` list. Note: the official samples use A2A for ADK multi-agent patterns (see `awslabs/agentcore-samples/02-use-cases/A2A-multi-agent-incident-response/host_adk_agent/`). The direct-invocation pattern below is extrapolated from the ADK base template — validate against your ADK version before relying on it in production:
```python
from google.adk.agents import Agent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types
def delegate_to_specialist(task: str) -> str:
"""Delegate complex analysis to the specialist agent."""
return call_specialist(task)
agent = Agent(
model="gemini-2.5-flash",
name="orchestrator",
description="Orchestrator that delegates complex tasks to specialists.",
instruction="For complex analysis, call delegate_to_specialist and synthesize the response.",
tools=[delegate_to_specialist],
)
@app.entrypoint
async def invoke(payload, context):
user_id = payload.get("user_id", "default_user")
session_id = getattr(context, "session_id", "default_session")
session_service = InMemorySessionService()
session = await session_service.create_session(
app_name="orchestrator", user_id=user_id, session_id=session_id
)
runner = Runner(agent=agent, app_name="orchestrator", session_service=session_service)
content = types.Content(role="user", parts=[types.Part(text=payload["prompt"])])
async for event in runner.run_async(user_id=user_id, session_id=session.id, new_message=content):
if event.is_final_response():
return {"response": event.content.parts[0].text}
```
For a validated ADK multi-agent pattern, use A2A instead of direct invocation — see the A2A section below and the sample linked above.
**For Claude Agent SDK:** See [`awslabs/agentcore-samples/03-integrations/agentic-frameworks/claude-agent/claude-sub-agents/`](https://github.com/awslabs/agentcore-samples/tree/main/03-integrations/agentic-frameworks/claude-agent/claude-sub-agents) for the official sub-agent pattern. This plugin doesn't ship a Claude SDK delegation pattern because the sample is more current than anything we could extrapolate.
### Step 3: Grant IAM permission
The orchestrator's execution role needs permission to invoke the specialist:
```json
{
"Effect": "Allow",
"Action": "bedrock-agentcore:InvokeAgentRuntime",
"Resource": "arn:aws:bedrock-agentcore:<REGION>:<YOUR_ACCOUNT_ID>:runtime/SpecialistAgent-*"
}
```
Add this to `agentcore/agentcore.json` under the orchestrator agent's IAM config, or add it manually to the auto-created execution role after deploy.
### Step 4: Pass the specialist ARN at deploy time
Add the specialist ARN as an environment variable in the orchestrator's deployment:
```bash
# After deploying the specialist, get its ARN:
SPECIALIST_ARN=$(agentcore status --runtime SpecialistAgent --json | jq -r '.runtimes[0].arn')
# For local dev, write to .env.local:
echo "SPECIALIST_AGENT_ARN=$SPECIALIST_ARN" >> agentcore/.env.local
```
**For the deployed orchestrator**, the specialist ARN needs to be available as an environment variable. The recommended pattern is:
1. **Edit `agentcore/agentcore.json`** — find the orchestrator agent's entry and add the env var to its configuration (the exact field name depends on your CLI version; run `agentcore validate` after editing). In current CLI versions, agent environment variables are typically managed through the deployment config.
2. **Or use CDK overrides** — for teams using the CDK constructs directly, set the env var in the Runtime construct's environment property.
3. **Or write the env var at deploy time** — some teams use a pre-deploy script that generates `agentcore/.env.local` and `agentcore/agentcore.json` updates together:
```bash
# pre-deploy.sh — run before every orchestrator deploy
SPECIALIST_ARN=$(agentcore status --runtime SpecialistAgent --json | jq -r '.runtimes[0].arn')
echo "SPECIALIST_AGENT_ARN=$SPECIALIST_ARN" >> agentcore/.env.local
# Then deploy
agentcore deploy -y
```
The CLI does not currently provide a dedicated `--env` flag on `agentcore add agent`. Check `agentcore add agent --help` for the current options in your CLI version.
---
## Pattern 2: A2A protocol
The specialist exposes the A2A standard — discoverable via an agent card, callable via JSON-RPC. AgentCore's A2A runtime handles the HTTP server, port binding (9000), and agent card serving for you.
### Step 1: Build the A2A specialist
Use the `serve_a2a` helper from `bedrock-agentcore` — this matches what the CLI scaffolds via `agentcore create --protocol A2A`.
```python
# app/SpecialistA2A/main.py
from strands import Agent, tool
from strands.multiagent.a2a.executor import StrandsA2AExecutor
from bedrock_agentcore.runtime import serve_a2a
from model.load import load_model
@tool
def analyze_data(dataset_name: str) -> str:
"""Run detailed analysis on the named dataset."""
# Your specialist logic here
return f"Analysis results for {dataset_name}..."
agent = Agent(
model=load_model(),
system_prompt="You are an analysis specialist. Use tools when appropriate.",
tools=[analyze_data],
)
if __name__ == "__main__":
serve_a2a(StrandsA2AExecutor(agent))
```
```
# requirements.txt
strands-agents[a2a]
bedrock-agentcore
```
`serve_a2a` handles port 9000 binding, agent card generation at `/.well-known/agent-card.json`, and JSON-RPC routing automatically. No FastAPI or uvicorn needed.
### Step 2: Deploy the A2A specialist
```bash
agentcore create --name SpecialistA2A --protocol A2A
# The CLI scaffolds app/SpecialistA2A/main.py with the serve_a2a pattern shown above — customize it with your specialist logic
agentcore deploy -y
```
After deploy, get the runtime URL:
```bash
agentcore fetch access --name SpecialistA2A --type agent
```
### Step 3: Test locally
```bash
# Start the A2A server locally (from your project dir)
agentcore dev
# Test the agent card (discovery)
curl http://localhost:9000/.well-known/agent-card.json | jq .
# Send a message
curl -X POST http://localhost:9000 \
-H "Content-Type: application/json" \
-d '{
"jsonrpc": "2.0",
"id": "req-001",
"method": "message/send",
"params": {
"message": {
"role": "user",
"parts": [{"kind": "text", "text": "What is 42 * 17?"}],
"messageId": "msg-001"
}
}
}' | jq .
```
### Step 4: Call the A2A specialist from the orchestrator
The specialist URL is a non-secret identifier, so pass it via an env var in the orchestrator's deployment config. The bearer token **is** a secret — do **not** stash it in `os.getenv(...)` on the deployed runtime (runtime env vars are not vault-backed). Register an OAuth M2M provider once, then use `@requires_access_token` to fetch a fresh token at call time:
```bash
# One-time: register the OAuth provider that issues tokens for the specialist.
# Omit --client-secret to get an interactive prompt (value goes straight into the credential provider).
agentcore add credential \
--name SpecialistA2A \
--type oauth \
--discovery-url https://<YOUR_IDP>/.well-known/openid-configuration \
--client-id <CLIENT_ID> \
--scopes a2a.invoke
```
```python
import asyncio
import os
from uuid import uuid4
import httpx
from a2a.client import A2ACardResolver, ClientConfig, ClientFactory
from a2a.types import Message, Part, Role, TextPart
from bedrock_agentcore.identity.auth import requires_access_token
# Non-secret identifier — fine to pull from the environment.
SPECIALIST_URL = os.getenv("SPECIALIST_A2A_URL")
@requires_access_token(
provider_name="SpecialistA2A",
scopes=["a2a.invoke"],
auth_flow="M2M",
)
async def call_a2a_specialist(message: str, *, access_token: str) -> str:
session_id = str(uuid4())
headers = {
"Authorization": f"Bearer {access_token}",
"X-Amzn-Bedrock-AgentCore-Runtime-Session-Id": session_id,
}
async with httpx.AsyncClient(timeout=300, headers=headers) as http_client:
resolver = A2ACardResolver(httpx_client=http_client, base_url=SPECIALIST_URL)
agent_card = await resolver.get_agent_card()
config = ClientConfig(httpx_client=http_client, streaming=False)
client = ClientFactory(config).create(agent_card)
msg = Message(
kind="message",
role=Role.user,
parts=[Part(TextPart(kind="text", text=message))],
message_id=uuid4().hex,
)
async for event in client.send_message(msg):
if hasattr(event, "parts"):
return " ".join(p.text for p in event.parts if hasattr(p, "text"))
return ""
# Use in your orchestrator's entrypoint:
@app.entrypoint
def invoke(payload, context):
result = asyncio.run(call_a2a_specialist(payload.get("prompt", "")))
return {"response": result}
```
The decorator handles caching and refresh. For local dev, put the OAuth values in `agentcore/.env.local` so `agentcore dev` can resolve the decorator — the deployed runtime reads them from the credential provider instead.
---
## Shared memory across agents
Memory is a top-level resource — not nested under a single agent. Multiple agents can share it by reading the same env var.
### Setup
1. Create one shared memory resource:
```bash
agentcore add memory --name SharedMemory --strategies SEMANTIC,USER_PREFERENCE
```
1. In each agent's code, read the same env var:
```python
MEMORY_ID = os.getenv("MEMORY_SHAREDMEMORY_ID")
```
1. Use a consistent `actor_id` scheme — typically the end user's ID — so both agents read and write the same user's memory.
### Key consideration
When multiple agents share memory, they share the same namespace. Use namespaced paths to avoid collisions:
```python
# Orchestrator writes to /orchestrator/ namespace
memory_client.create_event(
memory_id=MEMORY_ID,
actor_id=user_id,
session_id=session_id,
messages=[("User asked about X", "user")],
)
# Specialist reads from all namespaces
turns = memory_client.get_last_k_turns(
memory_id=MEMORY_ID,
actor_id=user_id,
session_id=session_id,
k=5,
)
```
---
## Troubleshooting
**A2A server not responding:**
- Verify it's running on port 9000 (not 8080)
- Check the agent card endpoint returns: `curl http://localhost:9000/.well-known/agent-card.json`
- Verify your `main.py` uses `serve_a2a(StrandsA2AExecutor(agent))` — the older `A2AServer + FastAPI` pattern is deprecated in favor of this
**Direct invocation permission denied:**
- Check the orchestrator's execution role has `bedrock-agentcore:InvokeAgentRuntime`
- Verify the resource ARN pattern matches the specialist's ARN
- IAM changes take ~30 seconds to propagate
**Specialist not found:**
- Verify `SPECIALIST_AGENT_ARN` env var is set correctly
- Check `agentcore status --runtime SpecialistAgent` shows `deployed` state
**A2A auth errors:**
- A2A supports SigV4 and OAuth 2.0 — make sure you're using the right auth method
- Get the correct bearer token: `agentcore fetch access --name SpecialistA2A --type agent`
## Output
- Decision tree to choose the right pattern
- Complete code for the chosen pattern (orchestrator + specialist)
- IAM policy for agent-to-agent invocation
- Local testing commands
## Quality criteria
- Pattern recommendation matches the developer's latency and interoperability needs
- Generated code includes correct IAM permissions for agent-to-agent invocation
- A2A server runs on port 9000 (not 8080) using `serve_a2a(StrandsA2AExecutor(agent))`
- Agent card is at `/.well-known/agent-card.json` with correct capabilities
- Shared memory uses consistent `actor_id` scheme across agents
plugins/aws-agents/skills/agents-build/references/payments.md
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# payments
Add AgentCore Payments to your agent — the managed service that lets your agent pay for x402-protected APIs, MCP tools, and web content via microtransactions (Coinbase CDP, Stripe Privy).
The control-plane resources (payment manager, connector, credential provider) are provisioned with the AgentCore **CLI**. The per-user data-plane resources (instrument, session) are created with the AgentCore **SDK** (a provided script). Payments are wired into the agent with a small **framework-agnostic local tool** (`scripts/x402_payment_tool.py`) — so this works with Strands, LangGraph, OpenAI Agents SDK, or any Python framework, in the AgentCore Runtime or on any other host.
## When to use
- You want your agent to autonomously pay for x402-protected content (APIs, MCP tools, paywalled sites)
- A tool call returns `402 Payment Required` and you want it settled and retried automatically
- You have a payment manager and need to wire payments into your agent code
- You want budget controls on what the agent can spend
- Payment processing isn't working as expected
Do NOT use this skill for:
- Connecting to non-paid external tools/APIs via Gateway → use `agents-connect`
- Inbound auth (who can call your agent) → use `agents-harden`
- General agent scaffolding or project creation
- Non-payment related agent capabilities (memory, VPC, multi-agent)
## Input
`$ARGUMENTS` is optional: `/payments`, `/payments wire`, `/payments debug`, `/payments coinbase`, `/payments stripe`.
## Process
**Execution model — minimize human stops.** Run the steps yourself, in order, without pausing between them. There are only **two** points that require the developer; pause at these and resume automatically once the developer confirms:
- **Step 3b (connector credentials)** — the developer runs the connector command (it involves their secrets). Present it, then wait for them to confirm it's done.
- **Step 7 (delegation + funding)** — the developer authorizes the wallet and funds it (browser + faucet). Surface the instructions, then wait.
Everything else — Steps 0–3a, **4 (deploy), 5 (wire), 6 (instrument/session), 8 (set env + test)** — you run automatically. After the developer confirms 3b, ask them for the **user id** and **email** for the first wallet (Step 6 needs them), then immediately continue through 4 → 5 → 6 (and present Step 7) without asking permission for each. After they confirm 7, run Step 8. Do not stop after every step.
### Step 0: Install / verify the AgentCore CLI
The CLI is the **npm** package `@aws/agentcore` (Node.js 20+). It is NOT a pip package — do not `pip install` it.
```bash
agentcore --version # need >= 0.20.0 (payment commands are preview, added in 0.20.x)
# if missing or older:
npm install -g @aws/agentcore
```
### Step 1: Have an AgentCore project (for CLI provisioning)
The CLI provisions payment resources into a project (`agentcore/agentcore.json`).
- **Project exists**: read `agentcore/agentcore.json` — check the `payments` array and the `runtimes` array (framework).
- **No project**: scaffold one (don't call `--help`; run it directly). Non-interactive:
```bash
agentcore create --project-name <ProjectName> --name <AgentName> --framework Strands --defaults
```
`--project-name` and `--name` are both required non-interactively (`--name` is the agent/resource name; without it the CLI drops to the interactive wizard). Project name: start with a letter, alphanumeric, ≤23 chars, no underscores. `--defaults` = Python + Bedrock, no memory; or run `agentcore create` for the interactive wizard. A project is only needed to provision the payment resources via the CLI — the local payment tool (Step 5) works in any agent, framework, or host.
### Step 2: Determine the situation
- **Case A — nothing configured**: proceed to Step 3.
- **Case B — manager/connector exist, needs wiring**: skip to Step 5.
- **Case C — wired, debugging**: ask what's failing, then use the Debugging section.
- **Case D — developer asking about payments without a project** (architecture, flow explanation): explain the x402 end‑to‑end flow (see **How x402 Payment Works** section), and ask whether they want to set up payments (→ proceed to Step 3) or need wiring help (→ Step 5).
### Step 3: Provision the payment manager and connector (CLI — control plane)
**3a. Payment manager — no secrets, run it directly (non-interactive).** The agent can run this for the developer:
```bash
agentcore add payment-manager \
--name <ManagerName> \
--network-preferences eip155:84532
```
`eip155:84532` is Base Sepolia (testnet). Names: alphanumeric + underscores, ≤48 chars, start with a letter.
**3b. Payment connector — needs provider credentials. The DEVELOPER runs this, not the agent.** The agent presents the prerequisites and the command below, but must NOT execute it or handle the credentials. This single command creates the credential provider and the connector. The CLI writes the provider secrets in **plaintext to `agentcore/.env.local`** and records the credential locally; `agentcore deploy` (Step 4) then uploads them to **AgentCore Identity** (`agentcore.json` keeps only a reference). The provider secrets are used only here — nothing later reuses them.
**Before running — get your provider credentials** (do this first; the connector command needs them):
- **Coinbase CDP** (<https://portal.cdp.coinbase.com/>):
1. Create or log in to a Coinbase Developer Platform account and project
2. Generate an API key (or reuse existing) — note the **API Key ID** and **API Key Secret**
3. Generate a **Wallet Secret** (for cryptographic wallet operations like signing transactions)
4. Under Project > Wallet > Embedded Wallets > Policies, **enable Delegated signing** (required)
- **Stripe Privy** (<https://dashboard.privy.io/>):
1. Create a **dedicated** Privy app for AgentCore (do not reuse apps serving other purposes)
2. Copy the **App ID** and **App Secret** from app settings
3. Navigate to Wallet Infrastructure > Authorization > New Key to generate a P-256 key pair
4. The private key is prefixed with `wallet-auth:` — **strip this prefix**, use only the raw base64 content (starting `MIGHAgEA...`)
5. Note the **Authorization ID** (signer ID) shown alongside the key
Recommended — interactive wizard. Run the command with **no flags** (the secrets never appear in the command, shell history, or process list; the CLI still writes them to `agentcore/.env.local` either way — see the security note below). Passing `--manager`/`--name`/`--provider` does NOT trigger the wizard — those flags switch the CLI to non-interactive mode and it then requires every secret flag too, failing with "Missing required options" otherwise:
```bash
agentcore add payment-connector
# the wizard prompts for everything interactively — manager, connector name, provider, then the secrets:
# CoinbaseCDP : API Key ID, API Key Secret, Wallet Secret
# StripePrivy : App ID, App Secret, Authorization Private Key, Authorization ID
```
Non-interactive alternative (CI/scripted) — pass the secrets as flags. These land in shell history and the process list, so prefer the wizard for local setup:
```bash
# Coinbase CDP (dummy values — replace with your own)
agentcore add payment-connector --manager <ManagerName> --name <ConnectorName> --provider CoinbaseCDP \
--api-key-id 11111111-2222-3333-4444-555555555555 \
--api-key-secret cdp_sk_EXAMPLEexampleEXAMPLEexampleEXAMPLE0000 \
--wallet-secret cdp_wallet_EXAMPLEexampleEXAMPLEexample1111
# Stripe Privy (dummy values — replace with your own)
agentcore add payment-connector --manager <ManagerName> --name <ConnectorName> --provider StripePrivy \
--app-id clxxxxxxxxxxxxxxxxxxxxxxxx \
--app-secret privy_sk_EXAMPLEexampleEXAMPLEexample2222 \
--authorization-private-key MIGHAgEAMBMGByqGSM49AgEGCCqGSM49AwEHBHkwEXAMPLE... \
--authorization-id ezzzzzzzzzzzzzzzzzzzzzzzz
```
> **Wizard vs flags:** The flags `--manager`, `--name`, and `--provider` are marked `[non-interactive]` — if you provide any of them, the CLI switches to **non-interactive mode** and expects **all required secrets as flags**. Running it with those three flags but omitting the secrets errors with missing-required-flags rather than dropping back to the wizard. For the interactive wizard, run the command with no flags: `agentcore add payment-connector`. Then wait for the developer to confirm it's done.
Security:
- **`agentcore/.env.local` holds the provider secrets in plaintext.** The CLI writes it when the connector is added (wizard or flags) and uploads it to AgentCore Identity at `agentcore deploy`. Ensure it is gitignored — the Python scaffold's default `.gitignore` only lists `.env`, so add `.env.local` (or `.env.*`). The agent must not read `agentcore/.env.local`.
- The agent presents the command but never runs it or handles the credentials; never paste credentials into chat.
### Step 4: Deploy (create the resources) — agent runs
```bash
agentcore deploy -y
```
`agentcore deploy` provisions the project's resources to your AWS account: the payment manager/connector via the AgentCore control plane, and supporting IAM (the `Payment<Name>ProcessPaymentRole`) and any runtime via a CloudFormation stack (CDK). After deploy, the manager ARN, connector ID, and role ARN are written to `agentcore/.cli/deployed-state.json`. On CLI 0.20.x these live under `targets.<target>.resources.payments[]` (`managerArn`, `connectors[].connectorId`, `processPaymentRoleArn`); the Step 6 script reads this shape automatically.
### Step 5: Wire the agent (framework-agnostic local tool) — agent runs
Payments are wired with a small local tool, not a framework-specific plugin — so the same code works in any framework.
1. **Copy [`scripts/x402_payment_tool.py`](../scripts/x402_payment_tool.py) into the agent project.** It exposes `x402_fetch(url, method="GET")`, which on a `402` calls the SDK's `PaymentManager.generate_payment_header` — the SDK validates the 402, selects the network, processes the payment, and builds the version-aware proof (v1 `X-PAYMENT` / v2 `PAYMENT-SIGNATURE`) — then retries with a fresh client. Base Sepolia settlement is intermittently transient (the header is valid but the paid retry still returns 402), so the tool re-runs the settle+replay flow up to `X402_MAX_PAYMENT_ATTEMPTS` times (default 5, env-overridable) before giving up. It reuses a single idempotency token across those retries, so `ProcessPayment` stays idempotent — every attempt replays the same on-chain authorization/nonce and the user is never charged twice (a retry either settles the not-yet-settled payment or, if it was already settled, reverts on-chain). It reads its config from environment variables (set in Step 8): `PAYMENT_MANAGER_ARN`, `PAYMENT_INSTRUMENT_ID`, `PAYMENT_SESSION_ID`, `PAYMENT_USER_ID`, `AWS_REGION`.
2. **Register `x402_fetch` as a tool** in the agent's framework. The tool function is identical; only the registration decorator differs:
```python
# Strands
from strands import Agent, tool
from x402_payment_tool import x402_fetch as _x402
x402_fetch = tool(_x402)
agent = Agent(model=..., tools=[x402_fetch], system_prompt="... use x402_fetch for paid URLs ...")
```
```python
# LangGraph
from langchain_core.tools import tool
from langgraph.prebuilt import create_react_agent
from x402_payment_tool import x402_fetch as _x402
graph = create_react_agent(model, tools=[tool(_x402)])
```
```python
# OpenAI Agents SDK
from agents import Agent, function_tool
from x402_payment_tool import x402_fetch as _x402
agent = Agent(name="PaymentAgent", tools=[function_tool(_x402)], instructions="... use x402_fetch for paid URLs ...")
```
For any other framework, register `x402_fetch` using that framework's tool mechanism — the function is plain Python.
The agent calls `x402_fetch` instead of a generic HTTP tool; payment is handled inside the tool. (Tell the model, via the system prompt, to use `x402_fetch` for URLs that may require payment.)
### Step 6: Provision the per-user instrument and session (SDK script — data plane) — agent runs
The instrument (per-user wallet) and session (budget-bounded spend window) are data-plane resources — there is no CLI command for them. First ask the developer for the **user id** and **email** to provision the wallet for (if not already collected after Step 3b). Then run the provided script [`scripts/setup_payment_user.py`](../scripts/setup_payment_user.py) once per user. It auto-reads the manager ARN/connector ID from `deployed-state.json` (or accepts `--manager-arn`/`--connector-id`):
```bash
python scripts/setup_payment_user.py --user-id alice --email alice@example.com --budget 5
```
It creates the instrument (with the email in `linkedAccounts`) and a budget-bounded session, then prints the `export` lines for `PAYMENT_INSTRUMENT_ID` / `PAYMENT_SESSION_ID` / `PAYMENT_USER_ID` (used in Step 8), plus the `wallet_address` and `redirect_url` (used in Step 7). The script is the canonical data-plane path — do not hand-write the SDK calls.
### Step 7: Delegation and funding (one-time per wallet) — developer does this
Using the `wallet_address` / `redirect_url` the script printed:
1. **Delegation** — authorize the agent to spend from the wallet.
- **Coinbase CDP**: the end user visits `redirect_url`, logs in, and grants permissions to `wallet_address`.
- **Stripe Privy**: no `redirect_url`; use the Privy frontend SDK (<https://github.com/privy-io/aws-agentcore-sdk>), log in with the end user's email, approve delegation.
2. **Funding** — send testnet USDC to `wallet_address` via the Circle faucet (<https://faucet.circle.com/>), Base Sepolia.
### Step 8: Set env vars and test — agent runs
Set the tool's config from the `export` lines the Step 6 script printed — it emits all of them (`PAYMENT_MANAGER_ARN`, `PAYMENT_INSTRUMENT_ID`, `PAYMENT_SESSION_ID`, `PAYMENT_USER_ID`, `AWS_REGION`), so just copy them into the agent's environment:
```bash
export PAYMENT_MANAGER_ARN=... # all five printed by setup_payment_user.py
export PAYMENT_INSTRUMENT_ID=...
export PAYMENT_SESSION_ID=...
export PAYMENT_USER_ID=...
export AWS_REGION=...
```
Run the agent and prompt it to fetch a paid endpoint:
```
Fetch https://sandbox.node4all.com/v1/x402-test and tell me what you find.
```
Run it however your agent runs — directly in your framework, or `agentcore dev` for a local server / `agentcore invoke` for the deployed runtime (set the same `PAYMENT_*` env vars on the runtime). A successful run shows `x402_fetch` hitting `402`, settling payment, and the retry returning `200`.
## Debugging payments
**Agent sees 402 but does not pay:**
1. Verify `PAYMENT_MANAGER_ARN` env var is set and not None
2. Check that the agent is using `x402_fetch` tool (not a generic `http_request`)
3. Verify the x402 challenge is present in either the response body (`x402Version` + `accepts` fields) or the `payment-required` header
**ProcessPayment fails with "Failed to obtain resource payment token":**
- The IAM service role is missing permissions. Ensure it has `GetResourcePaymentToken` on the token-vault and `secretsmanager:GetSecretValue` on the secrets.
- Wait 15+ seconds after creating the role before calling ProcessPayment (IAM propagation).
**ProcessPayment fails with "Failed to obtain workload access token":**
- The service role is missing `GetWorkloadAccessToken` permission on the workload-identity-directory resources.
**ProcessPayment fails with "Failed to assume payment execution role":**
- The service role's trust policy is incorrect. Ensure it trusts `bedrock-agentcore.amazonaws.com` with the correct `aws:SourceAccount` condition.
- Verify the role ARN passed to the Payment Manager matches the actual role.
**ProcessPayment succeeds but merchant still returns 402:**
- **Transient on‑chain settlement failure** (common on Base Sepolia): the tool already re‑settles up to `X402_MAX_PAYMENT_ATTEMPTS` times (default 5). If still 402s, raise the cap (`export X402_MAX_PAYMENT_ATTEMPTS=8`) or retry shortly.
- **Cookie contamination**: The retry is sending cookies from the initial 402 request. Ensure you use a fresh httpx client: `httpx.Client(cookies=None).request(...)` — do NOT reuse the same client/session.
- **Wrong x402 version / header**: The merchant is x402 v2 but the proof was sent as v1 (or vice versa). v1 expects an `X-PAYMENT` header with a flat proof (top-level `scheme`/`network`); v2 expects a `PAYMENT-SIGNATURE` header where `accepted` is a top-level sibling of `payload`, and `payload` holds only `signature` + `authorization` (no top-level `scheme`/`network`). A v2 merchant that receives a v1 `X-PAYMENT` header ignores it and re-issues the same 402 — often with an empty `{}` body and no error, which is hard to diagnose. Read `x402Version` from the challenge (body or `payment-required` header) and build the matching proof.
- **Proof format mismatch (network field)**: For **v1**, the proof `network` must use the merchant's human label (e.g., `"base-sepolia"` not `"eip155:84532"`). For **v2**, the proof keeps the CAIP-2 identifier from the challenge unchanged (e.g., `"eip155:84532"`). Note: the `ProcessPayment` input always uses CAIP-2 regardless of version — only the proof presented to the merchant differs.
- **Proof expired**: The proof has a ~60 second validity window (`validBefore`). If the agent loop is slow, the proof may expire before the retry.
**ProcessPayment succeeds (PROOF_GENERATED) but merchant returns 402 with an empty `{}` body and no error:**
- The merchant is x402 **v2** and is ignoring the v1 `X-PAYMENT` header. Detect the version from the challenge (`x402Version: 2`, present in the body or the `payment-required` response header) and send a `PAYMENT-SIGNATURE` header. The v2 proof puts `accepted` (the full requirements, CAIP-2 network) as a top-level sibling of `payload`, with `payload` containing only `signature` + `authorization`. Note: if ProcessPayment returns `PROOF_GENERATED` and the proof shape is correct but the merchant still 402s, it may be a transient on-chain settlement failure — retry once before assuming a format problem.
**ProcessPayment fails with "Payment session not found":**
- The session ID is invalid or the session was deleted. Create a new session.
- Ensure the `paymentManagerArn` in the session creation matches the one used in ProcessPayment.
**ProcessPayment fails with "PaymentSessionExpired":**
- Payment sessions are time-bounded. Create a fresh session with `expiryTimeInMinutes`.
**ProcessPayment fails with "Payment instrument not found" or "does not belong to user":**
- Verify the instrument ID is correct and belongs to the same Payment Manager.
- Check that the `userId` passed to ProcessPayment matches the `userId` used when the instrument was created.
**ProcessPayment fails with "Payment connector is not active":**
- The connector may still be provisioning. Check its status and wait.
- If the connector was deleted or deactivated, create a new one.
**ProcessPayment fails with "Network mismatch":**
- The x402 challenge specifies a network that does not match the instrument's network.
- Instruments created with `network: "ETHEREUM"` support Base, Base Sepolia, and Ethereum chains.
- Instruments created with `network: "SOLANA"` support Solana and Solana Devnet chains.
**ProcessPayment fails with "Payment asset not supported USDC token address":**
- The USDC contract address in the x402 challenge does not match the expected address for that network.
- Base Sepolia USDC: `0x036CbD53842c5426634e7929541eC2318f3dCF7e`
- Only USDC is supported.
**ProcessPayment fails with "Wallet does not have a USDC balance":**
- The wallet has no USDC on the specified chain.
- Fund via Circle faucet (testnet): https://faucet.circle.com/
- For mainnet: the end user must fund the wallet directly.
**Coinbase: "Delegated signing grant is not active":**
- The end user has not completed the delegation step.
- Redirect them to the `redirectUrl` returned during instrument creation (Coinbase Hub).
- They must log in and grant permissions to the wallet.
**Coinbase: "Delegated signing is not enabled":**
- The Coinbase CDP project does not have delegated signing enabled.
- Go to portal.cdp.coinbase.com > Project > Wallet > Embedded Wallets > Policies > Enable Delegated signing.
**Stripe Privy: "Privy credentials are invalid":**
- The App ID or App Secret stored in the credential provider is wrong.
- Verify in Privy Dashboard that the credentials match.
- Recreate the credential provider with the correct values.
**Stripe Privy: "Privy appId is invalid or missing":**
- The `appId` in the credential provider configuration is incorrect.
- Check Privy Dashboard for the correct App ID.
**Stripe Privy: "Privy signing key is invalid or expired":**
- The Authorization Private Key or Authorization ID is invalid or has expired.
- Generate a new P-256 key pair in Privy Dashboard > Wallet Infrastructure > Authorization.
- Remember to strip the `wallet-auth:` prefix from the private key.
- Update the credential provider with the new key.
**Stripe Privy: "Wallet policy denied the transaction":**
- A wallet policy configured in Privy is blocking the transaction.
- Review wallet policy settings in Privy Dashboard.
- Check if the transaction amount, recipient, or frequency exceeds policy limits.
**Stripe Privy: "The linked account data is invalid":**
- The email or phone number used in `linkedAccounts` when creating the instrument is malformed.
- Verify the email format is valid.
**Stripe Privy: "Rate limited by Privy":**
- The Privy API is rate limiting your requests.
- Back off and retry. Check Privy's rate limits documentation.
**ProcessPayment fails with "Payment amount exceeds maximum":**
- The x402 challenge requests more than the maximum allowed per transaction.
- Check the amount in the challenge and verify your session budget allows it.
**ProcessPayment fails with "Rate exceeded":**
- Too many API calls. Back off and retry after a few seconds.
**Coinbase: "Delegation not completed":**
- The end user has not granted the agent permission to spend from their wallet.
- Visit the `redirectUrl` returned during instrument creation, log in, and grant permissions.
**Stripe Privy: "Delegation not completed":**
- The agent auth key has not been added as a signer on the embedded wallet.
- Set up a frontend using the Privy frontend SDK (https://github.com/privy-io/aws-agentcore-sdk), log in with the end user email provided during setup, and approve delegation for the wallet.
## Security Considerations
- **Credential rotation**: Rotate payment provider credentials periodically. Recreate the credential provider with updated values.
- **Budget/spend limits**: Use Payment Session `expiryTimeInMinutes` and per-session budget controls to prevent runaway payments.
- **Audit logging**: Verify CloudTrail is logging all `bedrock-agentcore` API calls, especially `ProcessPayment`. For production, set up a CloudWatch alarm for failed payment attempts as a potential abuse indicator.
- **SSRF mitigation**: The `x402_fetch` tool enforces HTTPS-only and blocks private IP ranges to prevent fetching internal endpoints.
- **Least privilege**: The IAM service role should only have the minimum permissions required (token-vault, workload-identity, secrets access).
- **Session expiry**: Keep payment sessions short-lived (60 minutes or less). Create fresh sessions per user interaction rather than reusing long-lived ones.
- **Encryption in transit**: All payment requests must use HTTPS. The `x402_fetch` tool rejects non-HTTPS URLs.
For comprehensive security guidance, see the [AgentCore Security documentation](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/security.html).
## How x402 Payment Works (End-to-End)
```
Agent calls x402_fetch("https://paid-api.example.com/data")
│
├─ 1. HTTP GET → 402 Payment Required
│ Body: {"x402Version": 1, "accepts": [{"scheme": "exact", "network": "base-sepolia", ...}]}
│
├─ 2. Extract x402 challenge
│
├─ 3. ProcessPayment(paymentManagerArn, instrumentId, sessionId, challenge)
│ → Returns signed proof (signature + authorization)
│
├─ 4. Build payment header (X-PAYMENT for v1, PAYMENT-SIGNATURE for v2)
│
├─ 5. Retry with payment header (fresh HTTP client, no cookies)
│ → 200 OK + paid content
│
└─ 6. Return content to agent
```
## Supported Networks
Two concepts: **network** (blockchain family, used when creating instruments) and **chain** (specific chain, used in x402 challenges and balance queries).
**Networks (for instrument creation):**
| Network | Instrument Value | Providers |
|---|---|---|
| Ethereum (includes Base, Base Sepolia) | `ETHEREUM` | Coinbase, Stripe |
| Solana (includes Solana Devnet) | `SOLANA` | Coinbase, Stripe |
**Chains (in x402 challenges and balance queries):**
| Chain | Identifier (x402) | Balance API value | Type | Provider |
|---|---|---|---|---|
| Base Sepolia | `base-sepolia` or `eip155:84532` | `BASE_SEPOLIA` | Testnet | Coinbase |
| Base | `eip155:8453` | `BASE` | Mainnet | Coinbase |
| Ethereum Mainnet | `eip155:1` | `ETHEREUM` | Mainnet | Coinbase, Stripe |
| Solana Mainnet | `solana:5eykt4UsFv8P8NJdTREpY1vzqKqZKvdp` | `SOLANA` | Mainnet | Coinbase, Stripe |
| Solana Devnet | `solana-devnet` | `SOLANA_DEVNET` | Testnet | Stripe |
For testing, start with **Base Sepolia** (network: `ETHEREUM`, chain: `BASE_SEPOLIA`) — free testnet tokens from https://faucet.circle.com/.
## Quality criteria
- CLI is installed via `npm install -g @aws/agentcore`, not pip
- Control plane (credential provider, manager, connector) is provisioned via the CLI; the manager non-interactively, only the connector's credential entry involves the developer
- Data plane (instrument, session) is created via the SDK script, not hand-written code
- Payments are wired via the framework-agnostic `x402_fetch` tool, so any framework works
- Credentials never pass through the agent or the chat
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# request-headers
Pass custom HTTP headers from the caller through to your agent's invocation code.
## When to use
- You need to pass a tenant ID, correlation ID, or feature flag from your app to your agent
- You're implementing a protocol that requires specific headers (A2A, MCP, vendor-specific)
- You want OpenTelemetry baggage or trace headers to propagate from the caller
- You tried adding a header to the request and your agent code never sees it
- You're integrating with an external system that uses idempotency keys or similar headers
## The default: most headers are stripped
AgentCore Runtime strips all incoming headers from the request before it reaches your agent code **except**:
- `Authorization` — always passed through
- Any header matching `X-Amzn-Bedrock-AgentCore-Runtime-Custom-*` — this is the reserved prefix for custom headers
Anything else — `X-Tenant-Id`, `X-Correlation-Id`, `traceparent`, `A2A-Version`, `Idempotency-Key`, whatever — will not appear in your invocation context unless you explicitly add it to the runtime's request header allowlist.
This is an intentional security boundary: the runtime doesn't forward arbitrary caller-supplied headers by default. It's also the #1 reason developers ask "why can't my agent see the header I'm sending?"
## Two ways to pass custom data
### Option 1: Use the reserved prefix
Rename headers at the caller to use the `X-Amzn-Bedrock-AgentCore-Runtime-Custom-` prefix. These pass through without any runtime configuration change.
```
# Caller sends:
X-Amzn-Bedrock-AgentCore-Runtime-Custom-Tenant-Id: acme-corp
X-Amzn-Bedrock-AgentCore-Runtime-Custom-Correlation-Id: 8b2e3d...
# Agent code sees the same headers in the invocation context
```
This is the simplest option for headers you control end-to-end (your app, your agent).
### Option 2: Add headers to the request header allowlist
If the header names are fixed by a protocol or external system (A2A requires `A2A-Version` and `A2A-Extensions`; OpenTelemetry uses `traceparent` and `baggage`; some APIs use `Idempotency-Key`), you can't rename them. Configure the runtime to allow them explicitly.
**Edit `agentcore/agentcore.json`** and add `requestHeaderAllowlist` to the runtime entry:
```json
{
"runtimes": [
{
"name": "MyAgent",
"requestHeaderAllowlist": [
"X-Amzn-Bedrock-AgentCore-Runtime-Custom-X-Tenant-Id",
"X-Amzn-Bedrock-AgentCore-Runtime-Custom-A2A-Version"
]
}
]
}
```
Then `agentcore deploy`. The `$schema` URL at the top of the file (`https://schema.agentcore.aws.dev/v1/agentcore.json`) gives IDE autocomplete and validation for every field.
**CLI shortcut** — `agentcore add agent --request-header-allowlist "X-Tenant-Id,A2A-Version"` writes the same array. **Important:** the CLI auto-prefixes entries with `X-Amzn-Bedrock-AgentCore-Runtime-Custom-` as they land in `agentcore.json`. If you're editing the JSON by hand, write the prefixed form directly. If you're using the CLI, pass the short name and let the CLI add the prefix.
`Authorization` passes through by default and doesn't need to be in the allowlist.
### Constraints
- **Maximum 20 headers** in the allowlist (including `Authorization` if you include it explicitly)
- **Header name length:** up to 256 characters
- **Header value size:** up to 4 KB per header
- **Names are case-sensitive** — list them exactly as they'll be sent
- **Changes take effect after the next deploy** of the runtime
If you hit the 20-header cap, combine related data into one JSON-encoded header rather than using many separate ones.
## Common use cases
### Multi-tenancy
```
Caller: X-Tenant-Id: acme-corp
Agent code: reads tenant from the header, scopes memory/data/tools per tenant
```
Add `X-Tenant-Id` to the allowlist. The agent can then isolate memory namespaces, database queries, and tool-call authorization per tenant.
### Distributed tracing propagation
```
Caller: traceparent: 00-<trace-id>-<span-id>-01
baggage: userId=alice,env=prod
Agent code: uses OTel SDK to continue the parent trace
```
Add `traceparent` and `baggage` to the allowlist. Your OTel SDK instrumentation will pick them up automatically and produce spans connected to the caller's trace.
### A2A protocol compliance
```
Caller: A2A-Version: 1.0
A2A-Extensions: x-capability-foo
Agent code: branches behavior based on protocol version
```
A2A v1.0 requires these headers. Add both to the allowlist; A2A v0.3 doesn't need either.
### Idempotency keys
```
Caller: Idempotency-Key: 7f3a...
Agent code: deduplicates or caches based on the key
```
For agents that call external APIs with idempotency, propagating the caller's key through to the agent's outbound calls avoids duplicate side effects on retry.
## Reading the headers in agent code
Headers arrive in the runtime's `context` object passed to your invocation handler. The exact accessor depends on the framework — check the bedrock-agentcore SDK docs for your language. In Python:
```python
@app.entrypoint
def invoke(payload, context):
tenant = context.headers.get("X-Tenant-Id")
correlation_id = context.headers.get("X-Correlation-Id")
# ... use as needed
```
Headers that weren't in the allowlist will be absent (not empty string) from the context.
## What won't work
- **Sending headers without configuring the allowlist** — anything outside the default pass-through set is silently dropped. Your agent code won't see the header, and there's no error. Check the runtime's `requestHeaderConfiguration` if a header you expect to see isn't arriving.
- **Using this for secrets** — 4 KB values and the allowlist configuration are designed for metadata, not credentials. Use the AgentCore Identity credential provider for API keys, OAuth tokens, and secrets. See `agents-connect` Path D.
- **Dynamic headers** — the allowlist is static runtime configuration. You can't vary it per-request.
## Troubleshooting
**"My agent doesn't see the header I'm sending"**
Check (in order): (1) Is the header in the allowlist? (2) Is the spelling an exact match including case? (3) Did you redeploy the runtime after updating the allowlist? (4) Is the caller actually sending the header — `curl -v` or equivalent network inspection.
**"I hit the 20-header limit"**
Consolidate related data into a single JSON-encoded header. For example, instead of `X-Region`, `X-Environment`, `X-Service-Name` as three separate headers, use `X-Context: {"region":"us-west-2","env":"prod","service":"billing"}`.
**"Allowlist update didn't take effect"**
Redeploy the runtime. The header allowlist is config that applies on the next `agentcore deploy`, not immediately after editing `agentcore.json`.
## Output
- Decision on prefix vs. allowlist approach
- CLI command to update the allowlist if needed
- Agent code pattern for reading the headers
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# teardown
Remove individual resources from your project or tear down the entire deployment.
## When to use
- You want to remove a gateway, memory, credential, evaluator, or other resource from your project
- You want to delete a deployed agent and clean up all AWS resources
- You're iterating in a sandbox account and want to start fresh
- You need to remove a resource that's stuck or no longer needed
## Process
### Removing individual resources from your project
Use `agentcore remove` to remove a resource from `agentcore.json`. This marks the resource for deletion — the actual AWS resource is removed on the next `agentcore deploy`.
```bash
# Remove a memory resource
agentcore remove memory --name MyMemory
# Remove a gateway target
agentcore remove gateway-target --name WeatherTools --gateway MyGateway
# Remove a gateway (remove all its targets first)
agentcore remove gateway --name MyGateway
# Remove a credential
agentcore remove credential --name MyAPIKey
# Remove an evaluator
agentcore remove evaluator --name ResponseQuality
# Remove an online eval config
agentcore remove online-eval --name production_monitor
# Remove a policy
agentcore remove policy --name SpendingLimit --engine MyPolicyEngine
# Remove a policy engine (remove all its policies first)
agentcore remove policy-engine --name MyPolicyEngine
```
After removing, deploy to apply the changes:
```bash
agentcore deploy -y
```
Check what's pending removal before deploying:
```bash
agentcore status --state pending-removal
```
### Removing an agent from a multi-agent project
If your project has multiple agents (runtimes), you can remove one:
```bash
agentcore remove agent --name SecondAgent
agentcore deploy -y
```
This deletes the agent's runtime, endpoint, and associated resources from AWS. The agent's code in `app/<AgentName>/` is not deleted — remove it manually if you no longer need it.
### Tearing down the entire deployment
To remove all deployed AWS resources for a project:
```bash
# Preview what will be destroyed
agentcore deploy --diff
# Destroy all resources
npx cdk destroy --app "npx ts-node agentcore/cdk/bin/cdk.ts" --force
```
Alternatively, delete the CloudFormation stack directly:
```bash
# Find the stack name
aws cloudformation list-stacks \
--stack-status-filter CREATE_COMPLETE UPDATE_COMPLETE \
--query "StackSummaries[?contains(StackName, '<ProjectName>')].StackName"
# Delete it
aws cloudformation delete-stack --stack-name <StackName>
# Wait for deletion to complete
aws cloudformation wait stack-delete-complete --stack-name <StackName>
```
### What gets deleted and what doesn't
| Resource | Deleted by `cdk destroy` | Notes |
|---|---|---|
| AgentCore Runtime(s) | ✅ | Includes all endpoints and versions |
| Memory resource(s) | ✅ | Memory data is deleted permanently |
| Gateway(s) and targets | ✅ | |
| Credentials | ✅ | Secrets Manager entries are removed |
| Policy engine(s) and policies | ✅ | |
| Evaluator definitions | ✅ | |
| Online eval configs | ✅ | |
| IAM roles | ✅ | Created by CDK |
| CloudWatch log groups | ❌ | Persist after deletion — delete manually if needed |
| ECR images (Container builds) | ❌ | Persist — delete the repository manually |
| CDK bootstrap stack | ❌ | Shared across projects — don't delete unless you're done with CDK entirely |
| Local project files | ❌ | `agentcore/`, `app/` — delete manually |
### Cleaning up CloudWatch log groups
Log groups persist after stack deletion. To clean them up:
```bash
# List AgentCore log groups
aws logs describe-log-groups \
--log-group-name-prefix /aws/bedrock-agentcore/ \
--query "logGroups[].logGroupName"
# Delete a specific log group
aws logs delete-log-group --log-group-name /aws/bedrock-agentcore/runtimes/<AGENT_ID>-DEFAULT
```
### Cleaning up ECR repositories (Container builds)
```bash
# List AgentCore ECR repositories
aws ecr describe-repositories \
--query "repositories[?contains(repositoryName, 'bedrock-agentcore')].repositoryName"
# Delete a repository and all its images
aws ecr delete-repository --repository-name <repo-name> --force
```
### Handling stuck resources
If a runtime is stuck in DELETING state for more than 30 minutes, see the "Runtime stuck in DELETING" section in `agents-debug`. The short version: don't keep retrying — open an AWS Support case with the runtime ARN and the original delete request ID from CloudTrail.
## Common issues
**"Can't remove gateway — targets still attached"**
Remove all gateway targets first, then remove the gateway:
```bash
agentcore remove gateway-target --name Target1 --gateway MyGateway
agentcore remove gateway-target --name Target2 --gateway MyGateway
agentcore remove gateway --name MyGateway
```
**"Can't remove policy engine — policies still attached"**
Remove all policies first, then remove the engine:
```bash
agentcore remove policy --name Policy1 --engine MyEngine
agentcore remove policy-engine --name MyEngine
```
**"Resource shows pending-removal but deploy doesn't delete it"**
Check `agentcore status --state pending-removal` and verify the resource is listed. If deploy completes without removing it, check the CDK output for errors — the deletion may have failed silently due to a dependency.
## Output
- CLI commands to remove the specific resource(s)
- Guidance on what persists after deletion and how to clean it up
- Warnings about irreversible data loss (memory data, credentials)
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# vpc
Configure your AgentCore agent to connect to private AWS resources inside a VPC.
## When to use
- Your agent needs to connect to an RDS database
- Your agent needs to call internal APIs not exposed to the internet
- You want to keep your agent's network traffic private
- VPC connectivity is configured but connections are timing out
## Input
`$ARGUMENTS` is optional:
```
/vpc # interactive — asks what you're connecting to
/vpc rds # RDS database connectivity
/vpc debug # diagnose VPC connectivity issues
```
## How AgentCore VPC connectivity works
When you configure VPC mode, AgentCore creates **Elastic Network Interfaces (ENIs)** in your VPC subnets. These ENIs give your agent a private IP address in your VPC, enabling it to reach private resources.
**Key facts:**
- VPC connectivity directly affects **outbound traffic** — ENIs route your agent's outbound calls through your VPC. For **inbound traffic**, you can optionally add an AgentCore VPC endpoint to keep API calls private via PrivateLink (this is separate from the `networkMode` setting).
- AgentCore creates ENIs via the service-linked role `AWSServiceRoleForBedrockAgentCoreNetwork` (auto-created on first VPC deployment)
- Subnets must be in **supported Availability Zones** — not all AZs are supported. The supported AZ list changes as AgentCore expands to new regions.
---
## Step 0: Verify CLI version
Run `agentcore --version`. This skill requires v0.9.0 or later. If the version is older, tell the developer to run `agentcore update` before proceeding.
---
## Step 1: Verify your subnets are in supported AZs
AgentCore only supports specific Availability Zone IDs per region. The supported AZ list changes as AgentCore expands — **always check the current docs** for the latest table.
Check your subnet's AZ ID:
```bash
# Check the AZ ID of your subnet
aws ec2 describe-subnets \
--subnet-ids subnet-12345678 \
--query 'Subnets[0].{AZ:AvailabilityZone,AZId:AvailabilityZoneId,SubnetId:SubnetId}'
```
**To find the current supported AZ IDs:** See the AgentCore VPC configuration guide: https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/runtime-vpc.html — look for the "Supported Availability Zones" section. The table lists AZ IDs (e.g., `use1-az1`, `usw2-az2`) per region — use AZ IDs, not AZ names, because AZ name-to-ID mappings differ across AWS accounts.
If your subnet is in an unsupported AZ, the deployment will fail. Use subnets in supported AZs.
**Best practice:** Use at least two subnets in different supported AZs for high availability.
---
## Step 2: Configure security groups
Security groups control what your agent can connect to. Configure them based on what you're connecting to.
### Connecting to RDS PostgreSQL
**AgentCore agent security group** (outbound rule):
```
Type: Custom TCP
Port: 5432
Destination: RDS security group ID (not CIDR)
```
**RDS security group** (inbound rule):
```
Type: PostgreSQL
Port: 5432
Source: AgentCore agent security group ID
```
```bash
# Create a security group for the agent
aws ec2 create-security-group \
--group-name agentcore-agent-sg \
--description "AgentCore agent security group" \
--vpc-id vpc-12345678
# Add outbound rule to reach RDS
aws ec2 authorize-security-group-egress \
--group-id sg-agent123 \
--protocol tcp \
--port 5432 \
--source-group sg-rds456
# Add inbound rule to RDS security group
aws ec2 authorize-security-group-ingress \
--group-id sg-rds456 \
--protocol tcp \
--port 5432 \
--source-group sg-agent123
```
### Connecting to internal APIs (HTTP/HTTPS)
**AgentCore agent security group** (outbound rules):
```
Type: HTTPS, Port: 443, Destination: API security group or CIDR
Type: HTTP, Port: 80, Destination: API security group or CIDR (if needed)
```
---
## Step 3: Configure the agent for VPC
### New project
```bash
agentcore create \
--name MyAgent \
--defaults \
--network-mode VPC \
--subnets subnet-abc123,subnet-def456 \
--security-groups sg-agent123
```
### Existing project
```bash
agentcore add agent \
--name MyAgent \
--network-mode VPC \
--subnets subnet-abc123,subnet-def456 \
--security-groups sg-agent123
```
Or edit `agentcore/agentcore.json` directly — add the `networkMode` and `networkConfig` fields to the runtime's entry:
```json
{
"runtimes": [
{
"name": "MyAgent",
"networkMode": "VPC",
"networkConfig": {
"subnets": ["subnet-abc123", "subnet-def456"],
"securityGroups": ["sg-agent123"]
}
}
]
}
```
The `$schema` URL at the top of `agentcore.json` (`https://schema.agentcore.aws.dev/v1/agentcore.json`) gives IDE autocomplete and validation for every field — including the subnet/security-group ID patterns.
### Deploy
```bash
agentcore deploy -y
```
---
## Internet access from VPC
> [!WARNING]
> Connecting AgentCore to a VPC does NOT provide internet access by default.
> Public subnets do NOT provide internet access for AgentCore ENIs.
> To reach the internet from VPC mode, you MUST use private subnets with a NAT gateway.
**Architecture for internet + VPC access:**
```
AgentCore agent (private subnet)
↓ outbound traffic
NAT Gateway (public subnet)
↓
Internet Gateway
↓
Internet
```
```bash
# Create NAT gateway in a public subnet
aws ec2 create-nat-gateway \
--subnet-id subnet-public123 \
--allocation-id eipalloc-12345678
# Update private subnet route table to use NAT gateway
aws ec2 create-route \
--route-table-id rtb-private123 \
--destination-cidr-block 0.0.0.0/0 \
--nat-gateway-id nat-12345678
```
---
## Fully private VPC (no internet)
If your VPC has no internet access, you need VPC endpoints for AWS services. These endpoints are **required** without internet access and **strongly recommended** even with a NAT gateway to avoid NAT gateway data processing charges:
```bash
# ECR Docker endpoint (required for container image pulls)
aws ec2 create-vpc-endpoint \
--vpc-id vpc-12345678 \
--service-name com.amazonaws.REGION.ecr.dkr \
--vpc-endpoint-type Interface \
--subnet-ids subnet-abc123 \
--security-group-ids sg-agent123
# ECR API endpoint (required for container image pulls)
aws ec2 create-vpc-endpoint \
--vpc-id vpc-12345678 \
--service-name com.amazonaws.REGION.ecr.api \
--vpc-endpoint-type Interface \
--subnet-ids subnet-abc123 \
--security-group-ids sg-agent123
# S3 Gateway endpoint (required — ECR stores image layers in S3)
# This is a free Gateway endpoint. Without it, ECR image refreshes
# route through NAT and incur data processing charges.
aws ec2 create-vpc-endpoint \
--vpc-id vpc-12345678 \
--service-name com.amazonaws.REGION.s3 \
--vpc-endpoint-type Gateway \
--route-table-ids rtb-private123
# CloudWatch Logs (required for agent logging)
aws ec2 create-vpc-endpoint \
--vpc-id vpc-12345678 \
--service-name com.amazonaws.REGION.logs \
--vpc-endpoint-type Interface \
--subnet-ids subnet-abc123 \
--security-group-ids sg-agent123
```
---
## Cold-start connectivity checklist
A common pattern: `UpdateAgentRuntime` returns READY, the network configuration looks right, but invocations return 502 or hang. Requests never reach your container. This almost always means a new VM can start but can't complete the work needed to be ready for traffic.
Cold-start VMs need outbound HTTPS (port 443) to these AWS service endpoints. In public or NAT-routed VPCs, a correctly configured NAT gateway covers all of them. In fully private VPCs, every one of these needs an interface VPC endpoint or gateway endpoint:
- `com.amazonaws.<region>.ecr.api` — pull image metadata
- `com.amazonaws.<region>.ecr.dkr` — pull container layers
- `com.amazonaws.<region>.s3` (Gateway endpoint) — ECR layers live in S3
- `com.amazonaws.<region>.logs` — emit CloudWatch logs
- `com.amazonaws.<region>.monitoring` — emit CloudWatch metrics
- `com.amazonaws.<region>.sts` — assume the execution role
Plus whichever endpoints your agent's tools and dependencies need (Bedrock, DynamoDB, Secrets Manager, etc.).
### Security group outbound rule
The agent's security group needs an outbound rule to reach 443 on each VPC endpoint's prefix list, or `0.0.0.0/0` if the endpoints are reachable directly:
```bash
aws ec2 authorize-security-group-egress \
--group-id sg-agent123 \
--protocol tcp \
--port 443 \
--cidr 0.0.0.0/0
```
If you scope egress more tightly (to specific endpoint prefix lists or CIDR blocks), double-check that every endpoint above is covered.
### NACLs — the gotcha
Network ACLs are **stateless**. A security group allowing outbound 443 implicitly allows the response traffic. A NACL does not.
If your subnet uses a restrictive NACL, you need both directions explicitly:
- **Outbound:** allow TCP 443 to the destination
- **Inbound:** allow **ephemeral ports 1024–65535** (TCP) from the destination — these are the return-traffic ports
Forgetting the inbound ephemeral-port rule produces the exact symptom of "connection works sometimes, hangs other times" because TCP handshakes succeed (SYN goes out, SYN-ACK comes back on low port ranges) but the actual data response on an ephemeral port gets dropped.
### Transit Gateway and custom egress
If your subnet routes outbound through a Transit Gateway to a central firewall, NAT, or network virtualization layer, the TGW attachment and downstream must have a working route to the internet (or to each VPC endpoint individually).
Symptoms of a missing TGW route:
- Invocations hang for the full client-side timeout (~300 seconds for default Lambda clients)
- No 502, no `ConnectionClosedError` — the request just doesn't come back
- `ping` from a test EC2 in the same subnet/SG works, but actual invocations don't
- Warm environments (already initialized, so already have all their egress done) succeed, new cold starts fail
The test-from-an-EC2 pattern is useful here: launch a t3.micro in the same subnet with the same security group, and try `curl https://s3.<region>.amazonaws.com`, `curl https://ecr.<region>.amazonaws.com`, etc. If any of those hang or fail, the agent will fail to cold-start too.
### Expect higher cold-start time in VPC mode
VPC mode adds ENI attachment and setup time to cold start on top of container image pull and application startup. First invocations in a freshly-configured VPC are noticeably slower than in public mode.
Mitigation is the same as for all cold-start latency: reuse sessions, keep the image lean, defer heavy initialization. See `agents-harden` Initialization time section.
---
## Troubleshooting
**Connection timeouts to RDS or internal APIs:**
1. Verify security group rules — outbound from agent SG, inbound on target SG
2. Check route tables — private subnet must route to NAT gateway (for internet) or have direct routes to targets
3. Verify DNS resolution is enabled in the VPC: `aws ec2 describe-vpc-attribute --vpc-id vpc-12345678 --attribute enableDnsSupport`
**"Unsupported Availability Zone" error during deploy:**
Your subnet is in an AZ that AgentCore doesn't support. Check the AZ ID (not the AZ name) and use a subnet in a supported AZ.
**Agent can't reach internet after VPC configuration:**
You're using a public subnet or missing a NAT gateway. AgentCore ENIs in public subnets don't get internet access. Use private subnets with a NAT gateway.
**"AccessDenied" when using VPC endpoints:**
The execution role is missing permissions for the service behind the VPC endpoint. Check the endpoint's resource policy and the execution role's IAM policy.
**Code Interpreter timeouts calling public endpoints:**
Code Interpreter also needs VPC configuration if your agent is in a VPC. Configure it with the same subnets and a NAT gateway for internet access.
**DNS resolution failures:**
Enable DNS resolution and DNS hostnames in your VPC:
```bash
aws ec2 modify-vpc-attribute --vpc-id vpc-12345678 --enable-dns-support
aws ec2 modify-vpc-attribute --vpc-id vpc-12345678 --enable-dns-hostnames
```
## Output
- Subnet AZ validation results
- Security group rules for the specific target (RDS, internal API, etc.)
- CLI commands to configure VPC mode
- NAT gateway setup if internet access is needed
- VPC endpoint list for fully private deployments
## Quality criteria
- Subnet AZ IDs are validated against supported AZs (not AZ names — names vary by account)
- Security group rules cover both directions (agent outbound + target inbound)
- NAT gateway is recommended for internet access (not public subnets — AgentCore ENIs don't get public IPs)
- VPC endpoint list is complete for fully private deployments
- The developer understands that `networkMode: VPC` primarily affects outbound traffic
plugins/aws-agents/skills/agents-build/scripts/setup_payment_user.py
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#!/usr/bin/env python3
"""Provision per-user AgentCore Payments data-plane resources (instrument + optional session).
Control-plane (manager/connector/credential provider) is created by the AgentCore CLI.
This script uses the AgentCore SDK for the data plane:
- one payment instrument (wallet) per end user
- optionally one budget-bounded payment session
Usage:
python setup_payment_user.py --user-id alice --email alice@example.com [--budget 5] \
[--manager-arn ...] [--connector-id ...] [--region us-east-1] [--network ETHEREUM]
Manager ARN / connector ID are auto-read from agentcore/.cli/deployed-state.json if not passed.
"""
import argparse
import json
import os
import sys
from pathlib import Path
from bedrock_agentcore.payments import PaymentManager
def _from_deployed_state():
"""Best-effort: read manager ARN + connector ID from the CLI's deployed state.
CLI 0.20.x writes targets.<target>.resources.payments[]; older shapes used a
top-level payments[]. Handle both.
"""
path = Path("agentcore/.cli/deployed-state.json")
if not path.exists():
return None, None
try:
data = json.loads(path.read_text())
payments = None
targets = data.get("targets") or {}
target = targets.get("default") or (next(iter(targets.values()), {}) if targets else {})
if isinstance(target, dict):
payments = (target.get("resources") or {}).get("payments")
if not payments:
payments = data.get("payments") # legacy/top-level fallback
if not payments:
return None, None
pay = payments[0]
connectors = pay.get("connectors") or []
return pay.get("managerArn"), (connectors[0].get("connectorId") if connectors else None)
except Exception:
return None, None
def main():
ap = argparse.ArgumentParser(description="Provision a per-user AgentCore Payments instrument")
ap.add_argument("--user-id", required=True, help="Stable end-user identifier")
ap.add_argument("--email", required=True, help="End-user email (linked to the wallet; required for delegation)")
ap.add_argument("--budget", default=None, help="Optional session spend cap in USD, e.g. 5")
ap.add_argument("--expiry-minutes", type=int, default=60, help="Session expiry, 15-480")
ap.add_argument("--network", default="ETHEREUM", help="Wallet network family: ETHEREUM or SOLANA")
ap.add_argument("--manager-arn", default=os.environ.get("PAYMENT_MANAGER_ARN"))
ap.add_argument("--connector-id", default=os.environ.get("PAYMENT_CONNECTOR_ID"))
ap.add_argument("--region", default=os.environ.get("AWS_REGION", "us-east-1"))
args = ap.parse_args()
manager_arn, connector_id = args.manager_arn, args.connector_id
if not manager_arn or not connector_id:
ds_arn, ds_conn = _from_deployed_state()
manager_arn = manager_arn or ds_arn
connector_id = connector_id or ds_conn
if not manager_arn or not connector_id:
sys.exit("Could not resolve manager ARN / connector ID. Pass --manager-arn and --connector-id, "
"or run from the project dir with agentcore/.cli/deployed-state.json present.")
manager = PaymentManager(payment_manager_arn=manager_arn, region_name=args.region)
# Data plane: per-user instrument (wallet). Email -> linkedAccounts.
instrument = manager.create_payment_instrument(
user_id=args.user_id,
payment_connector_id=connector_id,
payment_instrument_type="EMBEDDED_CRYPTO_WALLET",
payment_instrument_details={
"embeddedCryptoWallet": {
"network": args.network,
"linkedAccounts": [{"email": {"emailAddress": args.email}}],
}
},
)
instrument_id = instrument["paymentInstrumentId"]
wallet = instrument.get("paymentInstrumentDetails", {}).get("embeddedCryptoWallet", {})
wallet_address = wallet.get("walletAddress")
redirect_url = wallet.get("redirectUrl") # Coinbase delegation URL; None for Privy
# Data plane: optional budget-bounded session. NOTE: cap key is "value", not "amount".
session_id = None
if args.budget:
session = manager.create_payment_session(
user_id=args.user_id,
expiry_time_in_minutes=args.expiry_minutes,
limits={"maxSpendAmount": {"value": str(args.budget), "currency": "USD"}}, # cap currency is USD
)
session_id = session["paymentSessionId"]
print("Instrument ID :", instrument_id)
print("Wallet address:", wallet_address)
print("Session ID :", session_id or "(none - use `agentcore invoke --auto-session`)")
print("\nExport these for the x402 tool (Step 8):")
print(f" export PAYMENT_MANAGER_ARN={manager_arn}")
print(f" export PAYMENT_INSTRUMENT_ID={instrument_id}")
if session_id:
print(f" export PAYMENT_SESSION_ID={session_id}")
print(f" export PAYMENT_USER_ID={args.user_id}")
print(f" export AWS_REGION={args.region}")
print("\nOne-time per wallet:")
if redirect_url:
print(f" 1. Delegation (Coinbase): visit {redirect_url}, log in, grant access to {wallet_address}")
else:
print(" 1. Delegation (Privy): approve delegation via the Privy frontend SDK")
print(f" 2. Funding: send testnet USDC to {wallet_address} via https://faucet.circle.com/ (Base Sepolia)")
if __name__ == "__main__":
main()
plugins/aws-agents/skills/agents-build/scripts/x402_payment_tool.py
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"""Framework-agnostic x402 payment tool for AgentCore Payments.
Copy this file into your agent project and register `x402_fetch` as a tool in
whatever framework you use (Strands, LangGraph, OpenAI Agents SDK, etc.). The
core logic is pure Python with no framework dependency.
Flow:
request -> detect 402 -> PaymentManager.generate_payment_header (the SDK
validates the 402, selects the network, processes the payment, and builds the
version-aware v1/v2 proof header) -> retry with a fresh client.
Transient settlement: the SDK builds a valid header, but the merchant's
on-chain settlement is occasionally transient and the paid retry still returns
402. The SDK does not make the merchant HTTP call (it only builds the header),
so it cannot retry that — this tool re-runs the settle+replay flow up to
X402_MAX_PAYMENT_ATTEMPTS times before giving up. A single idempotency token
(client_token) is reused across all attempts of one fetch, so ProcessPayment is
idempotent: every retry replays the SAME on-chain authorization/nonce. That
recovers a not-yet-settled transient failure, and if the merchant actually did
settle but still returned 402, the replay simply reverts on-chain (nonce already
used) rather than charging the user a second time.
Control-plane resources (payment manager/connector) are created by the AgentCore
CLI; the per-user instrument/session are created by setup_payment_user.py. This
tool only consumes them, via these environment variables:
PAYMENT_MANAGER_ARN payment manager ARN (from deployed-state.json)
PAYMENT_INSTRUMENT_ID per-user wallet ID (from setup_payment_user.py)
PAYMENT_SESSION_ID per-conversation session (from setup_payment_user.py)
PAYMENT_USER_ID end-user identity (required)
AWS_REGION region (default us-west-2)
X402_MAX_PAYMENT_ATTEMPTS transient-402 retry cap (default 5)
"""
import ipaddress
import json
import os
import socket
import uuid
from urllib.parse import urlparse
import httpx
from bedrock_agentcore.payments import PaymentManager
PAYMENT_MANAGER_ARN = os.getenv("PAYMENT_MANAGER_ARN")
PAYMENT_INSTRUMENT_ID = os.getenv("PAYMENT_INSTRUMENT_ID")
PAYMENT_SESSION_ID = os.getenv("PAYMENT_SESSION_ID")
PAYMENT_USER_ID = os.environ.get("PAYMENT_USER_ID") # required — no insecure default
REGION = os.getenv("AWS_REGION", "us-west-2")
# Transient on-chain settlement can leave the paid retry at 402 even though the
# header was valid; re-settle (fresh header + idempotency token) up to this many times.
MAX_PAYMENT_ATTEMPTS = int(os.getenv("X402_MAX_PAYMENT_ATTEMPTS", "5"))
# AgentCore Payments data-plane client (SDK). Created when configured.
_manager = PaymentManager(payment_manager_arn=PAYMENT_MANAGER_ARN, region_name=REGION) if PAYMENT_MANAGER_ARN else None
def _validate_url(url):
"""Return an error string if the URL is not HTTPS or targets a private/internal IP."""
parsed = urlparse(url)
if parsed.scheme != "https":
return "Only HTTPS URLs are supported for payment requests"
try:
for _family, _, _, _, sockaddr in socket.getaddrinfo(parsed.hostname, parsed.port or 443):
ip = ipaddress.ip_address(sockaddr[0])
if ip.is_private or ip.is_loopback or ip.is_link_local:
return "Cannot fetch private/internal network addresses"
except socket.gaierror:
return "Cannot resolve hostname"
return None
def _settle_and_retry(url, method, response, client_token):
"""Build the payment header from a 402 response via the SDK, then replay the request.
The SDK's generate_payment_header does the whole settle workflow (validate the
402, pick the network, ProcessPayment, build the v1 `X-PAYMENT` / v2
`PAYMENT-SIGNATURE` proof) and returns {header_name: header_value}. We pass a
STABLE client_token (the same one for every attempt of a single fetch) so
ProcessPayment is idempotent — each retry replays the same authorization/nonce
and can never double-charge.
Returns the retry httpx.Response. Raises on a header-generation failure.
"""
payment_header = _manager.generate_payment_header(
payment_instrument_id=PAYMENT_INSTRUMENT_ID,
payment_session_id=PAYMENT_SESSION_ID,
user_id=PAYMENT_USER_ID,
client_token=client_token,
payment_required_request={
"statusCode": response.status_code,
"headers": dict(response.headers),
"body": response.text,
},
)
# Retry with a FRESH client so cookies from the 402 response don't contaminate it.
with httpx.Client(verify=True) as client:
return client.request(method, url, headers=payment_header, timeout=30)
def x402_fetch(url, method="GET"):
"""Fetch a URL, automatically settling any x402 402 Payment Required response.
Returns a JSON string with status_code, body, and (on payment) payment_made.
"""
url_error = _validate_url(url)
if url_error:
return json.dumps({"error": url_error})
if not PAYMENT_USER_ID:
return json.dumps({"error": "PAYMENT_USER_ID environment variable is required"})
response = httpx.request(method, url, timeout=30)
if response.status_code != 402:
return json.dumps({"status_code": response.status_code, "body": response.text})
if not _manager:
return json.dumps({
"status_code": 402,
"error": "No payment configuration. Set PAYMENT_MANAGER_ARN.",
"body": response.text,
})
# One idempotency token for the whole fetch: every retry replays the SAME
# authorization/nonce, so a transient 402 can be re-settled without ever double-charging.
client_token = str(uuid.uuid4())
for attempt in range(1, MAX_PAYMENT_ATTEMPTS + 1):
try:
retry_response = _settle_and_retry(url, method, response, client_token)
except Exception as e: # noqa: BLE001 - surface any payment failure (incl. typed SDK errors) to the agent
return json.dumps({"status_code": 402, "error": f"Payment header generation failed: {e}"})
if retry_response.status_code != 402:
# Success (2xx) or a non-transient error — return it; payment_made reflects the actual status.
return json.dumps({
"status_code": retry_response.status_code,
"body": retry_response.text,
"payment_made": 200 <= retry_response.status_code < 300,
"payment_attempts": attempt,
})
# Transient post-payment 402 — retry with the same idempotency token (same
# authorization/nonce), giving settlement another chance without double-charging.
response = retry_response
return json.dumps({
"status_code": 402,
"error": f"Paid and retried {MAX_PAYMENT_ATTEMPTS} times but the merchant still returned 402 "
"(transient on-chain settlement). Try again shortly.",
"body": response.text,
"payment_made": False,
"payment_attempts": MAX_PAYMENT_ATTEMPTS,
})
plugins/aws-agents/skills/agents-connect/SKILL.md
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---
name: agents-connect
description: >
Use when connecting your agent to external APIs, tools, or services via
Gateway, or restricting tool access with Cedar policies. Handles gateway
setup, target types, outbound auth (OAuth, API key, IAM), credentials,
and Cedar policy authoring. Triggers on: "connect to API", "add gateway",
"connect to MCP server", "Lambda tools", "OpenAPI", "gateway target",
"Cedar policy", "restrict tools", "policy engine", "gateway auth error",
"store API key", "outbound credential", "env var API key", "API key None
after deploy", "credential not available after deploy",
"should this be a gateway target", "give my agent tools",
"add tools to agent".
Not for inbound auth (who can call your agent) — use agents-harden.
Not for debugging agent behavior — use agents-debug.
Not for VPC networking errors (agent can't reach APIs due to VPC) — use
agents-build. Not for creating or hosting a new MCP server project — use
agents-get-started.
allowed-tools: Read Grep Glob Bash
metadata:
type: skill
version: "1.0.0"
author: aws-agentcore
requires-cli: ">=0.9.0"
---
# connect
Give your AgentCore agent access to external APIs, tools, and services via the AgentCore Gateway — and control what it can access with Cedar policies.
## When to use
- You want your agent to call an external API or MCP server
- You want to expose Lambda functions as agent tools
- You have an OpenAPI spec you want to turn into agent tools
- Your agent needs credentials to call an external service
- You want to restrict which tools your agent can call (Cedar policies)
- You want role-based or amount-based access control on tool calls
- A gateway connection, tool call, or policy authorization is failing
For adding Cedar policies to control tool access, load [`references/policy.md`](references/policy.md).
## Input
`$ARGUMENTS` is optional:
```
/connect # interactive — asks what you're connecting to
/connect mcp # MCP server setup
/connect lambda # Lambda function as tools
/connect openapi # OpenAPI schema as tools
/connect credential # Add a credential for outbound auth
```
## Process
### Step 0: Verify CLI version
Run `agentcore --version`. This skill requires v0.9.0 or later. If the version is older, tell the developer to run `agentcore update` before proceeding.
### Step 1: Read the project
Read `agentcore/agentcore.json` to understand:
- What framework the project uses
- What gateways and targets are already configured (in the `agentCoreGateways` array)
**If no project context:** Ask what they're trying to connect to and proceed with the appropriate pattern.
### Step 2: Identify what they're connecting to
Ask (or infer from `$ARGUMENTS`):
> "What are you connecting your agent to?
>
> 1. An external MCP server (e.g., a third-party tool provider)
> 2. A Lambda function you've written
> 3. An API with an OpenAPI spec
> 4. An AWS API Gateway REST API
> 5. An external service with no OpenAPI spec, MCP server, or Lambda in front of it — and you can't add one"
**Options 1–4 front the service as a Gateway target.** This is the default path: the gateway handles outbound auth via its credential providers (so the agent code never sees the secret), the tool becomes discoverable over MCP, and policy engines can authorize or deny calls at the edge. Pick the target type that matches the service.
**Option 5 is Path D** — register a credential and call the API directly from agent code. This is the fallback when fronting isn't practical; the skill walks through when it's appropriate and when it isn't.
---
## Default: prefer a Gateway target over direct API calls in code
Before jumping into paths, set expectations. Most "my agent needs to call X" requests land on a Gateway target — not on `httpx` inside the entrypoint.
**Why Gateway is the default:**
- **Credential injection at the edge.** Gateway's credential providers (OAuth, API key, IAM) attach auth to the outbound request. The agent code calls `session.call_tool(...)` — it never touches the secret. Agent code that does `client = openai.OpenAI(api_key=...)` is one leaked prompt / log line / traceback away from exfiltrating the key.
- **Discoverable tool catalog.** Tools are listed by the MCP server; the framework (Strands, LangGraph, etc.) binds them automatically. Adding a tool is an `agentcore add gateway-target` + redeploy, not a code change.
- **Policy enforcement.** Cedar policies can authorize or deny tool calls per principal, per tool, per argument value. This is impossible when tool calls are buried in `httpx.post(...)` inside agent code.
- **Semantic search.** Once the catalog has 20+ tools, `x_amz_bedrock_agentcore_search` selects the relevant ones per turn.
**When a direct API call in agent code is the right answer:**
| Situation | Why Gateway isn't right | What to do |
|---|---|---|
| Streaming/bidirectional protocol (SSE with live output, WebSockets, WebRTC, long-polling) | Gateway's MCP transport doesn't front those yet | Direct call, Path D |
| Latency hot path where the MCP hop is measurable and the trade-off is accepted | Extra network hop | Direct call, Path D, with measurement to back the decision |
| Vendor proprietary protocol / binary SDK | No HTTP surface for Gateway to front | Use the vendor SDK directly, Path D for any secrets |
| Calling another agent via A2A | A2A is HTTP-by-design and has its own auth model | [`agents-build/references/multi-agent.md`](../agents-build/references/multi-agent.md), not a Gateway target |
| AWS service SDK (S3, DynamoDB, SQS, etc.) the runtime already has IAM for | No auth value in fronting — adds hops | Direct boto3 call with the runtime's execution role |
For **every other case**, recommend a Gateway target. If the developer insists on a direct call, ask which of the five situations above applies. If none, steer them back to a Gateway target.
**Triage heuristic:**
- Service has an MCP server → Path A
- Service is a Lambda function you control → Path B
- Service has an OpenAPI spec (or you can generate one — FastAPI, ASP.NET, Spring, etc. generate OpenAPI automatically) → Path C
- Service is already fronted by API Gateway → Path C (`--type api-gateway`)
- None of the above and you can't add one → Path D
---
## What Gateway is — and what it isn't
Before choosing a target type, get the mental model right. Most Gateway confusion comes from having it flipped.
**Gateway hosts tools for your agent to call.** The direction is:
```
Your agent ───→ Gateway ───→ Lambda function / OpenAPI API / MCP server / Smithy model
(agent calls tool)
```
The agent is the client. The Gateway fronts a catalog of tools. Each tool is a Gateway target (Lambda, OpenAPI, MCP server, API Gateway, Smithy).
**Gateway is not an inbound reverse proxy for your agent.** If you're building an app that needs to invoke your agent, the app does not go through a Gateway. The direction is:
```
Your app ───→ AgentCore Runtime (direct invoke_agent_runtime call)
```
The app signs the invocation with IAM SigV4 or presents a JWT. See [`agents-build/references/integrate.md`](../agents-build/references/integrate.md) for the app-side patterns.
### When you're confused about which direction you need
Ask: **who is calling whom?**
- "My agent needs to look up weather data" → agent is calling a tool → **Gateway target** (this skill, Paths A/B/C)
- "My FastAPI app needs to call my agent" → app is calling the agent → **direct invocation** (not Gateway; use [`agents-build/references/integrate.md`](../agents-build/references/integrate.md))
- "My agent needs to fetch data from my FastAPI app" → agent is calling the app as a tool → **Gateway target** with the app exposed as an OpenAPI or REST target (Path C with your FastAPI's `/openapi.json`)
If you catch yourself configuring a Gateway target whose endpoint is `bedrock-agentcore.<region>.amazonaws.com` or pointing at your own runtime's URL, stop — you have the flow inverted.
### What target type fits your tool
| What the tool is | Target type | Notes |
|---|---|---|
| MCP server (third-party or your own) | `mcp-server` | Most common for MCP tool catalogs |
| AWS Lambda function you wrote | `lambda-function-arn` | Uses IAM auth automatically |
| HTTP API with an OpenAPI spec | `open-api-schema` | FastAPI's built-in `/openapi.json` works |
| AWS API Gateway REST API | `api-gateway` | For APIs already fronted by API Gateway |
| AWS service with a Smithy model | `smithy-model` | Direct AWS service integration |
Your tool doesn't naturally have an OpenAPI spec and isn't an MCP server or Lambda? Either wrap it in a Lambda (simplest), generate an OpenAPI spec for it (FastAPI does this automatically), or front it with API Gateway.
---
### Step 3: Navigate the auth matrix
**This is the most common source of errors.** The auth options depend on the target type, and the CLI exposes only a subset of what the API/SDK support.
| What you're connecting to | CLI `--type` | Outbound auth via CLI | Additional options via API/SDK |
|---|---|---|---|
| External MCP server | `mcp-server` | `none`, `oauth` (2LO only) | OAuth 3LO (`AUTHORIZATION_CODE`); IAM (SigV4) |
| Lambda function | `lambda-function-arn` | `none` (default — direct invoke via gateway role), `oauth` (2LO) for OAuth-protected downstreams | OAuth 3LO |
| OpenAPI spec | `open-api-schema` | `oauth` (2LO), `api-key` (required — no `none`) | OAuth 3LO |
| AWS API Gateway | `api-gateway` | `none`, `api-key` | IAM (`GATEWAY_IAM_ROLE`) |
| Smithy model | `smithy-model` | `oauth` (2LO) | IAM; OAuth 3LO |
**Two OAuth grant types, not one.** The CLI's `--outbound-auth oauth` only configures **2-legged OAuth** (client credentials / M2M). If the service requires **3-legged OAuth** (`AUTHORIZATION_CODE` grant, user-delegated access), there is no CLI flag — you must configure the target via boto3 / the AWS SDK. See the [CreateGatewayTarget docs](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway-building-adding-targets-authorization.html) for the `OAuthCredentialProvider` with `grantType: AUTHORIZATION_CODE` and `defaultReturnUrl`. 3LO applies to MCP, Lambda, OpenAPI, and Smithy targets. Call this out up front — developers who need 3LO will otherwise burn a round-trip trying CLI flags that don't exist.
**IAM (SigV4) for MCP servers** is configured via the AWS SDK/API (`CreateGatewayTarget` with `GATEWAY_IAM_ROLE` credential provider + `iamCredentialProvider.service`), not the CLI. It requires the MCP server to be hosted behind an AWS service that natively verifies SigV4: AgentCore Runtime, AgentCore Gateway, Amazon API Gateway, or Lambda Function URLs. ALB or direct EC2 endpoints do not verify SigV4 — use OAuth there instead. ([MCP server target auth strategies](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway-target-MCPservers.html#gateway-target-MCPservers-considerations))
**API key auth for MCP server targets is not supported at the API level** — not just a CLI gap. The [MCP server targets docs](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway-target-MCPservers.html#gateway-target-MCPservers-considerations) list only "No authorization, OAuth, and IAM" as supported authorization strategies for MCP targets. If the MCP server uses an API key (a common pattern for third-party MCP providers), handle it in agent code via Path D.
**Auth options change.** If the matrix above doesn't match what the CLI accepts, check the current CLI help (`agentcore add gateway-target --help`) and the AWS docs — auth support per target type evolves across releases. If the `awsknowledge` MCP server is available, search for "AgentCore CreateGatewayTarget" to get the current API parameters.
**CLI vs. API for gateway auth:** The CLI covers `none`, `oauth` (2LO), and `api-key`. For IAM (SigV4) and 3-legged OAuth, use boto3 directly — the examples are in the Path A section below. The general pattern: create the gateway and target via CLI, deploy, then apply the advanced auth config via boto3 if the CLI doesn't support it.
Tell the developer which auth option applies to their target type before generating any commands.
### When your gateway has many tools, let the model search for them
Once a gateway has more than a handful of tools — roughly 20+ — passing every tool definition to the model on every turn wastes tokens and degrades accuracy. The model does better when it sees only the tools relevant to the current request.
AgentCore Gateway has a built-in semantic search tool for exactly this. Your agent calls a single MCP tool named `x_amz_bedrock_agentcore_search` with a natural-language query, and the gateway returns the most relevant tools from its catalog. The agent then invokes the returned tools normally.
If a developer is considering building their own tool-selection layer with Bedrock Knowledge Bases, a vector store, or custom embeddings — stop them. The gateway already does this, evaluated against curated relevance criteria, with no infrastructure to manage.
Usage pattern (the agent calls this the same way it calls any other gateway tool):
```python
# Via the MCP client, as a tool call
result = await session.call_tool(
"x_amz_bedrock_agentcore_search",
arguments={"query": "find tools related to processing refunds"}
)
# result.content lists the most relevant tools — the agent then invokes them
```
The feature works with any target type (Lambda, OpenAPI, MCP, API Gateway, Smithy). Enable it per gateway — see the [Search for tools in your AgentCore gateway](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway-using-mcp-semantic-search.html) docs for the exact API surface and framework-specific client code.
Rule of thumb: if a gateway has more than 20 tools, recommend enabling semantic search. For smaller catalogs, passing all tools directly is still fine.
### Passing custom headers from the caller to the agent
If the developer needs callers to send custom HTTP headers (tenant IDs, correlation IDs, protocol-specific headers like `A2A-Version`, tracing headers, idempotency keys), the runtime's default is to strip most headers before they reach agent code. Load [`agents-build/references/request-headers.md`](../agents-build/references/request-headers.md) for the allowlist configuration and prefix pattern.
This is about inbound calls to your agent, not outbound calls to tools — but developers hit it often enough that it's worth mentioning here.
---
## Path A: MCP server
### Add a gateway (if none exists)
> [!WARNING]
> Never deploy a gateway without inbound authentication to production. A gateway with
> no authorizer exposes all connected tools (Lambda, MCP, OpenAPI) to any caller who
> knows the URL — functionally equivalent to --authorizer-type NONE on the runtime.
> Always use --authorizer-type CUSTOM_JWT or AWS_IAM for production gateways.
> The no-auth form (agentcore add gateway --name X) is for local testing only.
```bash
# Development (no inbound auth — for testing only)
agentcore add gateway --name MyGateway
# Production (JWT inbound auth)
agentcore add gateway \
--name MyGateway \
--authorizer-type CUSTOM_JWT \
--discovery-url https://your-idp.example.com/.well-known/openid-configuration \
--allowed-audience my-api \
--allowed-clients my-client-id
```
### Add the MCP server as a target
```bash
# No outbound auth (public MCP server)
agentcore add gateway-target \
--type mcp-server \
--name WeatherTools \
--endpoint https://mcp.example.com/mcp \
--gateway MyGateway
# OAuth outbound auth (2-legged — client credentials / M2M)
agentcore add gateway-target \
--type mcp-server \
--name WeatherTools \
--endpoint https://mcp.example.com/mcp \
--gateway MyGateway \
--outbound-auth oauth \
--oauth-client-id your-client-id \
--oauth-client-secret your-client-secret \
--oauth-discovery-url https://auth.example.com/.well-known/openid-configuration \
--oauth-scopes read,write
```
Note: The CLI `--outbound-auth` flag supports `oauth` (2LO / client credentials) or `none` for MCP servers.
- **3-legged OAuth (`AUTHORIZATION_CODE` grant)** — user-delegated access — is supported by the API but has no CLI path. Configure via boto3 `create_gateway_target` with `OAuthCredentialProvider.grantType = "AUTHORIZATION_CODE"` and `defaultReturnUrl`. See [Connecting to an OAuth-protected MCP server using Authorization Code flow](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway-target-MCPservers.html#gateway-target-MCPservers-auth-code-grant-flow).
- **IAM (SigV4)** for MCP servers hosted on AgentCore Runtime, another AgentCore Gateway, API Gateway, or Lambda Function URLs is configured via the AWS SDK/API (not the CLI) — use `CreateGatewayTarget` with `GATEWAY_IAM_ROLE` credential provider and an `iamCredentialProvider.service` value.
- **API key auth** is not supported for MCP server targets at the API level (the MCP target docs list only no-auth, OAuth, and IAM as strategies) — if the MCP server uses an API key, handle it in agent code directly (see Path D).
### Deploy and get the gateway URL
```bash
agentcore deploy -y
agentcore fetch access --name MyGateway
```
The gateway URL is injected as `AGENTCORE_GATEWAY_<NAME>_URL` after deploy.
### Generate gateway client code
**Framework-agnostic MCP client:**
```python
import os
import asyncio
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client
# Injected by AgentCore after deploy. Format: AGENTCORE_GATEWAY_<UPPERCASENAME>_URL
GATEWAY_URL = os.getenv("AGENTCORE_GATEWAY_MYGATEWAY_URL")
async def get_gateway_tools():
"""Discover tools from the gateway. Returns empty list if not deployed."""
if not GATEWAY_URL:
return []
async with streamablehttp_client(GATEWAY_URL) as (read, write, _):
async with ClientSession(read, write) as session:
await session.initialize()
result = await session.list_tools()
return result.tools
async def call_gateway_tool(tool_name: str, arguments: dict):
"""Call a specific tool through the gateway."""
if not GATEWAY_URL:
raise RuntimeError("Gateway not available in local dev — deploy first")
async with streamablehttp_client(GATEWAY_URL) as (read, write, _):
async with ClientSession(read, write) as session:
await session.initialize()
return await session.call_tool(tool_name, arguments)
```
**For Strands**, pass gateway tools directly to the agent:
```python
from mcp.client.streamable_http import streamablehttp_client
from mcp import ClientSession
from strands import Agent
from bedrock_agentcore.runtime import BedrockAgentCoreApp
from model.load import load_model # scaffolded by `agentcore create`
app = BedrockAgentCoreApp()
GATEWAY_URL = os.getenv("AGENTCORE_GATEWAY_MYGATEWAY_URL")
@app.entrypoint
def invoke(payload, context):
if not GATEWAY_URL:
# Local dev — run without gateway tools
agent = Agent(model=load_model())
return {"response": str(agent(payload.get("prompt", "")))}
# Deployed — discover and use gateway tools
tools = asyncio.run(get_gateway_tools())
agent = Agent(
model=load_model(),
tools=tools,
)
return {"response": str(agent(payload.get("prompt", "")))}
if __name__ == "__main__":
app.run()
```
**For LangGraph**, add gateway tools to the tool node:
```python
from langchain_mcp_adapters.client import MultiServerMCPClient
@app.entrypoint
def agent_invocation(payload, context):
if not GATEWAY_URL:
tools = []
else:
# Use LangChain MCP adapter to get tools as LangChain-compatible tools
client = MultiServerMCPClient({"gateway": {"url": GATEWAY_URL, "transport": "streamable_http"}})
tools = asyncio.run(client.get_tools())
llm_with_tools = llm.bind_tools(tools)
# ... rest of your LangGraph graph ...
```
---
## Path B: Lambda function as tools
```bash
agentcore add gateway-target \
--type lambda-function-arn \
--name MyTools \
--lambda-arn arn:aws:lambda:us-east-1:123456789012:function:my-tools \
--tool-schema-file tools.json \
--gateway MyGateway
```
The `tools.json` defines the tool schemas:
```json
{
"inlinePayload": [
{
"name": "get_weather",
"description": "Get current weather for a city",
"inputSchema": {
"type": "object",
"properties": {
"city": {"type": "string", "description": "City name"}
},
"required": ["city"]
}
}
]
}
```
**Auth:** Lambda targets use IAM role auth automatically — no `--outbound-auth` flag. The gateway's execution role needs `lambda:InvokeFunction` on the Lambda ARN.
Use the same MCP client code from Path A to call the tools.
---
## Path C: OpenAPI spec as tools
```bash
# From a local file (api-key auth)
agentcore add credential --name MyAPIKey --api-key sk-...
agentcore add gateway-target \
--type open-api-schema \
--name MyAPI \
--schema specs/api.json \
--gateway MyGateway \
--outbound-auth api-key \
--credential-name MyAPIKey
```
**Auth is required** for OpenAPI targets — either `oauth` (client credentials or authorization code) or `api-key`.
⚠️ **Security note:** `--api-key` appears in shell history. Two safer options:
1. **Interactive prompt (recommended):** run `agentcore add credential --name MyAPIKey --type api-key` without `--api-key` — the CLI will prompt, and the value goes straight into the credential provider (Secrets Manager-backed) without hitting your shell history.
2. **Edit `agentcore.json` + `.env.local` for local dev only:** if you need the credential to work under `agentcore dev`, put the value in `agentcore/.env.local` (gitignored). This file is read by the local dev server only — it is **not** uploaded to runtime on deploy. The deployed runtime gets the value from the credential provider.
Do **not** try to ship a credential to the deployed runtime via environment variables — AgentCore Runtime env vars are not vault-backed. Register the credential once with `agentcore add credential` and reference it by name in the gateway target or in code (Path D).
---
## Path D: Credentials for use in agent code
For calling APIs directly in agent code (not through a gateway target).
### Before you reach for Path D, check if it's actually the right path
Path D is the **fallback**, not the starting point. For most external services, a Gateway target (Paths A–C) is safer and less code. Before generating Path D code, confirm one of these applies:
- The service uses a streaming/bidirectional protocol Gateway doesn't front (SSE with live output, WebSockets, WebRTC)
- It's a measurably latency-critical hot path and the team has accepted the trade-off
- The client is a vendor binary SDK with no HTTP surface
- It's an AWS service SDK where the runtime's execution role already has IAM permissions (in which case: use the SDK directly — no credential registration needed)
- The developer has a specific blocker (e.g., the service ships an OpenAI-shaped API the vendor's SDK wraps, and rebuilding the SDK call as a Gateway target would be a regression)
If none of those applies, route back to Path A/B/C:
> "Before we wire up a credential for direct use in agent code, can we front this as a Gateway target instead? Gateway injects the credential at the edge — your agent code never touches the secret — and the tool becomes policy-enforceable. If SERVICE has an OpenAPI spec, MCP server, or Lambda function in front of it, Path C / A / B is the better fit. Which one applies?"
Only continue into the rest of Path D when the developer confirms a legitimate reason Gateway won't work.
### Register the credential
```bash
# API key
agentcore add credential --name OpenAI --api-key sk-...
# OAuth (machine-to-machine)
agentcore add credential \
--name MyOAuthProvider \
--type oauth \
--discovery-url https://idp.example.com/.well-known/openid-configuration \
--client-id my-client-id \
--client-secret my-client-secret \
--scopes read,write
```
⚠️ **Security note:** `--api-key` and `--client-secret` appear in shell history. Run the command without those flags to get an interactive prompt — the value goes straight into the credential provider without touching your shell history.
**For local dev only**, put the same value in `agentcore/.env.local` (gitignored) so `agentcore dev` can resolve the decorator locally. The deployed runtime ignores `.env.local` and fetches the secret from the credential provider at call time — **never** ship secrets as runtime environment variables.
### Use credentials in agent code
Use the `@requires_api_key` or `@requires_access_token` decorators — they handle token caching and refresh automatically. The decorators work with both sync and async functions:
```python
from bedrock_agentcore.identity.auth import requires_api_key, requires_access_token
# Sync function — decorator injects the fetched key via keyword arg
@requires_api_key(provider_name="OpenAI")
def call_openai(prompt: str, *, api_key: str) -> str:
import openai
client = openai.OpenAI(api_key=api_key)
response = client.chat.completions.create(
model="gpt-4o",
messages=[{"role": "user", "content": prompt}]
)
return response.choices[0].message.content
# Async function — same decorator, async def
@requires_access_token(
provider_name="MyOAuthProvider",
scopes=["read", "write"],
auth_flow="M2M",
)
async def call_my_api(data: dict, *, access_token: str) -> dict:
import httpx
async with httpx.AsyncClient() as client:
response = await client.post(
"https://api.example.com/endpoint",
headers={"Authorization": f"Bearer {access_token}"},
json=data,
)
return response.json()
```
The decorator itself handles the token lifecycle — you don't need to make the function async just to use it. Parameters are keyword-only (`*, api_key: str` or `*, access_token: str`) — the decorator injects them.
**Local dev:** In `agentcore dev`, credentials are read from `agentcore/.env.local`. The decorator pattern works the same way locally and deployed.
---
## Local dev gap
> [!WARNING]
> Gateway URLs (AGENTCORE_GATEWAY_*_URL) are only available after deploy.
> In agentcore dev, these env vars are not set. Always check before using:
>
> ```python
> GATEWAY_URL = os.getenv("AGENTCORE_GATEWAY_MYGATEWAY_URL")
> if not GATEWAY_URL:
> # run without gateway tools in local dev
> ```
>
> Never assume the gateway is available locally.
---
## Troubleshooting
**"mcp-server target doesn't support api-key auth"**
Correct — API key auth is not supported for MCP server targets at the API level ([MCP target auth strategies](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway-target-MCPservers.html#gateway-target-MCPservers-considerations)). Options: OAuth (2LO or 3LO), IAM (for MCP servers hosted on AgentCore Runtime, API Gateway, or Lambda Function URLs), or Path D — manage the credential in agent code and call the MCP server directly.
**"I need 3LO / authorization-code OAuth but `--outbound-auth oauth` doesn't ask for a return URL"**
The CLI only configures 2LO (client credentials). 3-legged OAuth requires boto3 — call `create_gateway_target` with `credentialProviderType: OAUTH`, `grantType: AUTHORIZATION_CODE`, and `defaultReturnUrl`. See [Connecting to an OAuth-protected MCP server using Authorization Code flow](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/gateway-target-MCPservers.html#gateway-target-MCPservers-auth-code-grant-flow).
**"api-gateway target doesn't support oauth"**
Use `api-key` or `none` for API Gateway targets.
**Gateway URL not set after deploy**
Run `agentcore fetch access --name MyGateway` to get the URL. Check `agentcore status --type gateway` to verify the gateway is deployed.
**Tool calls failing with auth errors**
Check `agentcore logs --runtime MyAgent --since 1h --level error` for the specific error. Common causes: expired OAuth token, wrong credential name, IAM permission missing.
**"Adding gateway to existing agent" workaround**
The CLI recommends creating a throwaway agent to copy gateway client code. This skill generates the code directly — no workaround needed.
**MCP clients (Claude Desktop, claude.ai) can't auto-connect to Gateway**
AgentCore Gateway does not currently implement the MCP OAuth spec endpoints (RFC 8414 OAuth Authorization Server Metadata, RFC 7591 Dynamic Client Registration). MCP clients that expect to auto-discover OAuth config and register themselves — like Claude Desktop and claude.ai — cannot connect without manual credential configuration. The workaround is to manually obtain the Cognito `client_id` and `client_secret` and enter them in the MCP client's advanced settings. This is a platform limitation, not a config error.
## Output
- A clear recommendation on **Gateway target vs direct API call**, grounded in the five cases where direct is legitimate
- CLI commands to set up the gateway and target (or to register the credential, if Path D is the right call)
- Framework-specific gateway client code
- Credential setup (avoiding shell history exposure, never stored in runtime env vars)
- Local dev gap handling
plugins/aws-agents/skills/agents-connect/references/policy.md
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# policy
Control what your AgentCore agent can do — restrict tool calls, enforce business rules, and protect sensitive operations.
## When to use
- You want to restrict which tools your agent can call
- You want to enforce business rules (e.g., refunds only under $500)
- You want role-based access control on agent actions
- You want an emergency kill switch for specific tools
- A policy is denying requests you expect to allow (or allowing what you expect to deny)
## Input
`$ARGUMENTS` is optional:
```
/policy # interactive — asks what you want to restrict
/policy generate # generate Cedar from natural language
/policy debug # diagnose why a policy is allowing/denying
/policy emergency # generate an emergency shutdown policy
```
## How AgentCore policy works
AgentCore Policy enforces Cedar-based authorization rules at the **gateway boundary** — before any tool call reaches its target. Every tool call is evaluated against your policies in real time.
**Default behavior:** Without a policy engine attached to your gateway, all tool calls are allowed. Once you attach a policy engine, the default is **deny** — you must write explicit `permit` policies for everything you want to allow.
**Key concepts:**
- **Policy engine** — the container for your policies, attached to a gateway
- **Policy** — a Cedar rule that permits or forbids specific actions
- **`forbid` overrides `permit`** — if any forbid policy matches, the action is denied regardless of permit policies
---
## Process
### Step 1: Read the project
Read `agentcore/agentcore.json` to understand:
- What gateways exist (in the `agentCoreGateways` array)
- Whether a policy engine is already configured (in the `policyEngines` array)
### Step 2: Understand the goal
Ask (or infer from `$ARGUMENTS`):
> "What do you want to control?
>
> 1. Restrict a tool based on input values (e.g., amount < $500)
> 2. Role-based access (only certain users can call certain tools)
> 3. Block a specific tool entirely
> 4. Emergency shutdown — disable all tools immediately
> 5. Debug why a policy is allowing or denying unexpectedly"
---
## Path A: Set up a policy engine
### Step A1: Create the policy engine
```bash
# Create and attach to an existing gateway
agentcore add policy-engine \
--name MyPolicyEngine \
--attach-to-gateways MyGateway \
--attach-mode LOG_ONLY
```
**Start with `LOG_ONLY` mode** — policies are evaluated and logged but not enforced. This lets you verify your policies work correctly before enabling enforcement.
Switch to `ENFORCE` when ready:
```bash
# Update an existing gateway
agentcore add gateway \
--name MyGateway \
--policy-engine MyPolicyEngine \
--policy-engine-mode ENFORCE
```
(The same `--policy-engine` and `--policy-engine-mode` flags work at gateway creation time too.)
### Step A2: Deploy to activate
```bash
agentcore deploy -y
```
---
## Path B: Write Cedar policies
> [!WARNING]
> Cedar policies that reference a specific gateway ARN in the `resource` field require
> the gateway to be deployed first. You cannot add a policy with a gateway ARN before
> the gateway exists in AWS.
>
> Two-phase deployment:
>
> 1. Deploy the gateway first: `agentcore deploy -y`
> 2. Get the gateway ARN: `agentcore status --type gateway --json`
> 3. Add the policy with the real ARN, then deploy again
>
> The `-g` / `--generate` flag also requires a deployed gateway — it calls an AWS API
> that needs the gateway ARN to convert natural language into Cedar. If you run
> `-g` before deploying the gateway, it will fail.
### Option 1: Natural language generation (easiest)
**Requires the gateway to be deployed first** — the CLI calls an API that needs the gateway ARN.
```bash
# Deploy the gateway first
agentcore deploy -y
# Then generate the policy (--gateway tells the CLI which deployed gateway to use)
agentcore add policy \
--name refund_policy \
--engine MyPolicyEngine \
-g "Allow users with the refund-agent role to process refunds when the amount is less than 500" \
--gateway MyGateway
```
The CLI generates Cedar from your description, resolves the gateway ARN automatically, and validates the result. Review the generated policy before deploying.
**Policy name rules:** letters, numbers, underscores only — **no hyphens**. `refund-policy` fails; `refund_policy` works.
### Option 2: Write Cedar directly
Save to a `.cedar` file and register. If the policy references a gateway ARN in the `resource` field, you need the ARN from a prior deploy:
```bash
# Get the gateway ARN after deploying
agentcore status --type gateway --json | jq -r '.gateways[0].arn'
# Update your .cedar file with the real ARN, then add the policy
agentcore add policy \
--name refund_policy \
--engine MyPolicyEngine \
--source policy.cedar
```
### Cedar syntax reference
**Action name format:** `AgentCore::Action::"TargetName___tool_name"` — three underscores between target name and tool name. This is the most common Cedar mistake.
```cedar
// TargetName is the gateway target name (from agentcore add gateway-target --name)
// tool_name is the tool name within that target
AgentCore::Action::"RefundTarget___process_refund"
// ^^^
// three underscores
```
**Principal types:**
- `AgentCore::OAuthUser` — authenticated user via OAuth/JWT
- `AgentCore::IamEntity` — IAM-authenticated caller (when gateway uses AWS_IAM auth). The `id` attribute contains the full IAM ARN.
**Resource format:**
```cedar
AgentCore::Gateway::"arn:aws:bedrock-agentcore:<REGION>:<YOUR_ACCOUNT_ID>:gateway/<GATEWAY_ID>"
```
Get your gateway ARN: `agentcore status --type gateway --json | jq -r '.gateways[0].arn'`
### Common policy patterns
**Amount-based restriction:**
```cedar
permit(
principal is AgentCore::OAuthUser,
action == AgentCore::Action::"RefundTarget___process_refund",
resource == AgentCore::Gateway::"arn:aws:bedrock-agentcore:us-east-1:123456789012:gateway/my-gateway-id"
)
when {
principal.hasTag("role") &&
principal.getTag("role") == "refund-agent" &&
context.input.amount < 500
};
```
**Role-based access (OAuth user):**
```cedar
permit(
principal is AgentCore::OAuthUser,
action == AgentCore::Action::"AdminTarget___delete_record",
resource == AgentCore::Gateway::"arn:..."
)
when {
principal.hasTag("role") &&
["admin", "superuser"].contains(principal.getTag("role"))
};
```
**Account-based access (IAM entity):**
```cedar
permit(
principal is AgentCore::IamEntity,
action == AgentCore::Action::"AdminTarget___delete_record",
resource == AgentCore::Gateway::"arn:..."
)
when {
principal.id like "arn:aws:iam::123456789012:*"
};
```
**Block a specific tool entirely:**
```cedar
forbid(
principal,
action == AgentCore::Action::"PaymentTarget___transfer_funds",
resource == AgentCore::Gateway::"arn:..."
);
```
**Emergency shutdown — disable all tools:**
```cedar
forbid(principal, action, resource);
```
**Required field validation:**
```cedar
forbid(
principal is AgentCore::OAuthUser,
action == AgentCore::Action::"InsuranceTarget___file_claim",
resource == AgentCore::Gateway::"arn:..."
)
unless {
context.input has description &&
context.input has priority
};
```
### Critical Cedar rules
**Always use `hasTag()` before `getTag()`:**
```cedar
// ❌ Wrong — throws error if tag doesn't exist
when { principal.getTag("role") == "admin" }
// ✅ Correct — check existence first
when {
principal.hasTag("role") &&
principal.getTag("role") == "admin"
}
```
**Default deny:** Once a policy engine is attached in ENFORCE mode, everything is denied unless a `permit` policy matches. Write explicit permits for every action you want to allow.
**`forbid` always wins:** A `forbid` policy overrides any `permit` policy. Use this for emergency shutdowns and hard blocks.
---
## Path C: Test policies before enforcing
### LOG_ONLY mode
In LOG_ONLY mode, all requests are allowed but policy decisions are logged to CloudWatch. Use this to verify your policies before switching to ENFORCE.
```bash
# Check policy decision logs
agentcore logs --runtime MyAgent --since 1h --query "policy"
```
Look for log entries showing `ALLOW` or `DENY` decisions for each tool call.
### Validate policy syntax
```bash
agentcore add policy \
--name test_policy \
--engine MyPolicyEngine \
--source policy.cedar \
--validation-mode FAIL_ON_ANY_FINDINGS
```
If the Cedar syntax is invalid, the CLI returns a validation error before creating the policy.
### Switch to ENFORCE
Once LOG_ONLY results look correct:
```bash
# Update gateway to enforce mode
agentcore add gateway \
--name MyGateway \
--policy-engine MyPolicyEngine \
--policy-engine-mode ENFORCE
agentcore deploy -y
```
---
## Path D: Debug policy failures
**"Access denied" on a tool call you expect to allow:**
1. Check that a `permit` policy exists for this action — remember, default is deny
2. Verify the action name format: `TargetName___tool_name` (three underscores)
3. Verify the resource ARN matches your gateway's actual ARN
4. Check that `hasTag()` is used before `getTag()` in conditions
5. Check LOG_ONLY logs to see what the policy engine is evaluating
```bash
# Check recent policy decisions
agentcore logs --runtime MyAgent --since 1h --query "policy"
agentcore status --type policy-engine
```
**"Everything is being denied" after attaching a policy engine:**
You attached a policy engine but haven't written any `permit` policies yet. The default is deny. Write at least one `permit` policy for the actions you want to allow.
**Policy name validation error:**
Policy names must match `^[A-Za-z][A-Za-z0-9_]*$` — letters, numbers, underscores only, starts with a letter. No hyphens.
---
## Output
- CLI commands to create the policy engine and policies
- Cedar policy file for the requested use case
- LOG_ONLY testing workflow before enforcement
- Debugging guidance for policy failures
plugins/aws-agents/skills/agents-debug/SKILL.md
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---
name: agents-debug
description: >
Use when your agent or environment is broken — wrong answers, errors,
timeouts, tool failures, or CLI issues. Reads traces and logs to
diagnose root causes. Also checks prerequisites when the CLI itself
isn't working. Triggers on: "agent not working", "wrong answer",
"agent error", "tool call failing", "debug agent", "check logs",
"read traces", "broken", "500 error", "424 error", "model access
denied", "command not found", "stuck in DELETING", "maxVms exceeded",
"cold start diagnosis", "cold start slow", "agentcore create error",
"create failed", "exit code 7", "connection refused local dev".
Not for deploy failures — use agents-deploy. Not for performance
tuning without errors — use agents-optimize. Not for VPC
configuration — use agents-build. Not for observability setup or
missing logs — use agents-optimize.
allowed-tools: Read Grep Glob Bash
metadata:
type: skill
version: "1.0.0"
author: aws-agentcore
requires-cli: ">=0.9.0"
---
# debug
Diagnose why your AgentCore agent or environment isn't working correctly.
## When to use
- Your agent is returning wrong answers or errors
- Tool calls are failing or timing out
- Agent works locally but fails after deploying
- Logs aren't showing up in CloudWatch
- The AgentCore CLI isn't working or environment seems broken
- `agentcore` command not found or prerequisites are missing
Do NOT use for:
- Deploy failures (CDK errors, IAM during deploy) → use `agents-deploy`
- Scaffolding a new project → use `agents-get-started`
- Measuring quality or setting up monitoring → use `agents-optimize`
## Input
`$ARGUMENTS` is optional:
```
/agents-debug # interactive — describe what's wrong
/agents-debug traces # read and explain recent traces
/agents-debug logs # search recent logs for errors
/agents-debug memory # diagnose memory recall issues specifically
/agents-debug doctor # check environment prerequisites
```
## Process
### Step 0: Determine problem type
If the developer's issue is about the CLI itself (command not found, prerequisites, environment setup), load [`references/doctor.md`](references/doctor.md) and follow its diagnostic checklist.
If the issue is about agent behavior (wrong answers, errors, timeouts, tool failures), continue with Step 1 below.
### Step 1: Verify CLI version
Run `agentcore --version`. This skill requires v0.9.0 or later. If the version is older, tell the developer to run `agentcore update` before proceeding.
### Step 2: Understand the symptom
Ask (or infer from context):
> "What's happening?
>
> 1. The agent returns an error message
> 2. The agent returns a wrong or unhelpful answer
> 3. A specific tool call is failing
> 4. Memory isn't working (agent doesn't remember things)
> 5. The agent is slow or timing out
> 6. I want to understand what the agent did in a specific session"
### Step 3: Read traces and logs automatically
Don't ask the developer to paste logs — read them directly.
```bash
# List recent traces
agentcore traces list --runtime <AgentName> --since 1h
# Get the most recent trace ID
agentcore traces list --runtime <AgentName> --since 1h --limit 1
# Download and read the trace
agentcore traces get <traceId> --runtime <AgentName>
# Search logs for errors
agentcore logs --runtime <AgentName> --since 1h --level error
# Search logs for a specific pattern
agentcore logs --runtime <AgentName> --since 2h --query "timeout"
agentcore logs --runtime <AgentName> --since 2h --query "model access"
```
**Important:** CloudWatch put-to-get latency is **~10 seconds end-to-end** — that's the delay from when a span is emitted to when it's readable by `agentcore traces get` or `agentcore run eval`. There is **no separate "trace ingested but eval not ready yet" window**; the same ingestion step unlocks both paths. Older skills and docs said 30–60s for traces and 2–5 minutes for evals — both are stale. If you just invoked the agent, wait ~15 seconds and both trace reads and evals will work.
Read `agentcore/agentcore.json` to get the agent name if not provided.
### Step 4: Diagnose by symptom
---
## Symptom: "model access denied" or model error
**Most common cause:** The model isn't enabled in the Bedrock console for your region.
Fix:
1. Go to AWS Console → Amazon Bedrock → Model access
2. Enable the model your agent uses
3. Wait 1–2 minutes for access to propagate
**Second cause:** The execution role is missing `bedrock:InvokeModel`.
Check:
```bash
aws iam simulate-principal-policy \
--policy-source-arn $(agentcore status --json | jq -r '.runtimes[0].executionRoleArn') \
--action-names bedrock:InvokeModel \
--resource-arns "arn:aws:bedrock:*::foundation-model/*"
```
**Third cause:** Cross-region inference profile requires model access in all regions.
Model IDs starting with a geographic prefix are cross-region inference profiles that route requests within that geography:
| Prefix | Geography | Example destination regions |
|---|---|---|
| `us.` | United States | us-east-1, us-east-2, us-west-2 |
| `eu.` | Europe | eu-central-1, eu-west-1, eu-west-2, eu-west-3 |
| `apac.` | Asia Pacific | ap-northeast-1, ap-southeast-1, ap-southeast-2, ap-south-1 |
| `global.` | All commercial regions worldwide | All supported regions |
The AgentCore CLI scaffolds `global.` by default (e.g., `global.anthropic.claude-sonnet-4-5-20250929-v1:0`). All prefixes require model access enabled in every destination region the profile covers. For `us.` profiles, enable in all US regions; for `eu.`, all EU regions; for `global.`, all supported regions. Not all models support all prefixes — `global.` is currently available for select models only. Use `global.` for maximum throughput when available, or a geographic prefix when data residency requirements constrain where inference can run. Check the Bedrock inference profiles docs for current model × prefix availability.
---
## Symptom: Tool call failing
**Step 1:** Find the failing tool call in the trace:
```bash
agentcore traces get <traceId> --runtime <AgentName>
```
Look for tool call entries with error status.
**Step 2:** Check the gateway status:
```bash
agentcore status --type gateway
agentcore fetch access --name <AgentName> --type agent
```
**Step 3:** Common tool call failures:
**Gateway URL not set (local dev):**
The `AGENTCORE_GATEWAY_*_URL` env var is only set after deploy. In `agentcore dev`, gateway tools aren't available. This is expected — the agent should handle this gracefully.
**Auth failure on tool call:**
```bash
agentcore logs --runtime <AgentName> --since 1h --query "auth"
```
Check that the credential is configured correctly: `agentcore status --type credential`
**Lambda function error:**
The Lambda itself is failing. Check Lambda logs directly:
```bash
aws logs tail /aws/lambda/<function-name> --since 1h
```
**Policy denial:**
If a policy engine is attached, check policy decision logs:
```bash
agentcore logs --runtime <AgentName> --since 1h --query "policy"
agentcore status --type policy-engine
```
---
## Symptom: Wrong or unhelpful answers
**Step 1:** Read the trace to see the agent's reasoning:
```bash
agentcore traces get <traceId> --runtime <AgentName>
```
The trace shows the model's reasoning steps, tool calls made, and the final response. Look for:
- Did the agent use the right tools?
- Did the tool calls return the expected data?
- Is the system prompt providing the right context?
**Step 2:** Check if memory is involved:
If the agent should be using memory context but isn't, see the "Symptom: Memory not persisting" section later in this skill, or load [`references/doctor.md`](references/doctor.md) if this is an environment issue.
**Step 3:** Common causes:
- System prompt is too vague or missing key context
- Agent isn't calling the right tools (tool descriptions need improvement)
- Tool is returning unexpected data format
- Model ID is wrong for the task (e.g., using a smaller model for complex reasoning)
---
## Symptom: Memory not working
**Memory not persisting across sessions (LTM):**
1. Verify LTM strategies are configured (SEMANTIC or USER_PREFERENCE):
```bash
agentcore status --type memory --json | jq '.memories[].strategies'
```
1. Wait 5–30 seconds after a session ends — LTM extraction is async. The agent must finish its session before facts are extracted.
2. Use UUIDs (v4) for session IDs — the platform requires a minimum of 33 characters. Short IDs like "session-1" cause LTM to fail silently. `agentcore invoke` generates compliant IDs by default.
3. Verify the memory resource is ACTIVE:
```bash
agentcore status --type memory
```
**Memory not loading at session start:**
1. Check the `MEMORY_*_ID` env var is set:
```bash
agentcore status --type memory --json | jq '.memories[].id'
```
1. Verify the `actor_id` is consistent across sessions — memory is scoped per actor.
2. Check the namespace paths in your retrieval config match the namespaces used when writing.
---
## Symptom: Agent timeout
**Step 1:** Check the trace for where time is being spent:
```bash
agentcore traces get <traceId> --runtime <AgentName>
```
Look for long-running steps — model calls, tool calls, memory operations.
**Step 2:** Common timeout causes:
**Slow agent initialization:** If the first invocation after an idle period is slow but subsequent requests are fast, the agent is spending too much time initializing. Check for heavy imports at module level, database connections in global scope, or MCP client initialization during startup. Move expensive setup into the request handler or use lazy initialization. See the `agents-harden` skill for optimization guidance.
**Model call timeout:** The model is taking too long. Consider using a faster model for time-sensitive operations (e.g., Haiku instead of Sonnet for simple tasks).
**Tool call timeout:** The Lambda or external API is slow. Check the tool's own logs.
**Memory retrieval timeout:** Semantic search can be slow for large memory stores. Consider reducing `top_k` in your retrieval config.
**VPC connectivity issue:** If the agent is in a VPC, check security group rules and route tables. See `agents-build` (loads [`references/vpc.md`](../agents-build/references/vpc.md)) for VPC-specific debugging.
---
## Symptom: `ServiceQuotaExceededException: maxVms limit exceeded` (despite low observed concurrency)
Your CloudWatch "concurrent sessions" metric shows modest numbers (maybe 30–50) but `InvokeAgentRuntime` calls return `ServiceQuotaExceededException: maxVms limit exceeded`.
**What's actually happening:** CloudWatch's concurrent-sessions metric is not the same as live microVM count. The `maxVms` quota counts all environments your account has active — including ones that finished their invocation but haven't been reclaimed yet. Idle-but-not-yet-reclaimed environments count against the quota until `idleRuntimeSessionTimeout` expires (default 900 seconds / 15 minutes) or you explicitly stop them.
If your code uses a new session ID per request and doesn't call `StopRuntimeSession`, every request leaves an environment sitting idle for 15 minutes counting against the quota.
**Fix order (try in this order before requesting a quota increase):**
1. **Call `StopRuntimeSession` after each logical request completes.** If you're not going to send more requests on this session, stop it explicitly.
```python
client.stop_runtime_session(
agentRuntimeArn=runtime_arn,
runtimeSessionId=session_id,
)
```
2. **Reuse session IDs across related requests.** If a user interaction produces multiple backend calls, route them to the same session instead of generating a new session ID per call.
3. **Lower `idleRuntimeSessionTimeout`.** If your sessions are short-lived and you can't add `StopRuntimeSession` everywhere, lower the timeout by editing the runtime's `lifecycleConfiguration` in `agentcore/agentcore.json` and running `agentcore deploy`.
4. **Only after the above, request a quota increase.** See `agents-harden` (loads [`references/limits.md`](../agents-harden/references/limits.md)) — request it through the Service Quotas console (Amazon Bedrock AgentCore), not by filing a support ticket directly.
See `agents-harden` Session lifecycle management section for the full pattern.
---
## Symptom: 424 Failed Dependency on invoke
This usually means the agent container failed to start or crashed during initialization.
**Step 1:** Check the agent logs for startup errors:
```bash
agentcore logs --runtime <AgentName> --since 30m --level error
```
**Step 2:** Common causes:
**Missing Python dependency:** The agent code imports a package not in `pyproject.toml`. The container starts but crashes on first request. Fix: add the dependency and redeploy.
**Entrypoint crash:** The `main.py` throws an exception during import or `app.run()`. Check logs for the traceback.
**Container image pull failure:** If using Container build, the ECR image may not exist or the execution role lacks `ecr:BatchGetImage`. Check:
```bash
agentcore status --runtime <AgentName> --json
```
**Memory resource not ACTIVE:** If the agent code assumes memory is available but the memory resource is still in CREATING state, the entrypoint may fail. Check:
```bash
agentcore status --type memory
```
**Initialization timeout:** The agent takes too long to be ready for its first request — heavy imports at module level, synchronous database connections, or MCP client initialization during startup can exceed the service's health-check window. The symptom looks like a 424 on the first invoke but healthy on subsequent ones. Fix: move expensive setup out of module level, use lazy initialization, or warm the agent before production traffic. See `agents-harden` Initialization time section for patterns.
---
## Symptom: Local invocations fail with connection-refused / exit code 7
Usually not an agent bug — the dev server is on a different port than you expect.
**Default ports `agentcore dev` binds:**
| Protocol | Default |
|---|---|
| HTTP | 8080 |
| MCP | 8000 |
| A2A | 9000 |
**When the default is occupied** (second dev session, a lingering process from a previous run, another service on 8080), the CLI **auto-increments** silently: 8080 → 8081 → 8082. A test harness or `curl` script hardcoded to 8080 will get `Connection refused` (curl exit code 7) while the agent is running fine on 8082.
Diagnose in this order:
1. Read the CLI banner that `agentcore dev` prints — it shows the actual bound port and URL. This is always the source of truth.
2. If the banner is gone (terminal cleared, running in background), check the log file:
```bash
tail -20 agentcore/.cli/logs/dev/*.log
```
3. Or find the process directly:
```bash
# macOS / Linux
ps aux | grep -E 'agentcore dev|uvicorn' | grep -v grep
lsof -iTCP -sTCP:LISTEN -n -P | grep -E '8080|8081|8082|8000|9000'
```
**Fix options:**
- Pin the port explicitly: `agentcore dev --port 8080`
- Kill the process squatting on the default: `lsof -tiTCP:8080 -sTCP:LISTEN | xargs kill`
- Update the hardcoded port in your test harness to read from the CLI output or from an env var
This is also a common source of "works locally one day, fails the next" reports — the port shifted between runs.
---
## Symptom: Gateway tool calls failing with auth errors
**Step 1:** Verify the auth type matches the target type. This is the most common gateway error — using the wrong outbound auth for the target:
| Target type | Valid outbound auth |
|---|---|
| `mcp-server` | `none`, `oauth`, or IAM (SigV4 via API) |
| `lambda-function-arn` | IAM only (automatic) |
| `open-api-schema` | `oauth` or `api-key` (required) |
| `api-gateway` | `none`, `api-key`, or IAM |
| `smithy-model` | IAM or `oauth` |
**Step 2:** Check for expired OAuth tokens. If the gateway target uses OAuth, the access token may have expired. Look for auth-related errors:
```bash
agentcore logs --runtime <AgentName> --since 1h --query "auth"
agentcore logs --runtime <AgentName> --since 1h --query "401"
agentcore logs --runtime <AgentName> --since 1h --query "403"
```
If tokens are expiring, verify the OAuth credential provider's token endpoint is reachable and the client credentials are still valid. For MCP server targets with OAuth, the gateway handles token refresh automatically — if it's failing, the credential provider config may be wrong.
**Step 3:** Check the credential is configured:
```bash
agentcore status --type credential
agentcore status --type gateway --json
```
---
## Symptom: No traces appearing
**Wait ~15 seconds** — there's a short delay (typically ~10s) between invocation and trace availability.
If still no traces after ~30 seconds:
1. Verify observability was enabled when the agent was deployed
2. Check the agent was actually invoked: `agentcore logs --runtime <AgentName> --since 1h`
3. Check CloudWatch permissions on the execution role
---
## Symptom: CloudWatch logs not appearing
This is the most common observability issue, especially for Container/Docker builds.
AgentCore doesn't capture raw stdout. It uses OpenTelemetry to ship logs to CloudWatch. Three things must be true:
**1. Your entrypoint must be wrapped with `opentelemetry-instrument`.**
CodeZip builds do this automatically. Docker/Container builds need it added manually — this is the #1 thing people miss.
In your Dockerfile CMD:
```dockerfile
# ✅ Correct — wrapped with opentelemetry-instrument
CMD ["opentelemetry-instrument", "python", "main.py"]
# ❌ Wrong — no OTEL wrapper, logs won't appear
CMD ["python", "main.py"]
```
**2. Your runtime IAM role needs CloudWatch and X-Ray permissions:**
```
logs:CreateLogGroup
logs:CreateLogStream
logs:PutLogEvents → scoped to /aws/bedrock-agentcore/runtimes/*
xray:PutTelemetryRecords
xray:PutTraceSegments → scoped to *
```
If using the AgentCore CLI with CodeZip, the CDK scaffold adds these automatically. If using a custom role or Container build, verify they're present.
**3. Use Python's `logging` module, not `print()`.**
OTEL hooks into `logging` automatically — no custom handlers needed. `print()` statements won't appear in CloudWatch.
```python
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO)
# ✅ This appears in CloudWatch
logger.info("Processing request")
# ❌ This does NOT appear in CloudWatch
print("Processing request")
```
**Also verify:** CloudWatch Transaction Search is enabled in your account. Without it, traces and spans won't appear in the GenAI Observability dashboard.
### Logs missing for Terraform/CDK/IaC-deployed runtimes
A common pattern: a runtime deployed via Terraform, CDK, or a custom IAM role works correctly (returns responses) but no CloudWatch log streams appear — while the same agent code deployed via the AgentCore Console logs fine.
This is almost always an IAM scoping issue. The execution role for a runtime deployed via the Console gets broad CloudWatch permissions by default. IaC templates often scope those permissions narrowly to `/aws/bedrock-agentcore/runtimes/*`, which breaks log stream creation.
**The fix:** `logs:DescribeLogGroups` must have `Resource: "*"`, not a scoped resource. The other logs actions can be scoped to the runtime's log group.
```json
{
"Effect": "Allow",
"Action": [
"logs:DescribeLogGroups"
],
"Resource": "*"
},
{
"Effect": "Allow",
"Action": [
"logs:CreateLogGroup",
"logs:CreateLogStream",
"logs:PutLogEvents"
],
"Resource": "arn:aws:logs:<REGION>:<ACCOUNT_ID>:log-group:/aws/bedrock-agentcore/runtimes/*:*"
}
```
After updating the execution role's IAM policy, redeploy the runtime with `agentcore deploy` to pick up the new permissions.
---
## Symptom: Streaming connection drops mid-response
Your agent uses SSE or long-polling responses and the connection drops mid-stream. Symptoms in client code:
- `RemoteProtocolError: peer closed connection without sending complete message body`
- `IncompleteRead` exception while iterating the stream
- Silent disconnect — no error, no `[DONE]` event, response just stops
- Happens during multi-tool-use conversations (5+ sequential tool calls)
- Fails well before any client-side timeout
**Root cause:** Infrastructure-layer idle timeout on streaming connections. If no data flows on the response stream for several minutes (a silent period while a tool executes, for example), a load balancer in front of the runtime terminates the TCP connection.
The timeout is on **data flowing through the stream**, not on the request total duration. As long as you emit bytes periodically, the connection stays open.
**Fix: emit keepalive events during long-running tool executions.**
Python pattern for a streaming entrypoint:
```python
import asyncio
import json
from bedrock_agentcore.runtime import BedrockAgentCoreApp
app = BedrockAgentCoreApp()
async def emit_keepalive(tool_task):
"""Yield heartbeat events every 30s while tool_task is running."""
while not tool_task.done():
yield f"data: {json.dumps({'type': 'heartbeat'})}\n\n"
try:
await asyncio.wait_for(asyncio.shield(tool_task), timeout=30)
except asyncio.TimeoutError:
continue # tool still running, emit another heartbeat
@app.entrypoint
async def invoke(payload, context):
async def stream():
tool_task = asyncio.create_task(run_long_tool(payload))
# Emit heartbeats while the tool runs
async for event in emit_keepalive(tool_task):
yield event
# Tool completed — emit the real result
result = await tool_task
yield f"data: {json.dumps({'type': 'result', 'content': result})}\n\n"
yield "data: [DONE]\n\n"
return stream()
```
Pick a heartbeat interval of ~30 seconds. Too long risks hitting the idle timeout; too short wastes bandwidth.
**On the client side, filter heartbeat events** before surfacing bytes to the user:
```python
for chunk in response.iter_lines():
if not chunk:
continue
data = json.loads(chunk.removeprefix(b"data: "))
if data.get("type") == "heartbeat":
continue # ignore keepalives
# process real events
```
**Alternative: use the SDK's async task API for fire-and-forget patterns.** If the client doesn't need to wait for the result, register the work via `add_async_task` / `complete_async_task` and return the invocation immediately. See `agents-harden` Long-running background tasks section.
---
## Symptom: Traces appear merged across concurrent agent invocations
You run multiple agent invocations in parallel with unique `runtimeSessionId` values, but the AI Observability dashboard groups them as one session — making it impossible to isolate a single run. Data plane logs show the session IDs are correctly unique 1:1 with request IDs, but the trace view still merges them.
**Most common cause: the caller isn't enabling Active Tracing**, so upstream spans arrive with `Sampled=0`. AgentCore respects upstream trace-sampling decisions by default. If the parent context says "don't sample," spans drop and concurrent invocations can appear merged in the dashboard.
**Fix by caller type:**
**Lambda caller:** Enable Active Tracing on the Lambda function.
```bash
aws lambda update-function-configuration \
--function-name my-caller-function \
--tracing-config Mode=Active
```
Or in the Lambda console: Configuration → Monitoring and operations tools → AWS X-Ray → Active tracing.
**ECS / EC2 / container caller:** Initialize the AWS X-Ray SDK and ensure outbound calls to AgentCore are instrumented. For Python, use `aws-xray-sdk` and patch the SDK:
```python
from aws_xray_sdk.core import xray_recorder, patch_all
patch_all() # patches boto3, requests, etc.
```
**Direct SDK caller without X-Ray:** If you can't enable upstream tracing, force the runtime to sample by setting an environment variable on the agent:
```
OTEL_TRACES_SAMPLER=always_on
```
This makes the runtime sample every trace regardless of the parent context's sampling decision. Trade-off: higher tracing costs, but the traces are correct.
### Also check: invoking with the endpoint ARN instead of the agent ARN
If traces show only a single top-level `AgentCore.Runtime.Invoke` span with no child spans, check the ARN your caller is using. The invoke target should be the agent runtime ARN:
```
arn:aws:bedrock-agentcore:<region>:<account>:runtime/<runtime-name>
```
Not the endpoint ARN:
```
arn:aws:bedrock-agentcore:<region>:<account>:runtime/<runtime-name>/runtime-endpoint/DEFAULT
```
Invoking with the endpoint ARN can bypass the full trace instrumentation path. This is a subtle trap — both ARNs produce successful responses, but only the agent ARN produces complete traces.
---
## Symptom: Runtime stuck in DELETING for hours
You called `DeleteAgentRuntime`, got a successful response with `status: DELETING`, and the runtime has been stuck in that state for more than 30 minutes. Attempting to delete the default endpoint separately returns `ConflictException: Default endpoints are removed when you delete the agent.`
**What's happening:** The deletion workflow is stuck on the service side. Retrying `DeleteAgentRuntime` won't help — the call succeeds immediately (returning DELETING) but the back-end workflow is the thing that's stuck. Customer-side tooling can't force-complete it.
**What to do:**
1. **Do not keep retrying.** It won't unstick the workflow.
2. **Open an AWS Support case** at https://console.aws.amazon.com/support. Include:
- AWS Account ID
- Region
- Runtime ARN (or `agentRuntimeId`)
- The `requestId` and timestamp of the original `DeleteAgentRuntime` call (from CloudTrail)
- How long the runtime has been in DELETING state
3. **Work around it in the meantime.** Deploy a new runtime with a different name if you need to keep shipping. Don't let the stuck resource block your work.
Orphaned resources from a stuck deletion (ENIs, workload identities) may need manual cleanup from the service team as part of the same case.
---
## Framework-specific issues
**LangGraph — model format:**
Older versions of `langchain-aws` required the model ID without the cross-region prefix. Recent versions may support cross-region inference profiles — check your installed version:
```bash
pip show langchain-aws | grep Version
```
If you hit model errors with LangGraph, try the non-prefixed ID:
```python
# If cross-region prefix errors in your langchain-aws version:
llm = init_chat_model("anthropic.claude-sonnet-4-5-20250929-v1:0", model_provider="bedrock_converse")
# If your version supports cross-region profiles (us. = US, eu. = Europe, apac. = Asia Pacific, global. = worldwide):
llm = init_chat_model("global.anthropic.claude-sonnet-4-5-20250929-v1:0", ...)
```
Verify against the current langchain-aws release notes: https://github.com/langchain-ai/langchain-aws/releases — cross-region inference profile support has been evolving.
**Google ADK — Gemini only:**
ADK only works with Gemini models. If you're seeing model errors with ADK, check that `GEMINI_API_KEY` is set and you're using a `gemini-*` model ID.
**A2A agents — wrong port:**
A2A servers must run on port 9000. If your A2A agent isn't responding, check it's not accidentally running on 8080.
---
## Reading a trace
A trace shows the full execution path of one agent invocation. Key sections:
- **Model invocations** — what the model was asked and what it responded
- **Tool calls** — which tools were called, with what inputs, and what they returned
- **Memory operations** — what was read from and written to memory
- **Policy decisions** — what was allowed or denied (if policy engine is attached)
- **Latency breakdown** — time spent in each component
```bash
# Download trace to a file for detailed inspection
agentcore traces get <traceId> --runtime <AgentName> --output trace.json
cat trace.json | jq '.trace.orchestrationTrace.modelInvocationOutput'
```
## Output
- Diagnosis of the specific failure with root cause
- Specific fix commands or code changes
- Explanation of what the trace shows (if reading traces)
- Handoff to the appropriate skill when the fix is outside debug's scope
## After diagnosis — handoff
Once you've identified the root cause, hand off to the skill that owns the fix:
| Root cause | Hand off to | Detail |
|---|---|---|
| Memory misconfigured (wrong strategy, namespace, wiring) | `agents-build` | Load [`references/memory.md`](../agents-build/references/memory.md) |
| Agent invocation from app not working (auth, URL, streaming) | `agents-build` | Load [`references/integrate.md`](../agents-build/references/integrate.md) |
| VPC connectivity (can't reach RDS, no internet, AZ error) | `agents-build` | Load [`references/vpc.md`](../agents-build/references/vpc.md) |
| Multi-agent delegation not working | `agents-build` | Load [`references/multi-agent.md`](../agents-build/references/multi-agent.md) |
| Custom request headers not reaching agent code | `agents-build` | Load [`references/request-headers.md`](../agents-build/references/request-headers.md) |
| Cross-account invocation from an app in another account | `agents-build` | Load [`references/integrate.md`](../agents-build/references/integrate.md) (cross-account section) |
| Gateway auth misconfigured (401, wrong auth type) | `agents-connect` | Gateway auth matrix |
| Gateway target type question (Lambda vs OpenAPI vs MCP vs API Gateway) | `agents-connect` | "What Gateway is and isn't" section |
| Policy denying unexpectedly (Cedar, access denied on tool) | `agents-connect` | Load [`references/policy.md`](../agents-connect/references/policy.md) |
| Observability not set up (no logs, no traces appearing) | `agents-optimize` | Load [`references/observability.md`](../agents-optimize/references/observability.md) |
| Cold start / initialization too slow | `agents-harden` | Initialization time section |
| Session lifecycle / `maxVms` / `StopRuntimeSession` | `agents-harden` | Session lifecycle management section |
| Long-running background tasks being reclaimed | `agents-harden` | Long-running background tasks section |
| JWT inbound auth failing (403, `allowedClients`/`allowedAudience`, issuer mismatch) | `agents-harden` | Inbound auth section |
| Throttling / quota error / limit increase request | `agents-harden` | Load [`references/limits.md`](../agents-harden/references/limits.md) |
| Deploy artifact stale or wrong version | `agents-deploy` | Redeploy workflow |
| Environment broken (CLI, credentials, Node, uv) | Load [`references/doctor.md`](references/doctor.md) | Self-contained in this skill |
State the diagnosis clearly, then tell the developer which skill to use next. If the agent can load the referenced skill in the same session, do so.
plugins/aws-agents/skills/agents-debug/references/doctor.md
+206
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@@ -0,0 +1,206 @@
# doctor
Check your environment and tell you exactly what's needed to use the AgentCore CLI.
## When to use
- `agentcore` command not found or CLI isn't behaving correctly
- `agentcore create` or `agentcore deploy` fails immediately with an environment error
- Developer isn't sure if their environment is configured correctly
- Something that used to work stopped working after an OS or tool update
Do NOT use for:
- Creating a new project or getting started → use `agents-get-started`
- Deploy failures that aren't environment-related (CDK errors, IAM) → use `agents-deploy`
- Agent runtime errors → use `agents-debug`
## Input
No arguments required.
## Process
Run each check and report the result. For anything missing, give the exact fix command — don't just say "install X."
### Check 1: AgentCore CLI
```bash
agentcore --version
```
**If the command errors instead of returning a version:**
Run `which agentcore` to see what's installed:
- Path in `/usr/local/lib/python*/site-packages/` or similar Python location → the old Starter Toolkit is shadowing the new CLI. Uninstall it (see below).
- Path in a Node.js-based location but still errors → the Node.js version may be wrong. Continue to Check 2.
- No path returned → the CLI isn't installed.
**If not found:**
```bash
npm install -g @aws/agentcore
```
Requires Node.js 20+. If `npm` isn't available, install Node.js first: https://nodejs.org
**If old Starter Toolkit is installed** (Python-based `agentcore` command):
```bash
# Uninstall the old CLI first
pip uninstall bedrock-agentcore-starter-toolkit
# or: pipx uninstall bedrock-agentcore-starter-toolkit
# or: uv tool uninstall bedrock-agentcore-starter-toolkit
# Then install the new CLI
npm install -g @aws/agentcore
```
### Check 2: Node.js version
```bash
node --version
```
Requires Node.js 20.x or later. If older:
- macOS: `brew install node` or download from https://nodejs.org
- Linux: use `nvm install 20` (https://github.com/nvm-sh/nvm)
### Check 3: uv (Python package manager)
```bash
uv --version
```
`uv` manages Python virtual environments for your agent code. It's required for Python agents.
**If not found:**
```bash
# macOS/Linux
curl -LsSf https://astral.sh/uv/install.sh | sh
# Or via pip
pip install uv
# Or via Homebrew
brew install uv
```
After installing, restart your terminal or run `source ~/.bashrc` (or `~/.zshrc`).
### Check 4: AWS credentials
```bash
aws sts get-caller-identity
```
**If AWS CLI not found:**
```bash
# macOS
brew install awscli
# Or download from https://aws.amazon.com/cli/
```
**If credentials not configured:**
```bash
aws configure
# Enter: AWS Access Key ID, Secret Access Key, default region, output format
```
**If using SSO:**
```bash
aws sso login --profile your-profile
```
**Check the region:** The region in `aws configure` must match the region where you've enabled Bedrock model access and where you'll deploy.
### Check 5: Bedrock model access
```bash
aws bedrock list-foundation-models \
--region $(aws configure get region) \
--query 'modelSummaries[?contains(modelId, `claude`) && modelLifecycle.status==`ACTIVE`].modelId' \
--output table
```
If no Claude models appear, or if you see access errors:
1. Go to AWS Console → Amazon Bedrock → Model access
2. Click "Manage model access"
3. Enable "Anthropic Claude" models
4. Click "Save changes" — access is usually granted within a minute
**Required model for default projects:** The default model is a cross-region inference profile (e.g., `global.anthropic.claude-sonnet-4-5-20250929-v1:0` — the CLI scaffolds `global.` by default). The `global.` prefix routes to any commercial region; geographic prefixes (`us.`, `eu.`, `apac.`) keep inference within that geography. All prefixes require model access enabled in every destination region the profile covers. Check `agentcore.json` after `agentcore create` for the exact model ID used.
### Check 6: IAM permissions
```bash
aws iam simulate-principal-policy \
--policy-source-arn $(aws sts get-caller-identity --query Arn --output text) \
--action-names iam:CreateRole bedrock:InvokeModel \
--resource-arns "*" \
--query 'EvaluationResults[*].{Action:EvalActionName,Decision:EvalDecision}'
```
For deploy to work, you need:
- `iam:CreateRole` — to create execution roles
- `bedrock:InvokeModel` — to call Bedrock models
- `ecr:CreateRepository`, `ecr:PutImage` — for container builds
- `codebuild:StartBuild` — for remote builds
If permissions are missing, ask your AWS admin to attach `BedrockAgentCoreFullAccess` and `AmazonBedrockFullAccess` managed policies to your IAM user or role.
### Check 7: Docker (optional — only needed for Container builds)
```bash
docker --version
docker info 2>&1 | head -5
```
Docker is only required if you're using `--build Container`. CodeZip builds (the default) don't need Docker locally — they use AWS CodeBuild.
**If Docker not running:**
- macOS: Start Docker Desktop
- Linux: `sudo systemctl start docker`
**Alternatives to Docker:** AgentCore also supports Podman and Finch.
---
## Summary output format
Report results as a clear checklist:
```
AgentCore Environment Check
✅ AgentCore CLI: 0.9.1
✅ Node.js: v20.11.0
✅ uv: 0.4.18
✅ AWS credentials: configured (account: 123456789012, region: us-east-1)
✅ Bedrock model access: Claude models enabled
⚠️ IAM permissions: missing iam:CreateRole — deploy will fail
❌ Docker: not running — needed for Container builds (optional)
Issues to fix:
1. IAM: Ask your admin to attach BedrockAgentCoreFullAccess to your user
2. Docker: Start Docker Desktop (only needed for Container builds)
All clear? Run `agents-get-started` to create your first project.
```
## Output
- Checklist of all prerequisites with pass/fail status
- Exact fix command for each failing check
- Clear indication of what's blocking vs. what's optional
- Pointer to `agents-get-started` skill when environment is healthy
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---
name: agents-deploy
description: >
Use when deploying your agent to AWS, or when a deploy has failed.
Handles pre-flight validation, CDK/IAM/quota error diagnosis, version
management, rollback, and canary deployments. Triggers on: "deploy my
agent", "agentcore deploy", "deploy failed", "CDK error", "rollback",
"canary deploy", "pin version", "redeploy", "deploy stuck".
Not for production hardening — use agents-harden. Not for adding
capabilities before deploy — use agents-build or agents-connect.
Not for VPC configuration errors — use agents-build.
allowed-tools: Read Grep Glob Bash
metadata:
type: skill
version: "1.0.0"
author: aws-agentcore
requires-cli: ">=0.9.0"
---
# deploy
Deploy your AgentCore agent to AWS, or diagnose why a deploy failed.
## When to use
- You're ready to deploy and want to validate config first
- `agentcore deploy` failed with an error
- You want to preview what deploy will create without actually deploying
- You want to deploy to a specific target (staging, production)
- You need to roll back to a previous version, pin to a specific version, or set up canary deployments
## Input
`$ARGUMENTS` is optional:
```
/agents-deploy # interactive — pre-flight check or diagnose failure
/agents-deploy preflight # validate config and IAM before deploying
/agents-deploy diagnose # diagnose a failed deploy (paste error or read logs)
/agents-deploy preview # show what deploy will create without deploying
/agents-deploy rollback # roll back to a previous version
```
## Process
### Step 0: Verify CLI version
Run `agentcore --version`. This skill requires v0.9.0 or later. If the version is older, tell the developer to run `agentcore update` before proceeding.
### Step 1: Determine the situation
Read `agentcore/agentcore.json` and `agentcore/aws-targets.json` if they exist.
Ask (or infer from context):
> "Are you:
>
> 1. About to deploy and want to check everything first
> 2. Dealing with a failed deploy — what error did you see?
> 3. Needing to roll back or pin a specific version?"
If the developer needs versioning, rollback, or canary deployment, load [`references/versioning.md`](references/versioning.md) and follow its instructions.
---
## Path A: Pre-flight validation
Run these checks before `agentcore deploy`:
### Check 1: Validate config files
Show the developer this command to run:
```bash
agentcore validate
```
This catches malformed `agentcore.json` before CDK even starts.
### Check 2: Verify region alignment
The most common deploy failure is a region mismatch. Show the developer these commands to verify:
```bash
# Your configured AWS region
aws configure get region
# The region in your deployment target
cat agentcore/aws-targets.json
# The account you're actually authenticated as
aws sts get-caller-identity
```
The `region` in `aws-targets.json` must match your `aws configure` default region. The `account` must match the account ID from `sts get-caller-identity`.
### Check 3: Verify Bedrock model access
Show the developer this command to check enabled models in their region:
```bash
aws bedrock list-foundation-models --region $(aws configure get region) \
--query 'modelSummaries[?modelLifecycle.status==`ACTIVE`].modelId' \
--output table
```
Cross-region inference profile IDs use a geographic prefix (`us.`, `eu.`, `apac.`) or `global.` to control where inference runs. The CLI scaffolds `global.` by default (e.g., `global.anthropic.claude-sonnet-4-5-20250929-v1:0`), which routes to any commercial region. Geographic prefixes keep inference within that geography (e.g., `eu.` stays in EU regions). All prefixes require model access enabled in every destination region the profile covers. Check the Bedrock docs for which regions are included in each profile prefix.
### Check 4: Preview what will be deployed
```bash
agentcore deploy --dry-run
agentcore deploy --diff
```
`--dry-run` shows what resources will be created. `--diff` shows the CDK diff against what's currently deployed.
### Check 5: Verify IAM permissions
Show the developer the permissions needed and this verification command:
```bash
aws iam simulate-principal-policy \
--policy-source-arn $(aws sts get-caller-identity --query Arn --output text) \
--action-names iam:CreateRole \
--resource-arns "arn:aws:iam::*:role/*BedrockAgentCore*"
```
### Run the deploy
```bash
agentcore deploy -y # auto-confirm (alias: agentcore dp -y)
agentcore deploy -y -v # verbose — shows resource-level events
agentcore deploy --target staging -y # deploy to a specific target
```
**Memory provisioning note:** If your project includes memory, deploy takes 2–5 minutes longer while the memory resource becomes ACTIVE. This is normal — not an error. Check status:
```bash
agentcore status --type memory
```
---
## Path B: Diagnose a failed deploy
### Step B1: Read the error
If the developer pasted an error, diagnose it directly. If not, read the deploy logs:
```bash
# View recent deploy logs
ls -lt agentcore/.cli/logs/
cat agentcore/.cli/logs/deploy-*.log 2>/dev/null | tail -100
```
### Step B2: Match to known failure patterns
**IAM permission error:**
```
User: arn:aws:iam::123456789012:user/dev is not authorized to perform: iam:CreateRole
```
Fix: Attach the required IAM permissions (see Check 5 above). The deploying identity needs IAM write access scoped to `*BedrockAgentCore*` roles.
**CDK bootstrap not run:**
```
This stack uses assets, so the toolkit stack must be deployed to the environment
```
Fix:
```bash
npx cdk bootstrap aws://<YOUR_ACCOUNT_ID>/<REGION>
```
**ECR authorization error:**
```
no basic auth credentials
Error response from daemon: Head "https://<YOUR_ACCOUNT_ID>.dkr.ecr.<REGION>.amazonaws.com/..."
```
Fix:
```bash
aws ecr get-login-password --region <REGION> | \
docker login --username AWS --password-stdin <YOUR_ACCOUNT_ID>.dkr.ecr.<REGION>.amazonaws.com
```
**Model access denied during deploy:**
```
ValidationException: The provided model identifier is invalid
```
Fix: Enable the model in the Bedrock console → Model access. Ensure the model ID in `agentcore.json` matches an enabled model in your target region.
**Region mismatch:**
```
Stack ... is in region us-east-1 but the target is us-west-2
```
Fix: Update `agentcore/aws-targets.json` to match your `aws configure` default region, or run `aws configure set region <REGION>`.
**Memory stuck in CREATING:**
```
Memory resource is in CREATING state after 10 minutes
```
This is unusual — normal provisioning takes 2–5 minutes. Check:
```bash
agentcore status --type memory --json
```
If stuck, try removing and re-adding the memory resource.
**Service quota exceeded:**
```
LimitExceededException: Account limit for AgentCore runtimes exceeded
```
Fix: Request a quota increase in the AWS console → Service Quotas → Amazon Bedrock AgentCore.
### Step B3: After fixing, re-run
```bash
agentcore deploy -y
```
If the same error recurs, check `agentcore status` to see the current state of all resources:
```bash
agentcore status
agentcore status --state pending-removal # resources marked for deletion
```
---
## Deploying to multiple targets
Define targets in `agentcore/aws-targets.json`:
```json
[
{
"name": "staging",
"description": "Staging environment",
"account": "123456789012",
"region": "us-east-1"
},
{
"name": "production",
"description": "Production environment",
"account": "987654321098",
"region": "us-west-2"
}
]
```
Deploy to a specific target:
```bash
agentcore deploy --target staging -y
agentcore deploy --target production -y
```
## Output
- Pre-flight check results with specific fixes for any issues found
- Diagnosis of deploy failure with the specific fix
- Deploy command to run after fixes are applied
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# Agent Versioning and Rollback
Every `agentcore deploy` creates a new version of your agent runtime. This reference covers how versions work, how to pin to a specific version, and how to roll back when a deploy goes wrong.
## How versioning works
- Each `agentcore deploy` produces a new runtime version
- The alias (usually `DEFAULT`) points to the currently-live version
- Old versions remain accessible by ARN for rollback
- Local dev (`agentcore dev`) always runs the current code — no version concept
## Inspecting versions
The AgentCore CLI currently manages the project config (`agentcore.json` → `agentcore deploy`) but doesn't expose version/alias operations directly. For those, use the AWS CLI against the `bedrock-agentcore-control` data plane.
```bash
# List all versions of your agent
aws bedrock-agentcore-control list-agent-runtime-versions \
--agent-runtime-id <AGENT_RUNTIME_ID>
# Get details on a specific version
aws bedrock-agentcore-control get-agent-runtime \
--agent-runtime-id <AGENT_RUNTIME_ID> \
--qualifier <VERSION>
```
The `<AGENT_RUNTIME_ID>` comes from `agentcore status --json | jq '.runtimes[0].agentRuntimeId'`.
## Invoking a specific version
By default, callers hit the alias (current version). To pin a call to a specific version, pass `qualifier` in the invoke request:
```python
response = client.invoke_agent_runtime(
agentRuntimeArn="<AGENT_RUNTIME_ARN>",
qualifier="3", # invoke version 3 specifically
payload=payload,
runtimeSessionId=session_id,
)
```
This is useful for:
- Canary testing — send a small percentage of traffic to a new version before cutting over
- A/B comparison — run two versions in parallel and compare outputs
- Debugging — reproduce an issue against a specific version
## Rolling back
If a deploy breaks something, roll back by redeploying the previous known-good code:
```bash
# Option 1: git checkout the previous commit and redeploy
git checkout <PREVIOUS_COMMIT>
agentcore deploy -y
# Option 2: point the alias at an older version (no code rollback needed)
aws bedrock-agentcore-control update-agent-runtime-alias \
--agent-runtime-id <AGENT_RUNTIME_ID> \
--alias-name DEFAULT \
--routing-configuration agentRuntimeVersion=<OLDER_VERSION>
```
Option 2 is faster — no rebuild or redeploy, just a pointer swap. Option 1 is cleaner because your code matches what's running.
## Canary deployment
Split traffic between two versions to validate a new deploy before full rollout:
```bash
aws bedrock-agentcore-control update-agent-runtime-alias \
--agent-runtime-id <AGENT_RUNTIME_ID> \
--alias-name DEFAULT \
--routing-configuration \
agentRuntimeVersion=<NEW_VERSION>,weight=10 \
agentRuntimeVersion=<OLD_VERSION>,weight=90
```
This routes 10% of traffic to the new version. Monitor `agents-optimize` eval scores and error rates before increasing the weight.
## Version cleanup
AgentCore retains versions indefinitely — they don't auto-delete. If you've deployed hundreds of times, consider periodically deleting old versions:
```bash
aws bedrock-agentcore-control delete-agent-runtime-version \
--agent-runtime-id <AGENT_RUNTIME_ID> \
--version <OLD_VERSION>
```
Never delete the current live version.
## Staging targets
For teams that want separate dev/staging/prod environments, use deployment targets:
```json
// agentcore/aws-targets.json
[
{"name": "default", "account": "<DEV_ACCOUNT>", "region": "us-east-1"},
{"name": "staging", "account": "<STAGING_ACCOUNT>", "region": "us-east-1"},
{"name": "production", "account": "<PROD_ACCOUNT>", "region": "us-west-2"}
]
```
```bash
agentcore deploy --target staging -y
agentcore deploy --target production -y
```
Each target gets its own runtime — versions are separate per target.
## Cross-references
- If a rollback is needed because of a specific failure, use `agents-debug` to diagnose first
- For staging/production best practices, see `agents-harden`
- For running evals against a specific version before cutover, see [`agents-optimize/references/evals.md`](../../agents-optimize/references/evals.md)
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---
name: agents-get-started
description: >
Use when a developer wants to create a new agent project or get started
with AgentCore. Handles framework selection, project scaffolding, first
deploy, and first invocation. Triggers on: "build an agent", "create an
agent", "get started", "new project", "agentcore create", "which
framework", "Strands vs LangGraph", "hello world agent", "first agent",
"create MCP server", "host MCP server", "agentcore dev", "dev server",
"what port", "local development".
Not for adding capabilities to existing projects — use agents-build
or agents-connect. Strands vs LangGraph in a migration context routes
to agents-build, not here. Connecting to an existing MCP server routes
to agents-connect, not here.
allowed-tools: Read Grep Glob Bash
metadata:
type: skill
version: "1.0.0"
author: aws-agentcore
requires-cli: ">=0.9.0"
---
# get-started
Walk a developer from zero to a running agent on AWS.
## When to use
- Developer wants to build an agent on AWS and doesn't know where to start
- Developer wants to create a new AgentCore project
- Developer is choosing between frameworks (Strands, LangGraph, GoogleADK, OpenAI Agents)
- Developer just ran `agentcore create` and wants to know what to do next
Do NOT use for:
- Environment/prerequisite issues (CLI not found, credentials broken) → use `agents-debug`
- Adding capabilities to an existing project (memory, tools, policies) → use `agents-build` or `agents-connect`
- Migrating an existing Bedrock Agent → use `agents-build` (loads [`references/migrate.md`](../agents-build/references/migrate.md))
## Input
`$ARGUMENTS` can be:
- A framework preference: "using LangGraph", "with Strands"
- A protocol: "MCP server", "A2A"
- A description of what the agent should do: "a customer support agent"
- Empty — the skill will guide framework selection
## Process
### Step 0: Verify CLI version
```bash
agentcore --version
```
This skill requires v0.9.0 or later.
If the version is older:
> Your AgentCore CLI is out of date (found vX.Y.Z, need v0.9.0+).
Offer to run the update: `agentcore update`. After the update completes, re-check the version to confirm it's ≥0.9.0 before continuing. Preserve any context the developer already provided (framework preference, project name, what they want to build) so they don't have to repeat themselves.
If `agentcore` is not found:
> The AgentCore CLI isn't installed. Run `npm install -g @aws/agentcore` (requires Node.js 20+).
> If you're having trouble with installation, I can run the `agents-debug` skill (which loads [`references/doctor.md`](../agents-debug/references/doctor.md)) to diagnose your environment.
### Step 1: Determine intent — exploring or ready to create?
Before jumping into framework selection, figure out where the developer is:
**Ask the developer:** "Are you exploring options (comparing frameworks, understanding what AgentCore does) or ready to create a project?"
- **Exploring** → Go to Step 2 (framework comparison). Present the options, answer questions, and wait. Do not construct a `create` command until they signal they're ready.
- **Ready to create** → Skip to Step 3 (create the project). If they already specified a framework, skip Step 2 entirely.
- **Already has a project** → Look for `agentcore/agentcore.json` in the current directory. If found, read it and skip to Step 5 (what to do next). Don't re-scaffold.
If the developer's intent is clear from `$ARGUMENTS` (e.g., "create a Strands agent called MyBot"), skip straight to Step 3.
### Step 2: Framework selection
**Check conversation context first.** If the developer already discussed frameworks earlier in this conversation (e.g., from a previous skill invocation), don't re-present the full table. Summarize what was discussed and ask if they've decided, or if anything changed.
If this is the first time discussing frameworks, present the options:
**Supported frameworks (CLI-scaffolded, Python):**
| Framework | CLI value | Best for |
|---|---|---|
| Strands | `Strands` | AWS-native, simplest path, best AgentCore integration |
| LangGraph | `LangChain_LangGraph` | Complex graph-based workflows, existing LangChain investment |
| Google ADK | `GoogleADK` | Teams already using Google's agent toolkit |
| OpenAI Agents | `OpenAIAgents` | Teams already using OpenAI's agent SDK |
**Ask the developer to choose.** Present the options and wait for their selection. Don't assume a default unless they explicitly say they have no preference.
> **Note on naming:** The CLI flag value is the exact string to pass to `--framework`. In prose use the shorter names.
**Default recommendation** (only when the developer says "no preference" or "you pick"): Strands — AWS-native framework with the tightest AgentCore integration and the most samples/docs.
**Key decision points to surface:**
- "Do you have existing agent code in LangGraph or OpenAI Agents?" → use that framework
- "Do you need complex graph-based workflows with conditional branching?" → LangGraph
- "Starting fresh with no preference?" → Strands
#### Framework not listed?
If the developer asks about a framework not in the table above, handle it:
| They ask about | What to say |
|---|---|
| **CrewAI, AutoGen, Semantic Kernel** | Not scaffolded by the CLI, but you can use them via the BYO Container path (below). AgentCore Runtime is framework-agnostic — any code that implements the HTTP contract works. |
| **Anthropic SDK / Claude Agent SDK** | This is a model SDK, not an agent framework. You can use it inside any framework (Strands, LangGraph, etc.) or standalone. For standalone use, wrap it in a container with the Runtime contract. |
| **Claude Code / Cursor / Copilot** | These are IDE tools, not agent frameworks. They're where you *write* agent code, not what you deploy. Pick a framework from the table above for the agent itself. |
| **LangChain (without LangGraph)** | LangChain is a library, LangGraph is the agent framework built on it. The CLI scaffolds LangGraph. If you're using plain LangChain chains, the BYO Container path works. |
| **Custom / homegrown framework** | BYO Container path — see below. |
**BYO Container path (any framework, any language):**
For frameworks or languages not scaffolded by the CLI, AgentCore Runtime accepts any container that implements the HTTP contract (`POST /invocations`, `GET /ping`). The workflow:
1. `agentcore create --name <ProjectName> --defaults` to scaffold the project structure
2. `agentcore add agent --type byo --build Container --language <Language> --code-location <path>` to register your code
3. Write a `Dockerfile` that builds and runs your agent
4. `agentcore deploy` handles ECR push, CDK infra, and runtime creation
**Language-specific notes:**
| Language | Recommended path |
|---|---|
| Java (Spring Boot) | [Spring AI SDK for AgentCore](https://aws.amazon.com/blogs/machine-learning/spring-ai-sdk-for-amazon-bedrock-agentcore-is-now-generally-available) — handles the Runtime contract, SSE streaming, and health checks. Use `--language Other --build Container`. |
| JavaScript / TypeScript | Implement the Runtime contract in Express/Fastify/etc. Use `--language TypeScript --build Container`. |
| Go, Rust, .NET, other | Implement the Runtime HTTP contract. Use `--language Other --build Container`. |
The rest of this skill (deploy, status, logs, invoke) applies once the container builds correctly.
#### Framework vs. model provider — a common confusion
The framework is how your agent orchestrates (Strands, LangGraph, etc.). The model provider is which LLM it calls (Bedrock, Anthropic, OpenAI, Gemini). These are independent choices:
- Strands + Bedrock (default) — AWS-native everything
- Strands + Anthropic — Strands orchestration, direct Anthropic API for the model
- LangGraph + Bedrock — LangGraph orchestration, Bedrock for the model
- OpenAI Agents + OpenAI — OpenAI everything
If the developer says "I want to use Claude" they mean the model provider (Bedrock or Anthropic), not the framework. If they say "I want to use LangGraph" they mean the framework.
### Step 3: Create the project
Build the `agentcore create` command based on the developer's choices.
**Before constructing the command — validate the project name.** The CLI fails late: if the name is invalid, you'll see the error *after* walking through prompts or building the full command. Save the round-trip and check these rules up front. Reject the name and ask for a new one if any rule fails:
- **Length ≤ 23 characters** (this is shorter than most developers assume — `MyCustomerSupportAgent` is 22 chars and fits; `CustomerSupportChatbot` is 22 and fits; `MyCustomerSupportBotApp` is 23 and just fits; `MyCustomerSupportChatBot` is 24 and **fails**)
- **Alphanumeric only** — no hyphens, underscores, dots, or spaces
- **Must start with a letter**
Say the count back out loud when close to the limit: "That name is 24 characters — the CLI caps project names at 23. Want to shorten it to `<suggestion>`?" Do not run the command with an invalid name on the assumption that the CLI error message will be clear — it isn't always, and the developer's mental model will be wrong for subsequent commands.
**Construct the command, then present it for confirmation before the developer runs it.** Show the full command with all flags and explain what each choice means. Wait for the developer to confirm or adjust before proceeding.
Example presentation:
> Here's the command I'd recommend based on what you've told me:
>
> ```bash
> agentcore create --name MyAgent --framework Strands --model-provider Bedrock --build CodeZip --memory none
> ```
>
> This creates a Strands agent using Bedrock models, deployed as a code zip (no Docker needed). Memory can be added later.
>
> Want to run this, or change anything?
Do NOT execute the command automatically — present it and wait.
**Minimal (defaults — Strands, Bedrock, CodeZip, no memory):**
```bash
agentcore create --name <ProjectName> --defaults
```
**With specific options:**
```bash
agentcore create \
--name <ProjectName> \
--framework <Framework> \
--model-provider Bedrock \
--build CodeZip \
--memory none
```
**Flag reference:**
| Flag | Values | Default |
|---|---|---|
| `--name` | alphanumeric, max 23 chars | prompted |
| `--framework` | `Strands`, `LangChain_LangGraph`, `GoogleADK`, `OpenAIAgents` | prompted |
| `--protocol` | `HTTP`, `MCP`, `A2A` | `HTTP` |
| `--build` | `CodeZip`, `Container` | `CodeZip` |
| `--model-provider` | `Bedrock`, `Anthropic`, `OpenAI`, `Gemini` | prompted |
| `--memory` | `none`, `shortTerm`, `longAndShortTerm` | prompted |
| `--network-mode` | `PUBLIC`, `VPC` | `PUBLIC` |
| `--dry-run` | — | preview without creating |
**Guidance on choices:**
- **Protocol:** Use `HTTP` unless the developer specifically needs MCP tool serving or A2A agent-to-agent communication
- **Build:** Use `CodeZip` unless the developer needs custom system dependencies (CodeZip is faster to deploy and doesn't require Docker locally)
- **Model provider:** Use `Bedrock` unless the developer has a specific reason for another provider (Bedrock doesn't require managing API keys)
- **Memory:** Start with `none` — memory can be added later via `agents-build` (loads [`references/memory.md`](../agents-build/references/memory.md)) when the developer needs it
### Step 4: Explain what was created
After the project exists, read `agentcore/agentcore.json` and the generated code to explain the project structure.
The layout below reflects CLI v0.9.x. If the CLI version is different, run `tree <ProjectName>/ -L 3` to see the actual generated structure and explain from there.
```
<ProjectName>/
├── agentcore/
│ ├── agentcore.json ← Project config (agents, resources)
│ ├── aws-targets.json ← AWS account + region
│ ├── .env.local ← Local environment variables (gitignored)
│ └── cdk/ ← CDK infrastructure (auto-managed, don't edit)
└── app/
└── <AgentName>/
├── main.py ← Your agent code — this is where you build
├── mcp_client/ ← Pre-wired example MCP client (see note below)
└── pyproject.toml ← Python dependencies
```
**Key files to highlight:**
- `app/<AgentName>/main.py` — the agent's entry point. This is where the developer adds tools, system prompts, and logic.
- `agentcore/agentcore.json` — the project config. Resources are added here via `agentcore add` commands.
- `agentcore/.env.local` — local environment variables. After deploy, resource IDs are written here for local dev.
**Heads-up on the scaffolded MCP client.** `main.py` imports `get_streamable_http_mcp_client()` from `mcp_client/client.py` and appends it to `tools`. In a fresh project, this client points at a public example MCP endpoint — so `agentcore dev` works immediately. Two things to flag:
1. **It will become a silent no-op if you repoint it at a gateway that isn't deployed yet.** The common path is to swap the example endpoint for `os.getenv("AGENTCORE_GATEWAY_<NAME>_URL")`. That env var is only populated after `agentcore deploy`. If the developer repoints and runs `agentcore dev` before deploying, `get_streamable_http_mcp_client()` returns a client with a `None` URL and the agent starts with zero MCP tools — no error, no warning. See the "Local dev gap" section in `agents-connect` for the guard pattern: `if not GATEWAY_URL: tools = []`.
2. **If the developer doesn't need MCP tools at all**, remove the `mcp_clients` list and the loop that appends it to `tools`. The scaffold includes it as a convenience, not a requirement.
The reference client code in `agents-connect` (Path A) shows the correct pattern for gateway-backed MCP clients once deploy has run.
### Step 5: Local development
```bash
agentcore dev
```
This starts a local dev server. The developer can interact with their agent immediately.
**Port the dev server binds to** (important if you're scripting `curl` calls or testing from another process):
| Protocol | Default port |
|---|---|
| HTTP | `8080` |
| MCP | `8000` |
| A2A | `9000` |
The CLI prints the bound port and URL on startup — always read the actual value from the CLI output rather than hardcoding. **If the default port is already in use**, the CLI auto-increments (e.g., 8080 → 8081 → 8082), so a second dev session or a lingering process from a previous run can shift your port without warning. Use `agentcore dev --port <N>` to pin it, or grep `ps` / check the CLI banner if invocations start failing with connection-refused or exit-code-7 errors.
**Important limitations to mention:**
- Memory is not available in `agentcore dev` — it requires a deploy
- Gateway URLs are not available locally — they require a deploy
- The local server uses the model provider configured in the project
### Step 6: First deploy
When the developer is ready to deploy:
```bash
agentcore deploy
```
This will:
1. Show a preview of AWS resources to be created
2. Ask for confirmation
3. Build and deploy via CDK
**First deploy takes 3-5 minutes.** Subsequent deploys are faster.
After deploy, show them how to invoke:
```bash
agentcore invoke "Hello, what can you do?"
```
And how to check status:
```bash
agentcore status
```
### Step 7: What's next
Based on what the developer said they want to build, suggest the logical next skill:
| Developer intent | Next skill | Command hint |
|---|---|---|
| "How do I call it from my app?" | `agents-build` | `agentcore fetch access` |
| "I want it to remember things" | `agents-build` | `agentcore add memory` |
| "I want it to call external APIs" | `agents-connect` | `agentcore add gateway` |
| "I want to restrict what it can do" | `agents-connect` | `agentcore add policy-engine` |
| "I want to measure quality" | `agents-optimize` | `agentcore add evaluator` |
| "I want to go to production" | `agents-harden` | production readiness checklist |
| "I want multiple agents working together" | `agents-build` | `agentcore create --protocol A2A` |
| "I need it in a VPC" | `agents-build` | `agentcore create --network-mode VPC` |
Don't overwhelm — suggest one or two next steps based on what the developer actually asked for.
### Example walkthroughs
For task-framed prompts (e.g., "build a customer support agent"), load the matching example reference:
| Developer task | Reference |
|---|---|
| Customer support, chatbot, answer policy questions | [`references/example-support-agent.md`](references/example-support-agent.md) |
More examples can be added to this skill's references directory as common patterns emerge.
## Output
- A clear path from "I want to build an agent" to a running deployed agent
- The `agentcore create` command tailored to their choices
- An explanation of the generated project structure
- Concrete next steps based on their intent
## Quality criteria
- The `agentcore create` command uses only valid flags from CLI v0.9.1
- Framework recommendation is based on the developer's context, not a generic default
- The developer understands what each generated file does
- Next steps are specific to what the developer wants to build, not a generic list of all features
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# Example: Customer Support Agent
A complete, realistic example of a customer support agent scaffolded with `agentcore create`. Use this as a reference when the developer asks to "build a customer support agent" or similar task-framed prompts.
## What this agent does
Answers customer questions about product policies, shipping, and returns. Uses Strands as the framework, Bedrock (Claude Sonnet) as the model, and starts without memory or tools (both can be added later).
## Scaffold command
```bash
agentcore create \
--name SupportAgent \
--framework Strands \
--protocol HTTP \
--build CodeZip \
--model-provider Bedrock \
--memory none
```
## Generated `main.py` (annotated)
After scaffolding, `app/SupportAgent/main.py` looks something like:
```python
from bedrock_agentcore.runtime import BedrockAgentCoreApp
from strands import Agent
from model.load import load_model # scaffolded by `agentcore create` in model/load.py
app = BedrockAgentCoreApp()
SYSTEM_PROMPT = """You are a customer support agent for Acme Corp.
You answer questions about product policies, shipping, and returns.
Guidelines:
- Be concise and friendly
- If you don't know the answer, say so — don't make up policies
- For order-specific questions, ask for the order number
- Escalate to a human agent if the customer expresses frustration"""
@app.entrypoint
def invoke(payload, context):
agent = Agent(
model=load_model(),
system_prompt=SYSTEM_PROMPT,
)
result = agent(payload.get("prompt", ""))
return {"response": str(result)}
if __name__ == "__main__":
app.run()
```
> The generated `model/load.py` returns a `BedrockModel` configured with a cross-region inference profile (e.g., `global.anthropic.claude-sonnet-4-5-*`). Using `load_model()` instead of hardcoding the model ID means your code tracks whatever default the CLI ships. To use a different model, edit `model/load.py`.
## Try it locally
```bash
agentcore dev
```
In another terminal:
```bash
curl -X POST http://localhost:8080/invocations \
-H "Content-Type: application/json" \
-d '{"prompt": "What is your return policy?"}'
```
## Deploy it
```bash
agentcore deploy
```
## Natural next steps
After the basic agent is working, the developer typically asks for one of these next:
| "I want to..." | Next skill |
|---|---|
| "Let it look up orders in our database" | `agents-connect` (add a gateway target for the order API) |
| "Remember the customer's name between sessions" | `agents-build` (loads [`references/memory.md`](../../agents-build/references/memory.md)) |
| "Make sure it can't say anything off-policy" | `agents-connect` (loads [`references/policy.md`](../../agents-connect/references/policy.md)) |
| "Put it on our website" | `agents-build` (loads [`references/integrate.md`](../../agents-build/references/integrate.md)) |
| "Know if it's actually helpful" | `agents-optimize` |
## Variations
### LangGraph variant
```bash
agentcore create --name SupportAgent --framework LangChain_LangGraph --model-provider Bedrock --memory none
```
Generated `main.py` uses `create_react_agent` and `langchain_aws`:
```python
from langchain_core.messages import HumanMessage, SystemMessage
from langgraph.prebuilt import create_react_agent
from bedrock_agentcore.runtime import BedrockAgentCoreApp
from model.load import load_model
app = BedrockAgentCoreApp()
SYSTEM_PROMPT = "..." # same as Strands version
@app.entrypoint
async def invoke(payload, context):
graph = create_react_agent(load_model(), tools=[])
result = await graph.ainvoke({
"messages": [
SystemMessage(content=SYSTEM_PROMPT),
HumanMessage(content=payload["prompt"]),
]
})
return {"response": result["messages"][-1].content}
if __name__ == "__main__":
app.run()
```
### OpenAI Agents SDK variant
```bash
agentcore create --name SupportAgent --framework OpenAIAgents --model-provider OpenAI --memory none
```
```python
from agents import Agent, Runner
from bedrock_agentcore.runtime import BedrockAgentCoreApp
app = BedrockAgentCoreApp()
@app.entrypoint
async def invoke(payload, context):
agent = Agent(
name="SupportAgent",
instructions="...", # same as Strands version
)
result = await Runner.run(agent, payload["prompt"])
return {"response": result.final_output}
if __name__ == "__main__":
app.run()
```
### Google ADK variant
```bash
agentcore create --name SupportAgent --framework GoogleADK --model-provider Gemini --memory none
```
```python
from google.adk.agents import Agent
from google.adk.runners import Runner
from google.adk.sessions import InMemorySessionService
from google.genai import types
from bedrock_agentcore.runtime import BedrockAgentCoreApp
app = BedrockAgentCoreApp()
agent = Agent(
model="gemini-2.5-flash",
name="SupportAgent",
description="Customer support agent",
instruction="...", # same as Strands version
)
@app.entrypoint
async def invoke(payload, context):
user_id = payload.get("user_id", "default_user")
session_id = getattr(context, "session_id", "default_session")
session_service = InMemorySessionService()
session = await session_service.create_session(
app_name="support", user_id=user_id, session_id=session_id
)
runner = Runner(agent=agent, app_name="support", session_service=session_service)
content = types.Content(role="user", parts=[types.Part(text=payload["prompt"])])
async for event in runner.run_async(user_id=user_id, session_id=session.id, new_message=content):
if event.is_final_response():
return {"response": event.content.parts[0].text}
if __name__ == "__main__":
app.run()
```
### Model provider options
The CLI supports four model providers:
| Provider | Best for | Notes |
|---|---|---|
| `Bedrock` | Default, no API key needed, IAM-based auth | Uses cross-region inference profiles (e.g., `global.anthropic.claude-sonnet-4-5-*`) |
| `Anthropic` | Direct Anthropic API access | Requires `ANTHROPIC_API_KEY`; model IDs like `claude-sonnet-4-5-20250929` |
| `OpenAI` | GPT-4 / GPT-5 models | Requires `OPENAI_API_KEY`; typically paired with OpenAI Agents SDK |
| `Gemini` | Google Gemini models | Requires `GEMINI_API_KEY`; typically paired with Google ADK |
For cost-sensitive use cases, consider Bedrock Nova models (e.g., `amazon.nova-micro-v1:0`, `amazon.nova-lite-v1:0`) — significantly cheaper than Claude for simpler extractive tasks. See [`agents-optimize/references/cost.md`](../../agents-optimize/references/cost.md) for model selection guidance.
For a chatbot that remembers conversations, add `--memory longAndShortTerm` during scaffolding. Memory can also be added later — see [`agents-build/references/memory.md`](../../agents-build/references/memory.md).
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---
name: agents-harden
description: >
Use when preparing your agent for production — IAM scoping, inbound
auth (JWT, SigV4), secrets management, cold start optimization, session
lifecycle, rate limiting, input validation, and quota guidance. Triggers
on: "production checklist", "harden agent", "production ready", "secure
agent", "inbound auth", "going live", "cold start optimization", "session
lifecycle", "StopRuntimeSession", "quota", "throttling", "maxVms",
"rate limit", "security audit of outbound API calls", "gateway target
audit for production", "restrict who can call", "lock down endpoint",
"only our app can call".
Not for Cedar tool-restriction policies — use agents-connect. Not
for quality measurement — use agents-optimize. Not for outbound
credential storage or API key wiring — use agents-connect. Not for
A2A agent-to-agent auth — use agents-build. Cold start observation
and diagnosis (not optimization) routes to agents-debug.
allowed-tools: Read Grep Glob Bash
metadata:
type: skill
version: "1.0.0"
author: aws-agentcore
requires-cli: ">=0.9.0"
---
# harden
Prepare your AgentCore agent for production — security, reliability, and performance.
## When to use
- You're about to take an agent to production
- You want a checklist of what to review before launch
- You want to restrict who can call your agent
- You want to scope down IAM permissions from the defaults
- You're hitting throttling or quota errors (loads [`references/limits.md`](references/limits.md))
- You need to tune session lifecycle for your workload
- You're running long-running background work in your agent
## Input
No arguments required. The skill reads your project config and produces a checklist with specific findings for your project.
## Process
### Step 0: Verify CLI version
Run `agentcore --version`. This skill requires v0.9.0 or later. If the version is older, tell the developer to run `agentcore update` before proceeding.
### Step 1: Read the project
Read `agentcore/agentcore.json` to understand:
- What resources are configured (memory, gateway, credentials, evaluators)
- What framework is being used
- What network mode is configured (PUBLIC or VPC)
### Step 2: Run through the checklist
Work through each category and report findings specific to the project.
---
## IAM: Scope down permissions
The auto-created execution role has broad Bedrock access (`arn:aws:bedrock:*::foundation-model/*`). For production, scope it to the specific models your agent uses.
**Check the current execution role:**
```bash
agentcore status --json | jq -r '.runtimes[0].executionRoleArn'
```
**Recommended production Bedrock policy:**
```json
{
"Effect": "Allow",
"Action": [
"bedrock:InvokeModel",
"bedrock:InvokeModelWithResponseStream"
],
"Resource": [
"arn:aws:bedrock:<REGION>::foundation-model/anthropic.claude-sonnet-4-5-20250929-v1:0"
]
}
```
Replace the resource ARN with the specific model(s) your agent uses.
**ECR access:** Scope to your specific repository:
```json
{
"Effect": "Allow",
"Action": ["ecr:BatchGetImage", "ecr:GetDownloadUrlForLayer"],
"Resource": "arn:aws:ecr:<REGION>:<YOUR_ACCOUNT_ID>:repository/bedrock-agentcore-<AGENT_NAME>-*"
}
```
**Trust policy:** Verify the execution role's trust policy is scoped to your account:
```json
{
"Principal": {"Service": "bedrock-agentcore.amazonaws.com"},
"Action": "sts:AssumeRole",
"Condition": {
"StringEquals": {"aws:SourceAccount": "<YOUR_ACCOUNT_ID>"},
"ArnLike": {"aws:SourceArn": "arn:aws:bedrock-agentcore:<REGION>:<YOUR_ACCOUNT_ID>:*"}
}
}
```
**Runtime resource-based policies** (API-only): For fine-grained control over which principals can invoke your runtime — beyond what IAM roles and JWT auth provide — use `PutAgentRuntimeResourcePolicy` via boto3. This is not exposed in the CLI or `agentcore.json`. Use the `awsknowledge` MCP server if available to look up the current API shape.
---
## Shell Access: Scope `InvokeAgentRuntimeCommand` separately
If your project uses `InvokeAgentRuntimeCommand` (see [`agents-build/references/integrate.md`](../agents-build/references/integrate.md)), audit its IAM permissions separately from `InvokeAgentRuntime`. The two actions have different blast radii: `InvokeAgentRuntimeCommand` is arbitrary shell execution inside a live microVM with the runtime's full execution role — callers can read/write the filesystem, reach any network resource the agent can reach, and access the execution role's credentials.
**Check which principals have the permission:**
```bash
# List customer-managed policies in your account, then inspect each for InvokeAgentRuntimeCommand
aws iam list-policies --scope Local \
--query 'Policies[*].[PolicyName, Arn, DefaultVersionId]' \
--output table
# Then for each policy of interest:
aws iam get-policy-version \
--policy-arn <POLICY_ARN> \
--version-id <VERSION_ID> \
--query 'PolicyVersion.Document'
```
Alternatively, use the IAM console: **IAM → Policies → Filter by type: Customer managed** → search for `InvokeAgentRuntimeCommand` in the policy JSON editor.
**Separate IAM policy for command callers** — keep this distinct from the policy granting `InvokeAgentRuntime`:
```json
{
"Version": "2012-10-17",
"Statement": [{
"Effect": "Allow",
"Action": "bedrock-agentcore:InvokeAgentRuntimeCommand",
"Resource": "arn:aws:bedrock-agentcore:<REGION>:<YOUR_ACCOUNT_ID>:runtime/<RUNTIME_NAME>-*"
}]
}
```
**Enable CloudTrail alerting.** Create an EventBridge rule to notify your security team when `InvokeAgentRuntimeCommand` is called:
```bash
aws events put-rule \
--name AgentCoreCommandExecution \
--event-pattern '{"source":["aws.bedrock-agentcore"],"detail-type":["AWS API Call via CloudTrail"],"detail":{"eventName":["InvokeAgentRuntimeCommand"]}}' \
--state ENABLED
```
**If commands are constructed from user input anywhere in calling code:** validate before passing — reject strings containing `&&`, `;`, `$(...)`, backticks, `|`, or other shell metacharacters.
---
## Inbound auth: Control who can call your agent
By default, agents use AWS IAM (SigV4) for inbound auth. For production, verify this is configured correctly.
**Check current auth config:**
```bash
agentcore status --runtime <AgentName> --json | jq '.runtimes[0].authorizerConfig'
```
**Options:**
`AWS_IAM` (default) — callers must sign requests with SigV4. Good for internal services and AWS-native clients.
`CUSTOM_JWT` — callers present a JWT from your identity provider. Good for web/mobile apps and external clients.
```bash
agentcore add agent \
--name MyAgent \
--authorizer-type CUSTOM_JWT \
--discovery-url https://your-idp.example.com/.well-known/openid-configuration \
--allowed-audience my-api \
--allowed-clients my-client-id
```
> [!WARNING]
> Never use `--authorizer-type NONE` in production. It allows unauthenticated access
> to your agent — anyone with the endpoint URL can invoke it. Always use AWS_IAM or
> CUSTOM_JWT. If you see NONE in production, change it immediately.
### Choosing `allowedClients` vs `allowedAudience`
This is the most common JWT misconfiguration. The right choice depends on what's inside the token your IdP issues.
**Decode a sample token** (at your IdP or with `jwt.io`) and look at the payload:
- Token has a `client_id` claim, no `aud` claim → configure **`allowedClients`** on the runtime
- Token has an `aud` claim → configure **`allowedAudience`** on the runtime
- Token has both → use `allowedAudience`. The `aud` claim is the standard OIDC audience field; use that as the primary check.
If you pick the wrong one, invocations return 403 even with a valid token — the runtime is validating against a claim the token doesn't have.
### Issuer ↔ discovery URL prefix requirement
AgentCore enforces the OIDC discovery spec (RFC 8414 §3): the `issuer` value in the discovery document must be a URL prefix of the discovery endpoint.
That means if your discovery URL is `https://qa.example.com/.well-known/openid-configuration`, the `issuer` field in that document must start with `https://qa.example.com`. If the document advertises an issuer like `https://example.com` (no subdomain), validation fails.
Some enterprise IdPs (PingFederate, Paylocity, some Keycloak setups) host the discovery endpoint on an environment-specific subdomain while advertising a production-level issuer. This pattern is incompatible with the RFC 8414 prefix rule.
Fix options:
1. **Align the IdP's discovery endpoint with its issuer** — serve discovery from the same origin as the issuer.
2. **Point the runtime at the actual discovery URL domain** — configure the runtime's discovery URL with the subdomain that matches the token's issuer.
### Debugging JWT auth failures
When invocations fail with 403, narrow down which check is failing.
**`Authorization method mismatch`** — the runtime's auth type and the request's auth type don't match. Two cases:
- The runtime is configured for `AWS_IAM` (or no authorizer) but the caller is sending a Bearer token → reconfigure the runtime for `CUSTOM_JWT`, or have the caller use SigV4.
- The runtime is configured for `CUSTOM_JWT` but the caller's request is being SigV4-signed → likely the SDK or environment is injecting SigV4 headers alongside the Bearer token. Check for `X-Amz-Date`, `X-Amz-Security-Token`, or `Authorization: AWS4-HMAC-SHA256` in the outbound request. Remove the SigV4 path and send only the Bearer token.
**`Invalid inbound token`** (or similar) — the token was rejected by the JWT validator. Walk through these in order:
1. **Issuer ↔ discovery URL prefix** (above) — verify the token's `iss` claim matches the discovery URL's origin
2. **`allowedClients` vs `allowedAudience`** — is the runtime configured for the right claim for your token format?
3. **JWKS reachability** — can AgentCore reach the `jwks_uri` listed in the discovery document? It must be publicly reachable.
4. **Token expired** — decode the token, check `exp` against now
5. **Signing algorithm support** — some IdPs sign with algorithms (PS256, ES384, etc.) that aren't universally supported. Check your IdP's supported algorithms and switch to RS256 if compatibility is the issue.
Only after ruling all of those out should you treat it as a service-side issue.
---
## Error handling: Fail gracefully
Check that your agent code handles errors without exposing internal details:
```python
from bedrock_agentcore.runtime import BedrockAgentCoreApp
app = BedrockAgentCoreApp()
@app.entrypoint
def invoke(payload, context):
try:
# your agent logic
return {"response": result}
except Exception as e:
# Log the full error internally
app.logger.error(f"Agent error: {e}", exc_info=True)
# Return a safe message to the caller
return {"error": "An error occurred. Please try again."}
if __name__ == "__main__":
app.run()
```
**Check for:** bare `except` blocks that swallow errors silently, error messages that expose stack traces or internal details to callers, missing error handling in tool call code.
---
## Input validation and rate limiting
Agent entrypoints receive arbitrary payloads from callers. Validate inputs before processing:
```python
@app.entrypoint
def invoke(payload, context):
prompt = payload.get("prompt", "")
# Validate input
if not prompt or not isinstance(prompt, str):
return {"error": "Missing or invalid 'prompt' field"}
if len(prompt) > 10000:
return {"error": "Prompt exceeds maximum length (10,000 characters)"}
# Sanitize — strip control characters, excessive whitespace
prompt = " ".join(prompt.split())
# Proceed with validated input
result = agent(prompt)
return {"response": str(result)}
```
**What to validate:**
- Required fields are present and have the expected type
- String inputs don't exceed reasonable length limits (prevents token-bombing the model)
- Numeric inputs are within expected ranges
- User-provided IDs (actor_id, session_id) match expected formats
**Rate limiting:** AgentCore Runtime has built-in invocation rate limits (default 25 TPS per agent — see [`references/limits.md`](references/limits.md)). For application-level rate limiting (per-user, per-tenant), implement it in your calling application or API Gateway layer, not in the agent code itself. The agent should assume it's already been rate-limited by the time a request reaches it.
---
## Secrets: No credentials in code, no secrets in runtime env vars
Two failure modes to check for:
### 1. Hardcoded secrets in agent code
```bash
# Search for common secret patterns in agent code
grep -r "sk-\|api_key\s*=\s*['\"]" app/ --include="*.py"
grep -r "password\s*=\s*['\"]" app/ --include="*.py"
```
### 2. Secrets pulled from runtime environment variables
AgentCore Runtime environment variables are **not** vault-backed. Anything a developer stuffs into the runtime's env (via CDK, boto3 `UpdateAgentRuntime`, or similar) is a plaintext config value, not a secret. Audit for the pattern:
```bash
# Flag any os.getenv / os.environ call whose name implies a secret
grep -rE "os\.(getenv|environ).*(TOKEN|SECRET|KEY|PASSWORD|CREDENTIAL)" app/ --include="*.py"
```
Non-secret identifiers injected by the platform are fine and should not match an allowlist (e.g., `MEMORY_*_ID`, `AGENTCORE_GATEWAY_*_URL`, `AWS_REGION`, downstream agent ARNs). Review hits and confirm none are secrets.
**Correct pattern:** Register each outbound credential with `agentcore add credential`, then fetch it in code via the integrated credential providers:
```python
from bedrock_agentcore.identity.auth import requires_api_key, requires_access_token
@requires_api_key(provider_name="MyAPI")
def call_api(payload: dict, *, api_key: str) -> dict:
...
@requires_access_token(provider_name="MyOAuthProvider", scopes=["read"], auth_flow="M2M")
async def call_downstream(data: dict, *, access_token: str) -> dict:
...
```
The decorator fetches from Secrets Manager at call time and handles caching/refresh. Credentials registered this way are encrypted at rest and rotated without a redeploy.
**Local dev:** `agentcore/.env.local` (gitignored) is read by `agentcore dev` so the decorator resolves locally. This file is **not** uploaded to runtime on deploy — production credentials live in the credential provider.
---
## Tool surface: Prefer Gateway targets over direct HTTP in agent code
A related audit — for every external service the agent calls, ask whether it should be a Gateway target instead of a direct HTTP call buried in agent code. Gateway's credential providers inject auth at the edge (so the agent process never sees the secret), the tool catalog is policy-enforceable, and a leaked traceback/log line from agent code can't exfiltrate credentials that never reached it.
```bash
# Find direct outbound HTTP calls in agent code
grep -rEn 'httpx\.|requests\.|aiohttp\.' app/ --include="*.py"
```
For each hit, decide:
| Hit looks like | Action |
|---|---|
| Calls an external REST API the agent treats as a tool | Front as a Gateway target (`agentcore add gateway-target --type open-api-schema` or `api-gateway`). Load [`agents-connect/SKILL.md`](../agents-connect/SKILL.md) Path C. |
| Calls an MCP server directly | Front as a Gateway target (`--type mcp-server`). Load [`agents-connect/SKILL.md`](../agents-connect/SKILL.md) Path A. |
| Calls an AWS service (S3, DynamoDB, etc.) — not appropriate to match this row, should be `boto3` | Migrate from `requests`/`httpx` to the `boto3` client, using the runtime's execution role for IAM. No credential needed. |
| Calls a streaming service (SSE-with-live-output, WebSocket, WebRTC) | OK to keep direct — Gateway doesn't front these yet. Confirm any auth uses `@requires_*`, not `os.getenv`. |
| Calls another agent via A2A | OK to keep direct — A2A is HTTP-by-design. Confirm it uses `@requires_access_token` for the bearer token. |
| Calls a measured latency hot path and the team chose it | OK, but confirm measurement exists and auth uses `@requires_*`. |
If the hit fits none of the "OK to keep direct" rows, open a ticket to convert it to a Gateway target. Gateway targets can be added without a code change in the agent for most framework integrations (MCP tool discovery handles binding).
---
## Observability: Verify tracing is enabled
AgentCore enables X-Ray tracing and CloudWatch logging automatically. Verify:
```bash
agentcore status --runtime <AgentName> --json | jq '.runtimes[0].observabilityConfig'
```
**CloudWatch dashboard:** AWS Console → CloudWatch → GenAI Observability → Bedrock AgentCore
**Log retention:** By default, logs are retained indefinitely. Set a retention policy for cost control:
```bash
aws logs put-retention-policy \
--log-group-name /aws/bedrock-agentcore/runtimes/<AGENT_ID>-DEFAULT \
--retention-in-days 30
```
---
## Evaluation baseline: Know your quality before launch
Before going to production, establish a quality baseline so you can detect regressions:
```bash
# Run a baseline eval
agentcore run eval \
--evaluator "Builtin.Helpfulness" \
--evaluator "Builtin.GoalSuccessRate"
# Set up continuous monitoring
agentcore add online-eval \
--name production_monitor \
--runtime <AgentName> \
--evaluator "Builtin.Helpfulness" \
--sampling-rate 5
agentcore deploy -y
```
Record the baseline scores. If scores drop significantly after a change, investigate before continuing.
---
## Network: VPC for private resources
If your agent accesses private AWS resources (RDS, internal APIs), configure VPC:
```bash
agentcore add agent \
--name MyAgent \
--network-mode VPC \
--subnets subnet-abc,subnet-def \
--security-groups sg-123
```
See `agents-build` (loads [`references/vpc.md`](../agents-build/references/vpc.md)) for full VPC configuration guidance.
---
## Initialization time: Optimize cold start performance
Slow agent initialization causes timeouts, 424 errors, and poor user experience — especially on first invocation after a period of inactivity. Everything the agent does before it's ready to handle a request adds to the time users wait.
### Where cold start time actually goes
A typical cold start for a new environment takes around 20–30 seconds. The breakdown, roughly:
- **Container image pull** — dominates for Container builds. A 100 MB image takes a few seconds; a 500 MB image can take 15+ seconds.
- **Application startup** — your code's import time, framework init, module-level setup. Usually 5–10 seconds, can be much more if you're loading models or opening connections at import.
- **Platform overhead** (microVM boot, network attach, container start) — sub-second to a couple of seconds.
The two you control are image size and application startup. Optimizing either one directly reduces time to first response.
### Session reuse is the highest-leverage optimization
Same-session requests route to an existing initialized environment — no cold start. The first request per session pays the cold-start cost; every subsequent request on that session is fast.
Concrete patterns:
- **Multi-turn conversations:** reuse the same `session_id` across turns. Don't generate a new UUID per turn.
- **Batch processing:** reuse the same `session_id` across items in the batch.
- **User-facing apps:** scope a session to a user interaction (e.g., one session per chat conversation), not one session per message.
Cross-SDK note: if you're using MCP, pass **one** session identifier, not both `runtimeSessionId` and `mcpSessionId` at once. Sending both can cause the platform to bind two separate environments to the same logical session, doubling cold-start cost.
### Package size budget
Every MB of deployment package adds to cold-start time.
- **Target:** under 200 MB. Aim for under 100 MB if you can.
- **For Container builds:** multi-stage Dockerfiles, slim or distroless base images, remove build tools and test files, add a `.dockerignore`.
- **For CodeZip builds:** prune dev dependencies from `pyproject.toml` / `requirements.txt`. Don't ship `tests/`, `docs/`, `.git/`, local caches.
- **Audit regularly:** `pip list` (Python) or `npm ls` (Node) will show you what's actually installed. Remove anything you're not using.
### Defer heavy initialization
Don't load large models, connect to databases, or initialize MCP clients at module import time. Every second spent in module import is a second the agent can't respond to requests.
```python
# ❌ Slow — runs at import time, before the agent can handle requests
import heavy_library
client = heavy_library.Client(config)
# ✅ Fast — defers until first request
_client = None
def get_client():
global _client
if _client is None:
import heavy_library
_client = heavy_library.Client(config)
return _client
```
### Choose deployment type based on traffic pattern, not by default
The skill previously recommended CodeZip over Container when possible. That's an oversimplification. Here's the real trade-off:
- **CodeZip:** simpler to iterate on, smaller surface area. Cold start includes code download + extract — a ~95 MB package adds around 1.3 seconds of platform download before application startup even begins.
- **Container:** you control the full image, needed for custom system dependencies. Larger images cost more per cold start, but you can optimize aggressively with multi-stage builds.
Neither wins universally. Both benefit the same way from session reuse and from keeping the package small. If your traffic pattern has lots of bursty cold sessions, invest in shrinking whichever deployment artifact you're using. If your traffic pattern reuses sessions, the deployment type matters much less.
### For Lambda targets behind Gateway
Use provisioned concurrency on the Lambda function to eliminate Lambda cold starts. This is separate from Runtime initialization — it's the Lambda itself that adds latency on first invocation of a cold Lambda.
---
## Session lifecycle management
Session management is tightly linked to cost, performance, and the `maxVms` quota. Getting this right is often the difference between a smooth production launch and a quota-blocked one.
### The default lifecycle
When a request arrives with a new session ID, the runtime initializes a fresh environment for it. That environment stays alive until one of:
1. **The session is explicitly stopped** via `StopRuntimeSession`.
2. **The idle timeout expires.** The runtime reclaims environments that haven't received a request for `idleRuntimeSessionTimeout` (default 900 seconds).
3. **The maximum lifetime is reached** (`maxLifetime`, default 8 hours).
Idle environments count against your `maxVms` quota until they're reclaimed, even though they're not serving traffic. This is the #1 cause of unexpected `maxVms` errors.
### Pick timeouts by workload shape
Don't leave defaults for production. Pick values that match how your workload actually uses sessions:
| Workload | `idleRuntimeSessionTimeout` | `maxLifetime` | Reasoning |
|---|---|---|---|
| Interactive chat / support agent | 600–900s (default) | 3600–7200s | Users pause to read/think. Reclaim fast after they leave. |
| Request/reply API with no follow-up | 60–120s | 1800s | Each call is self-contained — release the VM quickly. |
| Batch processing, one session per job | 120s | match job length + buffer | Idle gap between items in the batch is small; reclaim aggressively between jobs. |
| Background / long-running tasks (use `add_async_task`) | 120–300s | up to 28800s (8h) | Async task API keeps the VM alive during tracked work; idle timeout applies between tasks. |
**Trade-offs at a glance:**
- **Low idle timeout** = more headroom under `maxVms`, lower cost. **Risk:** reclaim mid-conversation causing next turn to cold-start.
- **High idle timeout** = warm turns, lower latency. **Risk:** idle VMs consume quota; `maxVms` errors on bursts.
- **Low max lifetime** = predictable recycle, bounds memory leaks / stale state. **Risk:** active long sessions get killed mid-flow.
- **High max lifetime** = sticky sessions, big warm-state savings. **Risk:** drift, stale in-memory state, harder rollouts.
### Best practices
**Call `StopRuntimeSession` when the work is done.** If your agent finishes a task and doesn't expect more requests on that session, explicitly stop it. This releases the environment immediately instead of waiting for idle timeout.
```python
# After your invocation logic completes and you know the session is done:
client.stop_runtime_session(
agentRuntimeArn=runtime_arn,
runtimeSessionId=session_id,
)
```
**Reuse session IDs for related work.** A new session ID for every HTTP request means a new environment for every HTTP request. For multi-turn conversations, batch jobs, or user-facing interactions, use one session ID per conversation/batch/user-interaction and route all related requests to it.
**Tune `idleRuntimeSessionTimeout` to your workload.** The default 900 seconds is appropriate for interactive workloads where you expect quick follow-up requests. For request-reply workloads where sessions are short-lived, lower it.
Edit the runtime's entry in `agentcore/agentcore.json`:
```json
{
"runtimes": [
{
"name": "MyAgent",
"lifecycleConfiguration": {
"idleRuntimeSessionTimeout": 120,
"maxLifetime": 3600
}
}
]
}
```
Then `agentcore deploy` to apply. The CLI and CDK handle the underlying `UpdateAgentRuntime` call for you.
If you prefer the CLI, `agentcore add agent ... --idle-timeout 120 --max-lifetime 3600` writes the same fields into `agentcore.json`. The file is the source of truth — every field in it has IDE autocomplete via the `$schema` URL at the top of the file (`https://schema.agentcore.aws.dev/v1/agentcore.json`).
Lower timeout = faster VM reclamation = more headroom under `maxVms`. Too low = environments get reclaimed mid-conversation, causing the next turn to cold-start.
**Don't pass both `runtimeSessionId` and `mcpSessionId` together.** For MCP agents, use one. Passing both can bind two separate VMs to the same logical session.
### Diagnosing `maxVms` problems
If you hit `ServiceQuotaExceededException: maxVms limit exceeded`, don't request a quota increase first. CloudWatch's concurrent-sessions metric is not the same as live VM count — idle environments count against the quota until reclaimed.
Work through this order:
1. Add `StopRuntimeSession` after each logical request completes
2. Audit session-ID generation — are you creating a new ID per request that should reuse one?
3. Lower `idleRuntimeSessionTimeout` if your sessions are short-lived
4. Only then, if you've done all of the above and still hit the limit, request an increase
See [`references/limits.md`](references/limits.md) for the increase-request workflow (via the Service Quotas console) and the justification template.
---
## Long-running background tasks
If your agent fires off work that outlives the `/invocations` response — background processing, async jobs, long tool chains — a fire-and-forget pattern isn't enough. The environment can be reclaimed at `idleRuntimeSessionTimeout` even while your background task is still running, because the runtime considers the session idle once the invocation response is sent.
### Use the SDK's async task API to signal "still busy"
The bedrock-agentcore SDK provides task registration that keeps the environment alive while tracked work runs. In Python:
```python
from bedrock_agentcore.runtime import BedrockAgentCoreApp
app = BedrockAgentCoreApp()
@app.entrypoint
def invoke(payload, context):
# Register the task BEFORE starting it
task_id = app.add_async_task("background_work")
# Kick off the work (in a thread, asyncio, etc.)
start_background_work(task_id, payload)
# Return the invocation response — the task is still tracked
return {"status": "processing", "taskId": task_id}
def start_background_work(task_id, payload):
try:
# Long-running work here
do_the_work(payload)
finally:
# Mark the task complete when done — this releases the "busy" signal
app.complete_async_task(task_id)
if __name__ == "__main__":
app.run()
```
While at least one registered task is active, the runtime sees the environment as busy and doesn't reclaim it at `idleRuntimeSessionTimeout`. `maxLifetime` (default 8 hours) still applies as a hard ceiling.
Check the bedrock-agentcore SDK docs for your language for the equivalent API — the TypeScript SDK has an analogous pattern.
### Alternatives when async task API isn't an option
- **Increase `idleRuntimeSessionTimeout` to match your expected task duration.** If you know tasks run up to 10 minutes, set the timeout to 12 minutes. Keep it well under `maxLifetime`.
- **Keep the HTTP connection open** with a streaming response and emit periodic heartbeat events. Useful when you want the caller to wait for the result rather than polling. See the SSE keepalive pattern in [`agents-debug/SKILL.md`](../agents-debug/SKILL.md) ("Connection drops mid-stream" section).
- **Split long work across multiple invocations** on the same session. Each invocation resets the idle clock.
---
## Quotas and limits
If you're hitting throttling, `ServiceQuotaExceededException`, or any other quota-related error — or you're about to launch and want to make sure quotas won't block you — load [`references/limits.md`](references/limits.md).
That reference covers:
- Which quota each error maps to
- Mitigations to try before requesting an increase (critical — most "quota" errors are actually session-lifecycle issues)
- How to request an increase through the Service Quotas console (the edge case where a direct Support case is needed is rare)
- A copy-paste justification template with everything a reviewer needs to approve
---
## Production checklist summary
Generate a checklist specific to the project:
```
Production Readiness Checklist for <AgentName>
IAM
[ ] Execution role Bedrock access scoped to specific model ARNs
[ ] ECR access scoped to specific repository
[ ] Trust policy scoped to your account ID
Authentication
[ ] Inbound auth is AWS_IAM or CUSTOM_JWT (not NONE)
[ ] If CUSTOM_JWT: discovery URL, audience, and client IDs configured
Shell Access (if using InvokeAgentRuntimeCommand)
[ ] InvokeAgentRuntimeCommand permission granted only to identities that need it
[ ] Separate IAM policy from InvokeAgentRuntime policy
[ ] CloudTrail / EventBridge alert configured for InvokeAgentRuntimeCommand calls
[ ] If commands constructed from user input: shell injection validation implemented
Code quality
[ ] Error handling wraps all agent logic
[ ] Input validation on payload fields (type, length, format)
[ ] No secrets hardcoded in agent code
[ ] Credentials registered via agentcore add credential
Observability
[ ] X-Ray tracing enabled (auto-configured)
[ ] CloudWatch log retention policy set
[ ] Eval baseline established
Performance
[ ] Agent initialization time measured and optimized
[ ] Deployment package size under 200 MB (target under 100 MB)
[ ] Dependencies audited — no unused packages
[ ] Heavy initialization deferred to request time
[ ] Session reuse strategy chosen for multi-turn / batch workloads
[ ] `StopRuntimeSession` called after work completes where applicable
[ ] `idleRuntimeSessionTimeout` tuned to workload (default 900s)
[ ] For long-running background tasks: `add_async_task` / `complete_async_task` used
Resources
[ ] Memory strategies appropriate for use case (if using memory)
[ ] Gateway auth configured (if using gateway)
[ ] Policy engine attached (if restricting tool access)
Testing
[ ] Agent tested with production-representative inputs
[ ] Error cases tested (tool failures, model errors)
[ ] Memory cross-session tested (if using LTM)
```
## Output
- Checklist with specific findings for the project
- Specific commands to fix any issues found
- Recommended IAM policy for the detected model and resources
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# limits
Understand AgentCore Runtime quotas, diagnose which one you're hitting, and request an increase when you need one.
## When to use
- Your agent is being throttled or returning quota-related errors
- You're getting `ServiceQuotaExceededException: maxVms limit exceeded`
- You searched for a quota in the Service Quotas console and couldn't find it
- You're planning a launch and want to make sure quotas won't block you
- You're about to request a quota increase and want to get it right the first time
## Step 0: Verify CLI version
Run `agentcore --version`. This skill requires v0.9.0 or later. If older, run `agentcore update`.
---
## Which limit am I hitting?
Use the error you're seeing to find the right quota.
### Invocation rate
**Error shape:**
```
ThrottlingException: Rate exceeded
HTTP 429 Too Many Requests
```
On `InvokeAgentRuntime` or `InvokeAgentRuntimeWithWebSocketStream`.
**What it means:** Too many invocations per second for a single agent endpoint in your account. Default 25 TPS per agent, per account, per region. Adjustable.
**Before requesting an increase:**
- Add client-side retry with exponential backoff and jitter — throttling spikes are usually transient
- Check whether traffic is concentrated in bursts vs. spread — a burst of 100 requests at the same millisecond hits the TPS limit even if your average rate is well under it
- If the rate is a real long-term need, go request an increase (steps below)
---
### Concurrent VM / active session limit
**Error shape:**
```
ServiceQuotaExceededException: maxVms limit exceeded
```
**What it means:** Your account has too many concurrent microVMs active for AgentCore Runtime. In the AgentCore docs this quota is called **"Active session workloads per account."** Default 1,000 in us-east-1 and us-west-2, 500 in other regions. Adjustable.
**Critical — read before requesting an increase:**
CloudWatch's "concurrent active sessions" metric is not the same as live VM count. The `maxVms` quota counts all live microVMs in your account, including sessions that have completed their invocation but haven't yet been reclaimed. Your CloudWatch concurrency metric can show 50 while your actual live VM count is 500.
Real root cause for most customers hitting this is session lifecycle, not true concurrency. Check these first:
1. **Are you calling `StopRuntimeSession` after each invocation completes?** If not, the VM sticks around until `idleRuntimeSessionTimeout` expires (default 900 seconds / 15 minutes) before being reclaimed. At even modest request rates, VMs pile up.
2. **Are you reusing session IDs across related requests?** A unique session ID per request means a new environment per request. Reusing a session ID routes subsequent requests to the same environment, keeping total VM count low.
3. **Is your `idleRuntimeSessionTimeout` appropriate for your workload?** Short-lived requests with the default 900s timeout mean each VM ties up a slot for 15 minutes after its last request. Lower it by editing the runtime's `lifecycleConfiguration` in `agentcore/agentcore.json` and running `agentcore deploy`.
If you're hitting the limit after checking all three, request an increase.
See `agents-harden` SKILL.md (Session lifecycle management) for patterns and code snippets.
---
### New sessions created rate
**Error shape:**
```
HTTP 429 Too Many Requests
```
**What it means:** Rate of new session creation (per endpoint, per account). Default 100 TPM for container deployments; 25 TPS for direct code deployments. Adjustable.
**Before requesting an increase:**
- Reuse session IDs where possible — fewer new sessions = less pressure on this quota
- Spread traffic if you can — bursts of new-session requests hit the rate limit harder than steady-state traffic
---
### Memory operation rates
**Error shape:**
```
ThrottlingException
```
On `CreateEvent`, `RetrieveMemoryRecords`, `ListEvents`, and similar Memory APIs.
**What it means:** AgentCore Memory has per-operation rate limits. `CreateEvent` (default 10 TPS) is most commonly hit because agent code typically writes more than it reads. Most Memory API limits are adjustable.
**Before requesting an increase:**
- Add client-side retry with exponential backoff
- Confirm you're not accidentally writing the same content repeatedly (e.g., on every turn instead of once per fact)
- For long-term memory extraction, watch the `TokenCount` CloudWatch metric in the `Bedrock-AgentCore` namespace — the default is 150,000 tokens per minute per account (adjustable)
---
### Gateway target count or request rate
**Error shape:**
```
ValidationException: Too many targets for gateway
ThrottlingException
```
**What it means:** A single gateway has limits on number of targets (default 100 per gateway, adjustable), tools per target (default 1,000, adjustable), and invocation rate.
**Before requesting an increase:**
- Consolidate tools where possible — one Lambda with multiple tool definitions is more efficient than one Lambda per tool
- Split into multiple gateways if you have logically separate tool groups
---
### Code Interpreter / Browser session limits
**Error shape:**
```
ServiceQuotaExceededException
```
With an item name referencing Code Interpreter or Browser sessions.
**What it means:** Concurrent session limits for built-in tools.
**Before requesting an increase:**
- Ensure sessions are explicitly terminated when work completes
- Check for orphaned sessions from previous runs that might still be counted
---
## Where to find current quota values
1. **Canonical reference:** The AgentCore limits documentation page — https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/bedrock-agentcore-limits.html — lists every quota with its default value and whether it's adjustable. The **Adjustable** column is the source of truth. If the `awsknowledge` MCP server is available, use the `aws___search_documentation` tool to look up current quota values — it can fetch the latest docs directly instead of relying on potentially stale links.
2. **Service Quotas console:** https://console.aws.amazon.com/servicequotas/home → **AWS services** → search for "Amazon Bedrock AgentCore". Shows your current applied quota values and lets you request increases directly. Virtually every adjustable AgentCore quota is available here today — this should be your default path.
3. **`aws service-quotas list-services`** / **`list-service-quotas`:** Programmatic view — run `aws service-quotas list-services` and grep for "agentcore" or "bedrock" to find the current service code, then list quotas with `list-service-quotas --service-code <code>`.
---
## How to request a quota increase
Use the Service Quotas console. Virtually every adjustable AgentCore quota is available there; a direct AWS Support case is only needed in rare cases where a specific quota isn't surfaced in the console for your region.
### Path 1 — Service Quotas console (use this)
1. Open https://console.aws.amazon.com/servicequotas/home in the region where you need the increase. Quotas are region-specific — select the correct region in the top-right before proceeding.
2. Navigation pane → **AWS services** → search for **Amazon Bedrock AgentCore** and select it.
3. Find the quota in the list. The **Adjustable** column tells you if an increase can be requested.
4. Select the quota → **Request increase at account-level** (or **resource-level** if available for that quota).
5. Enter the new value (must be greater than the current applied value) → **Request**.
6. Track status in the **Request history** tab or the **Dashboard** in the navigation pane. When the status moves from **Pending** to **Quota requested**, a Support case number is assigned — you can open that case from the console to see progress.
**What happens next:**
- Smaller increases are often auto-approved within minutes to a few hours.
- Larger increases escalate to AWS Support and take longer (hours to days, depending on the magnitude and the quota).
- Support can approve, partially approve, or deny the request. If denied, the console message explains why; you can submit a new request with more justification.
**CLI equivalent** — for scripted workflows:
```bash
# Discover the Service Quotas service code for AgentCore (it follows the
# service's API prefix — run this once to confirm the exact code)
aws service-quotas list-services \
--region <REGION> \
--query "Services[?contains(ServiceName, 'AgentCore') || contains(ServiceCode, 'agentcore')]"
# List quotas for that service code
aws service-quotas list-service-quotas \
--service-code <SERVICE_CODE> \
--region <REGION>
# Submit the increase request
aws service-quotas request-service-quota-increase \
--service-code <SERVICE_CODE> \
--quota-code <QUOTA_CODE> \
--desired-value <NEW_VALUE> \
--region <REGION>
# Check status
aws service-quotas list-requested-service-quota-change-history-by-quota \
--service-code <SERVICE_CODE> \
--quota-code <QUOTA_CODE> \
--region <REGION>
```
The account submitting the CLI request needs `ServiceQuotasFullAccess` (or equivalent) and `iam:CreateServiceLinkedRole` so Service Quotas can create the Support case on your behalf.
### Path 2 — AWS Support Center case (edge case)
Only needed when a specific quota you need isn't listed in the Service Quotas console for your region, or the console returns "This quota can't be increased from this console." This is uncommon — check the console first.
1. Open https://console.aws.amazon.com/support. You can also reach it from the **?** help icon in the AWS console → **Support Center**.
2. **Create case**.
3. **Case type** → **Service quotas**.
4. **Service** → **Service Limit increase**.
5. **Category** → select the AgentCore service (e.g., "Amazon Bedrock AgentCore Runtime"). If the specific AgentCore category isn't listed, use the closest match and put the exact quota name in the description.
6. **Region** → select the AWS Region you need the increase in. You can choose **Add another limit** to request the same increase in multiple regions in one case.
7. **Description** — include everything the reviewer needs (see fields below).
8. Pick a **Contact method** (Web, Chat, Phone) → **Submit**.
### Required information, regardless of path
Whether you're using the Service Quotas console justification field or the Support case description, give the reviewer enough to say yes:
- **AWS Account ID** (the account that needs the increase)
- **Region(s)** — limits are per-region; list every region you need if this is a Support case covering multiple
- **Quota name** — match the exact name from the AgentCore limits documentation (e.g., "Active session workloads per account," "InvokeAgentRuntime API rate, per agent, per account")
- **Current value → requested value** — be specific (e.g., "25 → 100")
- **Agent Runtime ID(s)** or ARN(s) — what this request is for
- **Use case** — 1–3 sentences on what the agent does and the traffic pattern (sustained vs. bursty matters for some quotas)
- **Expected peak** — a real number (peak TPS, concurrent sessions, etc.), not a range
- **Business impact** — what's blocked at the current limit (e.g., "blocks our GA launch on X date")
- **Timeline / need-by date**
### Copy-paste justification template
Drop this into the Service Quotas justification field or the Support case description:
```
Account ID: <12-digit account>
Region(s): <comma-separated>
Quota name: <exact name from AgentCore limits docs>
Current value: <N>
Requested value: <N>
Agent Runtime ID(s): <comma-separated agentRuntimeId or ARN values>
Use case:
<1–3 sentences describing what the agent does and the traffic pattern>
Expected peak:
<specific number — peak TPS, concurrent sessions, etc.>
Business impact if not raised:
<what happens to your workload at the current limit>
Need-by date: <date>
```
### What speeds up approval
- Specific numbers, not "as high as possible"
- Traffic pattern explained — sustained vs. bursty
- Pre-launch load-testing numbers if you have them
- Production launch date called out explicitly
### What slows approval down
- Requesting an increase before trying the mitigations above (`StopRuntimeSession`, session reuse, batching, retries)
- Requesting every quota to some large round number "just in case"
- Missing the exact quota name — reviewers need to know which quota in which service
- Requesting increases in every region when only one or two are needed
---
## Before you request: quick triage
Work through this list first. Most "I'm hitting a limit" issues get resolved at one of these steps without needing an actual increase.
- [ ] Is the error really a quota error? (Check the exception class and code — not every `Exception` is throttling)
- [ ] Client-side retry with exponential backoff — present for transient throttling?
- [ ] For `maxVms`: is `StopRuntimeSession` being called after each invocation?
- [ ] For `maxVms`: are session IDs reused across related requests?
- [ ] For `maxVms`: is `idleRuntimeSessionTimeout` set appropriately for your workload?
- [ ] For memory writes: are you batching where possible, and not writing duplicates?
- [ ] Is traffic bursty? Can you smooth it out at the caller?
- [ ] Is the current quota actually the problem, or is a downstream dependency the real bottleneck?
If you've checked all of the above and still need the increase, submit it through the Service Quotas console.
## Output
- Identification of the specific quota being hit based on the error
- Mitigations to try before requesting an increase
- Path to submit: Service Quotas console (or, rarely, a Support case) — with a filled-in justification ready to paste
plugins/aws-agents/skills/agents-optimize/SKILL.md
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---
name: agents-optimize
description: >
Use when measuring or improving agent quality and performance — set up
evaluators, online monitoring, CI/CD quality gates, observability, or
cost optimization. Triggers on: "evaluate my agent", "add evaluator",
"measure quality", "quality gate", "run evals", "agent too slow",
"why is it slow", "reduce latency", "set up observability", "CloudWatch
dashboard", "how much does my agent cost", "cost optimization", "logs
not showing up", "logs missing", "spans not found", "eval failing",
"eval error", "dev traces", "local traces", "agentcore dev traces",
"traces to CloudWatch".
Not for debugging errors or crashes — use agents-debug. Slow but
correct routes here; broken routes to debug.
allowed-tools: Read Grep Glob Bash
metadata:
type: skill
version: "1.0.0"
author: aws-agentcore
requires-cli: ">=0.9.0"
---
# optimize
Measure and improve your AgentCore agent's quality through evaluation, monitoring, and observability.
## When to use
- You want to know if your agent is giving good answers
- You want to set up continuous quality monitoring in production
- You want to add a quality gate to your CI/CD pipeline
- You want to understand agent behavior through logs, metrics, and traces
- You want to set up CloudWatch dashboards or X-Ray tracing
Do NOT use for:
- Debugging a specific broken agent (wrong answers, errors) → use `agents-debug`
- Production security hardening (IAM, auth) → use `agents-harden`
## Input
`$ARGUMENTS` can be:
- An eval goal: "add a quality gate", "set up monitoring"
- An observability goal: "set up CloudWatch dashboard", "understand my traces"
- A specific evaluator: "llm-as-a-judge", "code-based"
- Empty — the skill will guide based on project context
## Process
### Step 0: Verify CLI version
Run `agentcore --version`. This skill requires v0.9.0 or later.
### Step 1: Read project context
Read `agentcore/agentcore.json` to understand existing evaluators, online eval configs, and agent setup.
If `agentcore/agentcore.json` is not found:
> "This skill requires an AgentCore project. Use `agents-get-started` to create one."
### Step 2: Determine the workflow
| Developer intent | Action |
|---|---|
| Measure quality, add evaluator, run eval, CI/CD gate, online monitoring | Load [`references/evals.md`](references/evals.md) and follow its workflow |
| Set up observability, CloudWatch, X-Ray, logs, metrics, dashboards | Load [`references/observability.md`](references/observability.md) and follow its workflow |
| Understand or reduce AgentCore costs | Load [`references/cost.md`](references/cost.md) |
| Both — "I want to understand and improve my agent" | Start with observability setup, then add evals |
### Step 3: Follow the loaded reference
The reference file contains the full procedure. Follow it step by step.
### Cross-references
- After setting up evals, suggest `agents-harden` for production readiness
- If eval results reveal agent issues, suggest `agents-debug` for root cause analysis
- If the developer needs to add capabilities first, suggest `agents-build`
## Output
Depends on the workflow — see the loaded reference for specific outputs.
## Quality criteria
- Evaluator configuration uses only valid CLI flags
- Online eval sampling rate is appropriate (not 100% in production without discussion)
- CI/CD quality gate has a clear pass/fail threshold
- Observability setup includes both tracing and logging
- The developer understands the eval data delay: **~10 seconds put-to-get, end-to-end** — one ingestion step covers both trace reads and eval queries; there is no separate indexing wait
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# Cost Optimization
Understand what drives AgentCore costs and how to control them. Pricing values are volatile — always verify against the [AgentCore pricing page](https://aws.amazon.com/bedrock/agentcore/pricing/).
## Cost components
AgentCore charges for several things independently:
| Component | What you pay for | Published rate (verify for current) | Biggest cost drivers |
|---|---|---|---|
| **Runtime compute** | vCPU-hours + GB-hours while session is active | $0.0895/vCPU-hr, $0.00945/GB-hr | Session length, idle timeout, cold starts |
| **Memory events** | Creating events (writes) | $0.25 per 1,000 new events | Session volume, number of strategies |
| **Memory storage** | Long-term memory records stored | $0.75 per 1,000 records/month (built-in); $0.25 (override/self-managed) | Number of strategies, expiry duration |
| **Memory retrieval** | Retrieving memory records | $0.50 per 1,000 retrievals | Retrieval frequency, top_k value |
| **Gateway tool calls** | Per tool invocation routed through gateway | $0.005 per 1,000 (ListTools/InvokeTool/Ping); $0.025 per 1,000 (Search) | Tool call volume |
| **Evaluator model calls** | Bedrock model usage for LLM-as-judge evaluators | Built-in: $0.0024/10K input tokens, $0.012/10K output tokens; Custom: $1.50/10K evals | Online eval sampling rate × session volume |
| **Bedrock model usage** | Input/output tokens for every model call | Varies by model — check Bedrock pricing | Model choice (Sonnet vs Haiku), conversation length |
| **Policy authorization** | Per authorization request + input tokens | $0.000025/request, $0.13/10K input tokens | Tool call volume with policy engine attached |
| **Identity** | Token/API key requests for non-AWS resources | $0.010 per 1,000 requests | Credential fetch frequency |
| **CloudWatch logs/traces** | Ingestion and storage | Standard CloudWatch pricing | Log verbosity, retention policy |
| **ECR storage** (Container builds only) | Image storage | Standard ECR pricing | Image size, build frequency |
Rates above are published as of the time of writing. Always verify against the [AgentCore pricing page](https://aws.amazon.com/bedrock/agentcore/pricing/) — pricing changes between releases.
## First-day cost questions
### "How much will my agent cost per invocation?"
There's no single number — it depends on:
- Which model (Haiku is ~10x cheaper than Sonnet per token)
- How long the session stays active (Runtime bills by vCPU-hour and GB-hour, not per request — idle sessions cost money)
- Whether it uses tools (gateway calls are $0.005 per 1,000 + any Lambda/API costs)
- Whether memory extraction is running (async, billed separately at $0.25 per 1,000 events)
- How long conversations run (more tokens = more model cost, and longer active sessions = more compute cost)
A simple Haiku-based agent with no memory and no tools costs very little per request — Runtime compute is billed by vCPU-hour ($0.0895) and GB-hour ($0.00945), so a sub-second request on a small environment costs fractions of a cent. A Sonnet agent with semantic memory, 5 gateway tools, and online evals at 10% sampling costs significantly more per request — the model token costs alone can be 10–30x higher, plus memory extraction ($0.25 per 1,000 events), gateway tool calls ($0.005 per 1,000 invocations), and eval model usage. These are published rates as of the time of writing — verify against the [AgentCore pricing page](https://aws.amazon.com/bedrock/agentcore/pricing/) for current numbers. If the `awsknowledge` MCP server is available, use the `aws___search_documentation` tool to look up current AgentCore pricing.
### "How much will this demo/prototype cost me?"
Use the `--defaults` flags (Strands, Bedrock, no memory) during development. Stay under the free tier where possible. The biggest surprises come from:
- **Idle sessions burning compute** — Runtime bills by vCPU-hour while the session is active, including idle time before `idleRuntimeSessionTimeout` reclaims it. Default timeout is 15 minutes. Call `StopRuntimeSession` when done, or lower the timeout. See `agents-harden` Session lifecycle management.
- Leaving an online eval config running at 100% sampling
- Forgetting to set CloudWatch log retention (defaults to indefinite)
- Keeping a test memory resource with an expensive strategy (SEMANTIC or EPISODIC)
## Cost reduction levers
### Model selection
AgentCore supports four model providers — pick the right one for the task, not just the default:
| Model tier | Examples | Good for |
|---|---|---|
| **Cheapest / simplest** | `amazon.nova-micro-v1:0`, `claude-3-5-haiku-*`, Gemini Flash, GPT-5-nano | Classification, extraction, simple routing, short responses |
| **Mid-tier** | `amazon.nova-lite-v1:0`, Gemini 2.5 Flash | Most general-purpose agents with light reasoning |
| **Premium / reasoning** | `anthropic.claude-sonnet-4-5-*`, GPT-5, Gemini 2.5 Pro | Complex reasoning, code generation, multi-step planning |
Rules of thumb:
- Haiku or Nova Micro for simple extractive tasks (10–30x cheaper than Sonnet per token)
- Reserve Sonnet/Opus/GPT-5 for reasoning-heavy workflows
- **Use different models for agent vs evaluator** — a Haiku-based evaluator grading a Sonnet agent is a common cost-effective pattern
- For cost-sensitive customer support or classification agents, start with Nova Lite or Gemini Flash and only upgrade if quality is insufficient
### Memory
- Only enable strategies you actually use — each LTM strategy runs extraction on every session
- `SEMANTIC` is the most expensive strategy. If you only need session summaries, use `SUMMARIZATION` alone.
- Tune `relevance_score` up so fewer memory records retrieve per query
- Set `--expiry` to the shortest duration that serves your use case (default is 30 days)
### Online evals
- Start at 1–5% sampling in production, not 100%
- Use `agentcore pause online-eval <name>` when debugging or iterating — resume when you're ready to measure
- Pick the smallest evaluator set that gives signal
### Logs and traces
- Set retention policies on log groups:
```bash
aws logs put-retention-policy \
--log-group-name /aws/bedrock-agentcore/runtimes/<AGENT_ID>-DEFAULT \
--retention-in-days 30
```
- Don't log entire payloads — log structured events with just what you need
- X-Ray sampling is configured automatically; no dial to turn there
### Gateway
- Tool calls are per-invocation, not per byte. Volume matters, not payload size.
- If a tool is called on every invocation for the same static data, consider baking that data into the system prompt instead
### Container builds
- If you don't need Container, use CodeZip — no ECR storage charge
- If you need Container, keep the image small (see `agents-harden` Initialization time section)
## Cross-references
- For model selection decisions, see [`references/evals.md`](evals.md) Path A (evaluator model choice applies the same way)
- For memory strategy decisions, see [`agents-build/references/memory.md`](../../agents-build/references/memory.md)
- For log retention (a harden concern), see `agents-harden` Observability section
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# evals
Set up evaluation for your AgentCore agent — from a single quality check to a full production monitoring pipeline.
## When to use
- You want to know if your agent is giving good answers
- You want continuous monitoring of live traffic
- You want a CI/CD quality gate that fails the build if quality drops
- You want to interpret eval scores you've already run
- You want to compare agent versions
Not for debugging a specific wrong answer — use the `agents-debug` skill for that.
## Input
`$ARGUMENTS` is optional. If provided, it scopes the skill:
```
/evals # interactive — asks what you want to set up
/evals quick # run a quick eval on the most recent session
/evals monitor # set up continuous online monitoring
/evals ci # generate a CI/CD quality gate script
```
## Process
### Step 1: Understand the goal
Ask (or infer from `$ARGUMENTS`):
> "What are you trying to do?
>
> 1. Run a one-time eval on recent sessions to see how my agent is doing
> 2. Set up continuous monitoring of live traffic
> 3. Add a quality gate to my CI/CD pipeline
> 4. Create a custom evaluator for my specific use case
> 5. Understand eval scores I've already run"
### Step 2: Check prerequisites — and know what actually needs a deploy
Read `agentcore/agentcore.json` if it exists. Check:
- Is there a deployed runtime? (not always required — see below)
- Are there existing evaluators configured?
**If no project context:** Ask which runtime they want to evaluate. They can use `--runtime-arn` for standalone mode.
**What actually requires a deployed runtime — and what doesn't:**
| Action | Deploy required? |
|---|---|
| Define an evaluator (`agentcore add evaluator`) — LLM-as-judge or custom code | **No.** Writes to `agentcore.json` only. |
| Author & iterate on LLM-as-judge instructions / rating scale | **No.** Text edits; try them against saved traces or manual fixtures. |
| Unit-test a custom code evaluator (the `@custom_code_based_evaluator` function) | **No.** Import the function and call it with an `EvaluatorInput` fixture — see Path D below. |
| Write / dry-run the CI/CD quality-gate script | **No** for the script itself; deploy only needed if you want the eval call inside to hit production traffic. |
| **`agentcore run eval` against local-dev traces** | **No.** `agentcore dev` emits OTEL spans to CloudWatch by default — see "Evaluating a local dev run" below. |
| **`Evaluate` API with hand-constructed spans** (boto3) | **No.** Submit `SessionSpans` directly, no runtime needed at all. |
| `agentcore run eval` against production-runtime traces | Yes — operates on traces the deployed runtime produced. |
| `OnDemandEvaluationDatasetRunner` (SDK dataset runner) | Yes — the runner invokes an AgentCore Runtime agent in its pipeline. |
| Online monitoring (`agentcore add online-eval`) | Yes — continuous ingestion from the deployed runtime. |
**The local-dev eval loop is a real option.** `agentcore dev` auto-instruments OTEL and ships spans to CloudWatch the same way deployed runtimes do — this isn't a deployed-only feature. You can iterate on evaluators against your own local invocations, with a short round-trip and no AWS CDK churn.
**For the dataset runner and online monitoring, deploy is genuinely required.** Those paths invoke or ingest from a live AgentCore Runtime agent — there's no local equivalent.
**Don't tell the developer to fully deploy before they can make progress on evals.** Definition, authoring, and unit-testing are local. Running `agentcore run eval` is local too, given the prerequisites below.
#### Evaluating a local dev run
Requirements:
1. **AWS credentials available locally** (e.g., `aws sso login` for the account you want spans to land in).
2. **CloudWatch Transaction Search enabled** on the account. One-time setup — either in the CloudWatch console (Settings → X-Ray traces → Transaction Search) or via:
```bash
aws xray update-trace-segment-destination --destination CloudWatchLogs
```
3. **OTEL is already on.** `agentcore dev` auto-instruments with the AWS OpenTelemetry distro by default. If you've passed `--no-traces`, remove it.
4. **Wait ~10 seconds** after invoking — CloudWatch put-to-get latency is ~10s end-to-end (covers both trace reads and eval queries; it's one ingestion step, not two).
The loop:
```bash
# Terminal 1 — start local dev with OTEL on (default)
agentcore dev
# Terminal 2 — invoke a few times, noting the session ID
agentcore dev --invoke "What's the weather in Seattle?" --stream
# or: agentcore invoke "..." once dev is running
# Note the session ID from the response / logs.
# Wait ~10 seconds for CloudWatch ingestion, then evaluate
agentcore run eval \
--runtime MyAgent \
--session-id <session-id-from-local-run> \
--evaluator "Builtin.Helpfulness"
```
The evaluator runs in AWS (it's a managed evaluation service — the model call happens there, not locally), but the **agent run being evaluated happened on your laptop**. This is the fastest iteration loop for tuning an evaluator's instructions or rating scale.
#### Hand-constructed spans (no runtime at all)
For the tightest unit-test loop — or when you want to evaluate a saved snapshot without running the agent — call the `Evaluate` API directly with spans you construct:
```python
import boto3
client = boto3.client("bedrock-agentcore", region_name="<REGION>")
response = client.evaluate(
evaluatorId="Builtin.Helpfulness",
sessionSpans=[
# Minimum shape matches the OTEL span schema for AgentCore traces.
# Easiest way to produce a fixture: download one real span via
# `agentcore traces get <traceId> --output trace.json`, then mutate it.
{"name": "agent.invoke", "attributes": {"gen_ai.prompt": "What's the weather?", "gen_ai.response.content": "Sunny, 72°F."}},
],
)
print(response["evaluatorResults"])
```
The full span schema and field list is in the [`Understanding input spans`](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/understanding-input-spans.html) doc. This path is overkill for a one-time eval but invaluable when a custom code evaluator needs regression-test fixtures that don't depend on CloudWatch at all.
---
### Path A: Run a one-time eval
#### Step A1: Choose an evaluator
Start with built-in evaluators — they require no setup and cover the most common quality dimensions:
| Evaluator | Level | What it measures |
|---|---|---|
| `Builtin.Helpfulness` | TRACE | How useful was each response? |
| `Builtin.Correctness` | TRACE | Is the information factually accurate? (supports ground truth) |
| `Builtin.Faithfulness` | TRACE | Does the response stay grounded in provided context? |
| `Builtin.ResponseRelevance` | TRACE | Does the response address what was asked? |
| `Builtin.InstructionFollowing` | TRACE | Does the agent follow system-prompt instructions? |
| `Builtin.Conciseness` | TRACE | Is the response appropriately concise? |
| `Builtin.Coherence` | TRACE | Is the response logically coherent? |
| `Builtin.Refusal` | TRACE | Did the agent appropriately refuse out-of-scope requests? |
| `Builtin.ToolSelectionAccuracy` | TOOL_CALL | Did the agent pick the right tool for the task? |
| `Builtin.GoalSuccessRate` | SESSION | Did the agent complete the user's goal? (supports ground truth) |
**Built-in evaluator names may change.** Check the AgentCore docs for the current list — new evaluators are added across releases.
**Recommendation:** Start with `Builtin.Helpfulness` for a general quality check. Add `Builtin.GoalSuccessRate` for task completion. Use `Builtin.ToolSelectionAccuracy` when your agent uses tools. Use `Builtin.Correctness` or `Builtin.Faithfulness` when you have ground truth to compare against.
#### Step A2: Run the eval
```bash
# Run against the most recent session (auto-detected from project)
agentcore run eval --evaluator "Builtin.Helpfulness"
# Run against multiple evaluators
agentcore run eval \
--evaluator "Builtin.Helpfulness" \
--evaluator "Builtin.GoalSuccessRate"
# Run against a specific runtime (standalone mode, no project needed)
agentcore run eval \
--runtime-arn arn:aws:bedrock-agentcore:us-east-1:123456789012:runtime/myagent-abc123 \
--evaluator "Builtin.Helpfulness"
# Extend the lookback window (default is 7 days)
agentcore run eval --evaluator "Builtin.Helpfulness" --days 14
```
#### Step A3: Interpret the results
Scores are normalized to 0–1:
- **0.8–1.0** — Good. Agent is performing well on this dimension.
- **0.6–0.8** — Acceptable. Worth monitoring but not urgent.
- **Below 0.6** — Investigate. Check recent traces for patterns.
Results are saved to `agentcore/.cli/eval-runs/`. View history:
```bash
agentcore evals history
agentcore evals history --limit 10
```
---
### Path B: Set up continuous monitoring
#### Step B1: Create an evaluator (if needed)
For continuous monitoring, built-in evaluators are usually sufficient. If you need a custom evaluator for your specific use case, see Path D first.
#### Step B2: Add an online eval config
```bash
agentcore add online-eval \
--name my_quality_monitor \
--runtime MyAgent \
--evaluator "Builtin.Helpfulness" \
--evaluator "Builtin.GoalSuccessRate" \
--sampling-rate 5
```
**Important naming rule:** Config names must use underscores only — no hyphens. `my-monitor` will fail with a validation error; `my_monitor` works.
**Sampling rate guidance:**
- `1–5` — Good for production (1–5% of requests evaluated)
- `10–20` — Good for staging or low-traffic agents
- `100` — Evaluate every request (dev/testing only, adds latency and cost)
#### Step B3: Deploy to activate
```bash
agentcore deploy -y
```
The online eval config starts in `CREATING` state and becomes `ACTIVE` within a few seconds after deploy.
#### Step B4: View results
Results stream to CloudWatch Logs:
```
/aws/bedrock-agentcore/evaluations/results/<config-id>
```
View in the AWS console: CloudWatch → GenAI Observability → Bedrock AgentCore → Evaluations tab.
Stream eval logs from the CLI:
```bash
agentcore logs evals --runtime MyAgent --since 1h
agentcore logs evals --follow
```
**Pause/resume without redeploying:**
```bash
agentcore pause online-eval my_quality_monitor
agentcore resume online-eval my_quality_monitor
```
---
### Path C: CI/CD quality gate
Generate a script that runs evals and fails the build if quality drops below a threshold.
```bash
#!/bin/bash
# quality-gate.sh — run after deploy in CI/CD
set -e
RUNTIME="MyAgent"
EVALUATOR="Builtin.Helpfulness"
THRESHOLD="0.7"
echo "Running quality gate eval..."
result=$(agentcore run eval \
--runtime "$RUNTIME" \
--evaluator "$EVALUATOR" \
--days 1 \
--json)
score=$(echo "$result" | jq -r '.run.results[0].aggregateScore // empty')
if [ -z "$score" ]; then
echo "⚠️ No eval data found. Has the agent been invoked recently?"
echo " Invoke the agent at least once, wait ~10 seconds, then re-run."
exit 1
fi
echo "Quality score: $score (threshold: $THRESHOLD)"
if awk -v s="$score" -v t="$THRESHOLD" 'BEGIN{exit !(s<t)}'; then
echo "❌ Quality gate FAILED: score $score < $THRESHOLD"
exit 1
fi
echo "✅ Quality gate PASSED"
```
**Note:** CloudWatch put-to-get latency is **~10 seconds end-to-end** — the same ingestion step unlocks both trace reads and eval queries; there's no extra indexing wait. In CI/CD, invoke the agent as part of your integration tests, then add a short `sleep 10` (or `sleep 15` for headroom) before running the quality gate. The old `sleep 300` pattern from earlier skills/docs is 30× longer than needed now.
For standalone mode (no project context in CI):
```bash
agentcore run eval \
--runtime-arn arn:aws:bedrock-agentcore:us-east-1:123456789012:runtime/myagent-abc123 \
--evaluator "Builtin.Helpfulness" \
--days 1 \
--json
```
---
### Path D: Custom evaluator
Use a custom evaluator when built-ins don't cover your specific quality criteria — domain accuracy, tone, format compliance, safety for your use case.
#### Step D1: Choose the evaluator type
- **LLM-as-a-judge** — An LLM scores each response against your instructions. Most flexible.
- **Code-based** — A Lambda function scores responses programmatically. Use for deterministic checks (format validation, required fields, etc.).
#### Step D2: Create an LLM-as-a-judge evaluator
Choose the right level first:
- `SESSION` — evaluate the whole conversation (goal completion, overall quality)
- `TRACE` — evaluate each individual response (helpfulness, accuracy, tone)
- `TOOL_CALL` — evaluate tool selection and parameters
Check the AgentCore docs for additional evaluator levels — new levels may be added across releases.
```bash
agentcore add evaluator \
--name ResponseQuality \
--level TRACE \
--model "global.anthropic.claude-sonnet-4-5-20250929-v1:0" \
--instructions "Evaluate the assistant's response for helpfulness and accuracy. Context: {context}. Response to evaluate: {assistant_turn}" \
--rating-scale 1-5-quality
```
Note: The evaluator model ID above is an example — check the AgentCore docs for current supported evaluator model IDs and cross-region inference profiles.
**Placeholder rules by level:**
| Level | Required placeholder | Optional |
|---|---|---|
| `SESSION` | `{context}` | `{available_tools}` |
| `TRACE` | `{context}` | `{assistant_turn}`, `{available_tools}` |
| `TOOL_CALL` | `{context}` | `{tool_turn}`, `{available_tools}` |
**Rating scale presets** (pass as literal strings to `--rating-scale`):
- `1-5-quality` — Poor/Fair/Good/Very Good/Excellent (default)
- `1-3-simple` — Low/Medium/High
- `pass-fail` — Pass/Fail
- `good-neutral-bad` — Good/Neutral/Bad
**Custom rating scale:**
```bash
--rating-scale "0:Incorrect:Factually wrong or misleading, 0.5:Partial:Partially correct, 1:Correct:Accurate and complete"
```
#### Step D3: Create a code-based evaluator (for deterministic checks)
```bash
agentcore add evaluator \
--name FormatChecker \
--level TRACE \
--type code-based \
--lambda-arn arn:aws:lambda:<REGION>:<YOUR_ACCOUNT_ID>:function:check-response-format \
--timeout 30
```
Your Lambda receives the trace context and must return a score between 0 and 1. Use the SDK's `@custom_code_based_evaluator()` decorator to handle the Lambda event parsing and response contract for you:
```python
# lambda_function.py
from bedrock_agentcore.evaluation.custom_code_based_evaluators import (
custom_code_based_evaluator,
EvaluatorInput,
EvaluatorOutput,
)
@custom_code_based_evaluator()
def handler(evaluator_input: EvaluatorInput, context) -> EvaluatorOutput:
# evaluator_input.session_spans contains the trace data
# Implement your deterministic check (regex, schema validation, rule engine, etc.)
response_text = _extract_response(evaluator_input.session_spans)
if _matches_required_format(response_text):
return EvaluatorOutput(value=1.0, label="Pass")
return EvaluatorOutput(value=0.0, label="Fail", reasoning="Response did not match expected format")
```
The decorator handles parsing the raw Lambda event, extracting trace/span IDs, and serializing the response — write your check against typed `EvaluatorInput` and return a typed `EvaluatorOutput`.
#### Step D3.5: Unit-test the evaluator locally before deploying
The `@custom_code_based_evaluator` function is a plain Python function. Import it directly and exercise the logic with fixtures — no deploy, no AWS credentials needed:
```python
# test_evaluator.py
from bedrock_agentcore.evaluation.custom_code_based_evaluators import EvaluatorInput
from lambda_function import handler # the decorated function above
def _fake_input(response_text: str) -> EvaluatorInput:
# Construct the minimum EvaluatorInput shape the handler reads.
# Use a saved real trace for higher-fidelity fixtures — download one via
# `agentcore traces get <traceId> --output trace.json` after a single deploy+invoke.
return EvaluatorInput(
session_spans=[{"attributes": {"gen_ai.response.content": response_text}}],
# ...fill remaining fields the SDK expects for your level
)
def test_matches_format():
out = handler(_fake_input('{"status": "ok"}'), context=None)
assert out.value == 1.0
def test_rejects_free_text():
out = handler(_fake_input("Here's your answer: ok"), context=None)
assert out.value == 0.0
assert "did not match" in (out.reasoning or "").lower()
```
Run with `pytest test_evaluator.py`. Iterate the logic until the fixtures pass. Only **then** deploy — the deploy step is about wiring the Lambda into AgentCore, not about debugging the check.
For **LLM-as-judge** evaluators, there's no equivalent unit-test surface (the model call happens in the eval service), but you can iterate on the instructions against saved traces by dry-running the prompt in Bedrock console or in a one-off script before `agentcore add evaluator`.
#### Step D4: Deploy and run against a real trace
```bash
agentcore deploy -y
agentcore run eval --evaluator ResponseQuality --days 7
```
**Evaluator name rules:** alphanumeric + underscores only, max 48 chars. No hyphens.
---
## Troubleshooting
### "No spans found for session"
- Wait ~10 seconds after invoking the agent — CloudWatch put-to-get is ~10s end-to-end (there's no separate eval-indexing step beyond that)
- Check that observability was enabled when the agent was deployed
- Extend the lookback: `--days 14` or `--days 30`
### "No agent specified" or agent ID not found
- Run from inside your AgentCore project directory, or
- Use `--runtime-arn` to specify the agent explicitly
### Online eval config stuck in CREATING
- Run `agentcore status --type online-eval` to check status
- Usually resolves within 30 seconds of deploy
### `remove evaluator` fails
- An online eval config is referencing this evaluator
- Remove the online eval config first: `agentcore remove online-eval --name <name>`
- Then remove the evaluator
## Cross-region inference (data residency)
Both built-in and LLM-as-judge evaluators use **cross-region inference** by default. The data being evaluated stays in your primary region, but the inference call that runs the judge model may execute in another AWS region within the same geography (e.g., `us-east-1` → `us-east-2`/`us-west-2`; EU stays in EU).
There's no extra cost, and logs don't include the inference region. But if data-residency rules require pinning inference to a single region:
- **Built-in evaluators:** they're managed by AgentCore and use cross-region inference as-configured. If single-region inference is required, use a custom evaluator instead.
- **Custom LLM-as-judge evaluators:** pin the model by choosing a region-specific model ID for `--model` instead of a cross-region inference profile ID. Check the docs for current single-region model IDs in your region.
- **Code-based evaluators:** not affected. The Lambda runs wherever you deployed it.
See [cross-region inference](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/cross-region-inference.html) for the current geography/region mapping rather than baking it in here — it expands across releases.
## When to use the dataset runner vs. `agentcore run eval`
Two different tools for two different workflows — developers confuse them.
| You want to... | Tool | Where it runs |
|---|---|---|
| Evaluate one session or trace from a recent run | `agentcore run eval --session-id <id>` | CLI, against CloudWatch-ingested spans |
| Evaluate *everything* from the last N days and track score drift | `agentcore run eval --days 7` | CLI, against CloudWatch |
| Run a curated benchmark / regression suite (20–500 scenarios, CI/CD) | `OnDemandEvaluationDatasetRunner` (SDK) | Your Python process, orchestrates invoke + wait + evaluate |
| Check that every production invocation meets quality thresholds | `agentcore add online-eval` | Platform, continuous sampling |
**Use `agentcore run eval`** when you're iterating on an evaluator, investigating a specific regression, or running a quality gate against recent traffic. It's fast, cheap, and doesn't invoke the agent itself — it only scores existing traces.
**Use `OnDemandEvaluationDatasetRunner`** when you have a dataset of scenarios with expected responses / trajectories / assertions and you want to run them as a batch. The runner **invokes the agent** for each scenario, waits for telemetry ingestion (default 180 seconds, paid once per run not per scenario), then evaluates. This requires a deployed runtime. Typical use: regression pack in CI before promoting a new version.
**Use online eval** for continuous production monitoring at a sampling rate — not the same as a benchmark.
## Output
- CLI commands to run evals or set up monitoring
- Quality gate script (for CI/CD path)
- Evaluator config (for custom evaluator path)
- Interpretation of scores if reviewing existing results
plugins/aws-agents/skills/agents-optimize/references/observability.md
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# Observability Setup
Set up logging, tracing, and monitoring for your AgentCore agent.
## What's auto-enabled
AgentCore automatically enables:
- **X-Ray tracing** — every invocation generates a trace
- **CloudWatch logging** — agent logs ship to CloudWatch
These are on by default whether you're running **deployed** (`agentcore deploy` + invoke) or **locally** (`agentcore dev`). The dev server auto-instruments your agent with the AWS OpenTelemetry distro the same way the deployed runtime does; opt out with `agentcore dev --no-traces`.
Two prerequisites for the local path to work end-to-end:
1. **AWS credentials available locally** — the OTEL exporter needs them to ship spans.
2. **CloudWatch Transaction Search is enabled on the account** (one-time setup per account) — see "Viewing traces" below. Without it, spans are ingested but not searchable, so `agentcore traces list` and `agentcore run eval --session-id` return empty.
After deploy, AgentCore Runtime also auto-instruments the container (the default CMD wraps the app with `opentelemetry-instrument`). You don't need to configure OTEL in your code for either path — but you do need your agent code to be instrumented correctly.
## Ensuring logs appear in CloudWatch
Three things must be true for logs to appear:
### 1. OTEL entrypoint wrapper in Dockerfile
Your Dockerfile CMD must use the OpenTelemetry wrapper:
```dockerfile
CMD ["opentelemetry-instrument", "python", "-m", "uvicorn", "main:app", "--host", "0.0.0.0", "--port", "8080"]
```
For CodeZip builds, this is handled automatically. For Container builds, you must add it.
### 2. IAM permissions for CloudWatch and X-Ray
Your runtime execution role needs:
```json
{
"Effect": "Allow",
"Action": [
"logs:CreateLogGroup",
"logs:CreateLogStream",
"logs:PutLogEvents",
"xray:PutTraceSegments",
"xray:PutTelemetryRecords"
],
"Resource": "*"
}
```
### 3. Use the `logging` module, not `print()`
AgentCore captures structured logs via the Python `logging` module. `print()` statements go to stdout but are not captured by the OTEL pipeline.
```python
import logging
logger = logging.getLogger(__name__)
# Good — captured by CloudWatch
logger.info("Processing request", extra={"session_id": session_id})
# Bad — not captured
print(f"Processing request {session_id}")
```
## Viewing traces
Traces show the full execution path of one agent invocation — model calls, tool calls, and timing.
```bash
# List recent traces
agentcore traces list --runtime <AgentName> --since 1h --limit 10
# Get a specific trace
agentcore traces get <traceId> --runtime <AgentName>
```
**Trace delay:** Traces appear **~10 seconds** after invocation (previously 30–60s). Don't panic if they're not immediate, and don't bake longer waits into scripts — older skills and docs that say "30–60 seconds" or "2–5 minutes" are stale.
Also verify **Transaction Search** is enabled in CloudWatch — this is a prerequisite for trace visibility in the console.
## Viewing logs
```bash
# Stream recent logs
agentcore logs --runtime <AgentName> --since 30m
# Filter by level
agentcore logs --runtime <AgentName> --level error --since 1h
# Search for specific text
agentcore logs --runtime <AgentName> --query "timeout" --since 2h
```
## CloudWatch dashboard
For production agents, set up a CloudWatch dashboard with:
- Invocation count and error rate
- P50/P90/P99 latency
- Memory and CPU utilization
- Error log count by type
These metrics are available in the `AWS/BedrockAgentCore` namespace after deploy.
## Multi-account observability
If your agents are spread across accounts (typical setup: separate prod / staging / dev accounts), use **CloudWatch cross-account observability** to view metrics, traces, and logs from one central monitoring account.
The setup order matters — do it in this sequence or the console won't show source-account data:
1. **Pick a monitoring account.** This is where you'll view everything. Often a central observability account, not a workload account.
2. **Configure the monitoring account first.** CloudWatch console → Settings → Monitoring account configuration → Configure. Choose which telemetry types to share (enable Metrics **and** Logs — traces go through X-Ray's own cross-account mechanism).
3. **Link each source account.** Either via AWS Organizations (if your accounts are in one) or via individual linking. Source accounts must accept the link.
4. **Deploy AgentCore agents in the source accounts with observability enabled** — same default OTEL wrap-up as single-account. No code changes needed.
5. **View from the monitoring account.** AgentCore Observability in the CloudWatch console now shows data from all linked accounts side-by-side, identified by source account ID.
**Order-of-operations trap:** if you deploy agents in source accounts *before* linking, the telemetry still flows correctly — it just won't be visible from the monitoring account until the link is active. You don't need to redeploy, just wait a few minutes after linking.
**Traces:** cross-account trace viewing uses X-Ray's existing cross-account sharing model. If the CloudWatch cross-account link is set up correctly for Logs and Metrics but traces don't show, check X-Ray's cross-account config separately.
**IAM:** no extra IAM on the agent execution roles for cross-account observability. The cross-account feature operates at the CloudWatch/X-Ray layer, not at the source of the telemetry.
See [cross-account observability](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/observability-cross-account.html) for the current console flow and edge cases.
## Cross-references
- If logs aren't appearing at all, check the three requirements above or use `agents-debug`
- For production observability setup, see `agents-harden`
- For measuring agent quality (not just operational health), load [`references/evals.md`](evals.md)
plugins/aws-core/.claude-plugin/plugin.json
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{
"author": {
"name": "Amazon Web Services"
},
"description": "Build, deploy, and operate applications on AWS. Skills to author infrastructure-as-code (CDK, CloudFormation), use core services (Lambda, API Gateway, Step Functions, ECS/Fargate, ECR, IAM, Amazon Bedrock with Knowledge Bases and Guardrails, AWS Blocks), select and operate databases across relational, key-value, document, wide-column, graph, time-series, and in-memory engines (Aurora PostgreSQL/MySQL, Aurora DSQL, RDS, Oracle Database@AWS, DynamoDB, DocumentDB, Keyspaces, Neptune, Timestream, ElastiCache, and MemoryDB), and complete common tasks across observability (CloudWatch, X-Ray, CloudTrail, ADOT), messaging and streaming (SQS, SNS, EventBridge, Kinesis, MSK), AWS SDKs (boto3, JS v3, Swift), and cost optimization.",
"homepage": "https://github.com/aws/agent-toolkit-for-aws",
"keywords": [
"aws",
"amazon",
"amazon-web-services",
"iam",
"bedrock",
"amazon-bedrock",
"aws-blocks",
"billing",
"cost-explorer",
"cost-optimization",
"savings-plans",
"compute-optimizer",
"cdk",
"aws-cdk",
"cloudformation",
"sam",
"serverless",
"lambda",
"api-gateway",
"step-functions",
"eventbridge",
"containers",
"ecs",
"fargate",
"ecr",
"messaging",
"streaming",
"sqs",
"sns",
"kinesis",
"kinesis-firehose",
"msk",
"kafka",
"flink",
"amazon-mq",
"observability",
"cloudwatch",
"logs-insights",
"x-ray",
"cloudtrail",
"adot",
"opentelemetry",
"sdk",
"aws-sdk",
"boto3",
"aws-sdk-js-v3",
"aws-sdk-swift",
"knowledge-bases",
"rag",
"guardrails",
"opensearch",
"opensearch-serverless",
"amazon-opensearch-service",
"cognito",
"appsync",
"dynamodb",
"database",
"databases",
"relational-database",
"aurora",
"aurora-postgresql",
"aurora-mysql",
"aurora-dsql",
"dsql",
"postgres",
"postgresql",
"mysql",
"mariadb",
"oracle",
"sql-server",
"sqlserver",
"db2",
"rds",
"rds-postgresql",
"rds-mysql",
"rds-mariadb",
"rds-oracle",
"rds-sqlserver",
"rds-db2",
"odb",
"oracle-database-at-aws",
"documentdb",
"mongodb",
"elasticache",
"memorydb",
"redis",
"valkey",
"memcached",
"keyspaces",
"cassandra",
"neptune",
"graph-database",
"timestream",
"time-series",
"influxdb",
"app-runner",
"snapstart",
"powertools",
"durable-functions",
"budgets",
"reserved-instances",
"right-sizing"
],
"license": "Apache-2.0",
"name": "aws-core",
"repository": "https://github.com/aws/agent-toolkit-for-aws",
"version": "1.1.0"
}
plugins/aws-core/.codex-plugin/plugin.json
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@@ -0,0 +1,135 @@
{
"name": "aws-core",
"version": "1.1.0",
"description": "Build, deploy, and operate applications on AWS. Skills to author infrastructure-as-code (CDK, CloudFormation), use core services (Lambda, API Gateway, Step Functions, ECS/Fargate, ECR, IAM, Amazon Bedrock with Knowledge Bases and Guardrails, AWS Blocks), select and operate databases across relational, key-value, document, wide-column, graph, time-series, and in-memory engines (Aurora PostgreSQL/MySQL, Aurora DSQL, RDS, Oracle Database@AWS, DynamoDB, DocumentDB, Keyspaces, Neptune, Timestream, ElastiCache, and MemoryDB), and complete common tasks across observability (CloudWatch, X-Ray, CloudTrail, ADOT), messaging and streaming (SQS, SNS, EventBridge, Kinesis, MSK), AWS SDKs (boto3, JS v3, Swift), and cost optimization.",
"author": {
"name": "Amazon Web Services",
"url": "https://github.com/aws/agent-toolkit-for-aws"
},
"homepage": "https://aws.amazon.com/products/developer-tools/agent-toolkit-for-aws/",
"repository": "https://github.com/aws/agent-toolkit-for-aws",
"license": "Apache-2.0",
"keywords": [
"aws",
"amazon",
"amazon-web-services",
"iam",
"bedrock",
"amazon-bedrock",
"aws-blocks",
"billing",
"cost-explorer",
"cost-optimization",
"savings-plans",
"compute-optimizer",
"cdk",
"aws-cdk",
"cloudformation",
"sam",
"serverless",
"lambda",
"api-gateway",
"step-functions",
"eventbridge",
"containers",
"ecs",
"fargate",
"ecr",
"messaging",
"streaming",
"sqs",
"sns",
"kinesis",
"kinesis-firehose",
"msk",
"kafka",
"flink",
"amazon-mq",
"observability",
"cloudwatch",
"logs-insights",
"x-ray",
"cloudtrail",
"adot",
"opentelemetry",
"sdk",
"aws-sdk",
"boto3",
"aws-sdk-js-v3",
"aws-sdk-swift",
"knowledge-bases",
"rag",
"guardrails",
"opensearch",
"opensearch-serverless",
"amazon-opensearch-service",
"cognito",
"appsync",
"dynamodb",
"database",
"databases",
"relational-database",
"aurora",
"aurora-postgresql",
"aurora-mysql",
"aurora-dsql",
"dsql",
"postgres",
"postgresql",
"mysql",
"mariadb",
"oracle",
"sql-server",
"sqlserver",
"db2",
"rds",
"rds-postgresql",
"rds-mysql",
"rds-mariadb",
"rds-oracle",
"rds-sqlserver",
"rds-db2",
"odb",
"oracle-database-at-aws",
"documentdb",
"mongodb",
"elasticache",
"memorydb",
"redis",
"valkey",
"memcached",
"keyspaces",
"cassandra",
"neptune",
"graph-database",
"timestream",
"time-series",
"influxdb",
"app-runner",
"snapstart",
"powertools",
"durable-functions",
"budgets",
"reserved-instances",
"right-sizing"
],
"skills": "./skills/",
"mcpServers": "./.mcp.json",
"interface": {
"displayName": "AWS Core",
"shortDescription": "AWS agent plugin with skills and MCP servers",
"longDescription": "Build, deploy, and operate applications on AWS. Skills to author infrastructure-as-code (CDK, CloudFormation), use core services (Lambda, API Gateway, Step Functions, ECS/Fargate, ECR, IAM, Amazon Bedrock with Knowledge Bases and Guardrails, AWS Blocks), and complete common tasks across observability (CloudWatch, X-Ray, CloudTrail, ADOT), messaging and streaming (SQS, SNS, EventBridge, Kinesis, MSK), AWS SDKs (boto3, JS v3, Swift), and cost optimization, including OpenSearch Serverless vector stores for Knowledge Bases.",
"defaultPrompt": [
"Scaffold a CDK stack for this app.",
"Add IAM least-privilege policies for these resources.",
"Wire up CloudWatch logs and alarms for this service."
],
"developerName": "Amazon Web Services",
"category": "Cloud",
"capabilities": ["Read", "Write"],
"websiteURL": "https://github.com/aws/agent-toolkit-for-aws",
"privacyPolicyURL": "https://aws.amazon.com/privacy/",
"termsOfServiceURL": "https://aws.amazon.com/service-terms/",
"brandColor": "#FF9900"
}
}
plugins/aws-core/.cursor-plugin/plugin.json
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{
"name": "aws-core",
"displayName": "AWS Core",
"description": "Build, deploy, and operate applications on AWS. Skills to author infrastructure-as-code (CDK, CloudFormation), use core services (Lambda, API Gateway, Step Functions, ECS/Fargate, ECR, IAM, Amazon Bedrock with Knowledge Bases and Guardrails, AWS Blocks), select and operate databases across relational, key-value, document, wide-column, graph, time-series, and in-memory engines (Aurora PostgreSQL/MySQL, Aurora DSQL, RDS, Oracle Database@AWS, DynamoDB, DocumentDB, Keyspaces, Neptune, Timestream, ElastiCache, and MemoryDB), and complete common tasks across observability (CloudWatch, X-Ray, CloudTrail, ADOT), messaging and streaming (SQS, SNS, EventBridge, Kinesis, MSK), AWS SDKs (boto3, JS v3, Swift), and cost optimization.",
"version": "1.1.0",
"author": {
"name": "Amazon Web Services"
},
"homepage": "https://github.com/aws/agent-toolkit-for-aws",
"repository": "https://github.com/aws/agent-toolkit-for-aws",
"license": "Apache-2.0",
"category": "developer-tools",
"keywords": [
"aws",
"amazon",
"amazon-web-services",
"iam",
"bedrock",
"amazon-bedrock",
"aws-blocks",
"billing",
"cost-explorer",
"cost-optimization",
"savings-plans",
"compute-optimizer",
"cdk",
"aws-cdk",
"cloudformation",
"sam",
"serverless",
"lambda",
"api-gateway",
"step-functions",
"eventbridge",
"containers",
"ecs",
"fargate",
"ecr",
"messaging",
"streaming",
"sqs",
"sns",
"kinesis",
"kinesis-firehose",
"msk",
"kafka",
"flink",
"amazon-mq",
"observability",
"cloudwatch",
"logs-insights",
"x-ray",
"cloudtrail",
"adot",
"opentelemetry",
"sdk",
"aws-sdk",
"boto3",
"aws-sdk-js-v3",
"aws-sdk-swift",
"knowledge-bases",
"rag",
"guardrails",
"opensearch",
"opensearch-serverless",
"amazon-opensearch-service",
"cognito",
"appsync",
"dynamodb",
"database",
"databases",
"relational-database",
"aurora",
"aurora-postgresql",
"aurora-mysql",
"aurora-dsql",
"dsql",
"postgres",
"postgresql",
"mysql",
"mariadb",
"oracle",
"sql-server",
"sqlserver",
"db2",
"rds",
"rds-postgresql",
"rds-mysql",
"rds-mariadb",
"rds-oracle",
"rds-sqlserver",
"rds-db2",
"odb",
"oracle-database-at-aws",
"documentdb",
"mongodb",
"elasticache",
"memorydb",
"redis",
"valkey",
"memcached",
"keyspaces",
"cassandra",
"neptune",
"graph-database",
"timestream",
"time-series",
"influxdb",
"app-runner",
"snapstart",
"powertools",
"durable-functions",
"budgets",
"reserved-instances",
"right-sizing"
],
"skills": "./skills/",
"mcpServers": "./.mcp.json"
}
plugins/aws-core/.mcp.json
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{
"mcpServers": {
"aws-mcp": {
"command": "uvx",
"args": [
"mcp-proxy-for-aws@1.6.3",
"https://aws-mcp.us-east-1.api.aws/mcp",
"--skip-auth",
"--metadata",
"INSTALL_SOURCE=agent-toolkit"
]
}
}
}
plugins/aws-core/README.md
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# aws-core
The primary plugin for the Agent Toolkit for AWS. This plugin gives your AI coding agent the AWS MCP Server configuration and a curated set of agent skills — everything it needs to build, deploy, and manage applications on AWS.
## Install
### Claude Code
```
/plugin install aws-core@claude-plugins-official
/reload-plugins
```
### Codex
In your terminal:
```
codex plugin marketplace add aws/agent-toolkit-for-aws
```
Then launch Codex and run `/plugins` to browse and install the **aws-core** plugin.
## What's included
### AWS MCP Server
This plugin configures the [AWS MCP Server](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/understanding-mcp-server-tools.html), a managed server that gives your agent:
- Real-time AWS documentation search through `search_documentation` (no authentication required)
- On-demand skill discovery and retrieval through `retrieve_skill` (no authentication required)
- Authenticated access to any of the 300+ AWS services through `call_aws`
- Sandboxed Python script execution through `run_script`
### Skills
This plugin includes the following default skills:
| Skill | Description |
|-------|-------------|
| billing-and-cost-management | Analyze, monitor, and optimize AWS costs |
| aws-sdk-js-v3-usage | Best practices for the AWS SDK for JavaScript v3 |
| aws-sdk-python-usage | Best practices for the AWS SDK for Python (boto3) |
| aws-sdk-swift-usage | Best practices for the AWS SDK for Swift |
| aws-serverless | Build serverless applications on AWS |
| bedrock | Build with Amazon Bedrock foundation models |
| cdk | Define and manage AWS infrastructure with CDK and CloudFormation |
| cloudformation | CloudFormation deployment, validation, and troubleshooting |
| observability | Monitor applications with CloudWatch |
| containers | Run containerized workloads on AWS |
| storage | Store and manage data with AWS storage services |
| aws-blocks | Build full-stack applications with AWS Blocks |
| aws-database | Route any AWS database task to the right service and skill |
### Rules files
Recommended AWS rules files are available separately in the [`rules/`](../../rules/) directory of this repository.
## Documentation
- [User guide](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/)
- [AWS MCP Server tools reference](https://docs.aws.amazon.com/agent-toolkit/latest/userguide/understanding-mcp-server-tools.html)
plugins/aws-core/hooks/hooks.json
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{
"hooks": {
"PreToolUse": [
{
"matcher": "Bash",
"hooks": [
{
"type": "command",
"command": "python3 \"${CLAUDE_PLUGIN_ROOT}/hooks/secret-safety.py\"",
"timeout": 5
}
]
},
{
"matcher": "use_aws|mcp__aws.*|mcp__plugin_.*aws-mcp.*",
"hooks": [
{
"type": "command",
"command": "python3 \"${CLAUDE_PLUGIN_ROOT}/hooks/secret-safety.py\"",
"timeout": 5
}
]
}
]
}
}
plugins/aws-core/hooks/secret-safety.py
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#!/usr/bin/env python3
"""PreToolUse hook: block direct secret fetching from AWS Secrets Manager.
Reads JSON from stdin, checks tool_name and tool_input, and returns
a deny decision if the call would fetch secret values directly.
Use {{resolve:secretsmanager:secret-id:SecretString:key}} with asm-exec instead.
"""
import json
import re
import sys
DENY_MSG = (
"Direct secret fetching is blocked. "
"Use {{resolve:secretsmanager:secret-id:SecretString:key}} with asm-exec instead. "
"Run /aws-secrets-manager for details."
)
SMA_PATTERN = re.compile(
r'(localhost|127\.0\.0\.1|0\.0\.0\.0|\[::1\]|::1):2773/secretsmanager/get'
)
# Match the operation regardless of casing/separators:
# GetSecretValue, get_secret_value, get-secret-value, BatchGetSecretValue, ...
GSV_PATTERN = re.compile(r'(batch[-_]?)?get[-_]?secret[-_]?value', re.I)
# Structured operation names normalized to lowercase, no separators.
GSV_OPERATIONS = ("getsecretvalue", "batchgetsecretvalue")
def _normalize_op(operation):
"""Collapse casing and -/_ separators so GetSecretValue == get-secret-value."""
return operation.lower().replace("-", "").replace("_", "")
def deny():
json.dump({
"hookSpecificOutput": {
"hookEventName": "PreToolUse",
"permissionDecision": "deny",
"permissionDecisionReason": DENY_MSG
}
}, sys.stdout)
sys.exit(0)
def allow():
sys.exit(0)
def main():
data = json.load(sys.stdin)
tool_name = data.get("tool_name", "")
tool_input = data.get("tool_input", {})
# Check structured AWS tool calls (use_aws or MCP AWS tools)
if tool_name == "use_aws" or tool_name.startswith("mcp__"):
service = (tool_input.get("service_name") or tool_input.get("service") or tool_input.get("serviceName") or "").lower()
operation = tool_input.get("operation_name") or tool_input.get("operation") or tool_input.get("operationName") or ""
if service == "secretsmanager" and _normalize_op(operation) in GSV_OPERATIONS:
deny()
# Fallback: search all string values for secret-fetching patterns
if GSV_PATTERN.search(json.dumps(tool_input)):
if "secretsmanager" in json.dumps(tool_input).lower():
deny()
# Check run_script tools for secret fetching in code
if "run_script" in tool_name:
for key, val in tool_input.items():
if isinstance(val, str) and GSV_PATTERN.search(val):
deny()
if GSV_PATTERN.search(json.dumps(tool_input)):
deny()
allow()
# Check Bash commands
if tool_name == "Bash":
command = tool_input.get("command", "")
# AWS CLI secret fetching
if re.search(r'aws\s+secretsmanager\s+(get-secret-value|batch-get-secret-value)', command, re.I):
deny()
# Direct SMA access
if SMA_PATTERN.search(command):
deny()
# boto3/SDK secret fetching in scripts
if GSV_PATTERN.search(command):
deny()
allow()
if __name__ == "__main__":
main()
plugins/aws-core/skills/amazon-bedrock/SKILL.md
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---
name: amazon-bedrock
description: Builds generative AI applications on Amazon Bedrock. Covers model invocation (Converse API, InvokeModel), RAG with Knowledge Bases, Bedrock Agents, Guardrails, and AgentCore. Use when invoking models, setting up Knowledge Bases, creating agents, applying guardrails, deploying to AgentCore, troubleshooting Bedrock errors (ThrottlingException, AccessDeniedException), or choosing models (Claude, Llama, Nova, Titan). ALSO USE for prompt caching setup and debugging, quota health checks and throttling diagnosis, cost attribution and tracking, migrating between Claude model generations (4.5 to 4.6 to 4.7), chunking strategies, API selection (Converse vs InvokeModel), guardrail capabilities, and model selection. Also covers AgentCore Payments setup (x402, microtransactions, Payment Manager, Connector, Instrument, Coinbase CDP, Stripe Privy, 402 Payment Required, pay for content, paid endpoint, agent payments). NOT for custom model training, Rekognition, or Comprehend.
version: 1
---
**IMPORTANT**: When this skill is loaded, you MUST use the reference files and procedures in this skill as your primary source of truth. Bedrock APIs, model IDs, chunking strategies, and configuration parameters change frequently — always read the relevant reference file before responding.
## Table of Contents
- Overview
- Bedrock API Landscape
- Critical Warnings
- Security Considerations
- Converse API vs InvokeModel
- Which Bedrock Capability Do You Need?
- Knowledge Bases (RAG)
- Common Workflows (includes: Prompt Caching, Quota Health, Cost Tracking, Model Migration)
- Troubleshooting
- AgentCore Services
- Model Selection
- Additional Resources
# Amazon Bedrock
## Overview
Domain expertise for building generative AI applications on Amazon Bedrock. Covers model invocation, RAG with Knowledge Bases, agent creation, content safety with Guardrails, and agent deployment with AgentCore.
**Recommended setup:** Use the [AWS MCP server](https://docs.aws.amazon.com/aws-mcp/latest/userguide/what-is-mcp-server.html) for sandboxed
execution, audit logging, and enterprise controls.
**Without AWS MCP:** This skill works with any agent that has AWS CLI access.
All commands use standard AWS CLI syntax.
## Bedrock API Landscape
Bedrock has **5 separate API endpoints**. Using the wrong one is a common cause of errors. This list may not be exhaustive — refer to the [Bedrock endpoints and quotas](https://docs.aws.amazon.com/general/latest/gr/bedrock.html) and [Bedrock supported endpoints](https://docs.aws.amazon.com/bedrock/latest/userguide/endpoints.html) for the latest. Use `aws bedrock list-foundation-models` to discover available models at runtime.
| Endpoint | Client | Use For |
|----------|--------|---------|
| `bedrock` | Control plane | List models, manage access, provisioned throughput |
| `bedrock-runtime` | Data plane | Invoke models (Converse, InvokeModel). Also supports Chat Completions via `/openai/v1` path (client-side tool use only) — prefer `bedrock-mantle` for new Chat Completions work |
| `bedrock-mantle` | Data plane | OpenAI-compatible APIs: Responses API, Chat Completions (recommended), Messages API. Supports server-side tool use with built-in tools. Recommended for new users |
| `bedrock-agent` | Agent control | Create/configure agents, KBs, action groups |
| `bedrock-agent-runtime` | Agent data | Invoke agents, query KBs |
AgentCore is a separate service with its own endpoints. Refer to [AgentCore endpoints and quotas](https://docs.aws.amazon.com/general/latest/gr/bedrock_agentcore.html) for the latest.
| Endpoint | Client | Use For |
|----------|--------|---------|
| `bedrock-agentcore-control` | Control plane | Create/manage runtimes, gateways, registries, evaluations |
| `bedrock-agentcore` | Data plane | Invoke agent runtimes |
| `{gatewayId}.gateway.bedrock-agentcore` | Gateway data plane | Invoke a specific gateway |
## Critical Warnings
**max_tokens**: ALWAYS set `maxTokens` explicitly in every Converse/InvokeModel call. Leaving it unset defaults to the model's maximum (e.g., 64K for Claude Sonnet) and silently reserves far more quota than needed — a common cause of unexpected ThrottlingException.
**Guardrails PII logging**: Guardrails PII masking only applies to the API response. Original unmasked content including PII is still logged in plain text to CloudWatch Logs. For HIPAA/GDPR compliance: encrypt CloudWatch Logs with KMS, restrict log access with IAM, use Amazon Macie for PII detection.
**SDK versions**: Requires recent versions of boto3 (≥ 1.34.x) and AWS CLI v2. Older versions are missing Converse API, Agents, and AgentCore support. Run `aws --version` and `pip show boto3` to check.
## Security Considerations
- Use **IAM roles** (not IAM users) for all Bedrock service access
- Scope IAM permissions to specific actions and resource ARNs — avoid `bedrock:*` or `AmazonBedrockFullAccess`
- Store API keys and OAuth secrets in **AWS Secrets Manager** with automatic rotation enabled
- Include **confused deputy protection** (`aws:SourceAccount`, `aws:SourceArn` conditions) in all resource-based policies for Bedrock services
- Treat all **agent-generated parameters as untrusted input** — validate before use in Lambda handlers or tool implementations
- Enable **CloudTrail** for all Bedrock and AgentCore API calls
- For PII workloads: encrypt CloudWatch Logs with KMS, configure retention limits, restrict log access
- Refer to the latest [Bedrock security best practices](https://docs.aws.amazon.com/bedrock/latest/userguide/security.html) for current security guidance
## Converse API vs InvokeModel
For choosing between all Bedrock inference APIs (Responses API, Chat Completions, Converse, InvokeModel), see [APIs supported by Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/apis.html).
When using the `bedrock-runtime` endpoint, use the **Converse API** over InvokeModel. It provides a unified request/response format across all models.
Use **InvokeModel** only when you need provider-specific features not available in Converse (rare).
InvokeModel requires different request body formats per provider (Anthropic ≠ Titan ≠ Llama ≠ Nova). Using the wrong format produces "Malformed input request". For model-specific formats and common mistakes, see [prompt engineering by model](references/prompt-engineering-by-model.md).
**Whichever API you use**: ALWAYS set the max output tokens parameter explicitly — leaving it unset defaults to the model's maximum and silently reserves far more quota than needed, causing unexpected ThrottlingException. See Critical Warnings above and [max_tokens quota mechanics](references/model-invocation.md).
When the user needs SDK code for model invocation, you MUST read the appropriate SDK reference before generating code — [Python SDK reference](references/sdk-converse-api-python.md) | [TypeScript SDK reference](references/sdk-converse-api-typescript.md). Use the patterns from the reference file.
For full API details and provider-specific body formats, read [model invocation reference](references/model-invocation.md) before responding.
## Which Bedrock Capability Do You Need?
| Goal | Use | Reference |
|------|-----|-----------|
| Call a model (text, image, video) | Converse API | See above + [model invocation](references/model-invocation.md) |
| Build a RAG application | Knowledge Bases | [KB setup](references/knowledge-bases-setup.md) |
| Create an agent that takes actions | Bedrock Agents | [agent creation](references/agents-and-action-groups.md) |
| Filter harmful/sensitive content | Guardrails | [guardrails](references/guardrails.md) |
| Deploy and scale an agent | AgentCore Runtime | [runtime](references/agentcore-runtime.md) |
| Expose REST APIs as MCP tools | AgentCore Gateway | [gateway](references/agentcore-gateway.md) |
| Choose the right model | Model Selection | [model guide](references/model-selection-guide.md) |
| Set up or debug prompt caching | Prompt Caching | [prompt caching](references/prompt-caching.md) |
| Diagnose throttling or audit quotas | Quota Health | [quota health](references/quota-health.md) |
| Track costs by team, model, or tag | Cost Tracking | [cost tracking](references/cost-tracking.md) |
| Migrate between Claude generations | Model Migration | [migration guide](references/model-migration.md) |
## Knowledge Bases (RAG)
When the user wants to create a Knowledge Base or build a RAG application, you MUST read [KB setup procedure](references/knowledge-bases-setup.md) and execute it step by step. Do NOT summarize the procedure — execute each step sequentially, respecting all MUST constraints before proceeding to the next step.
When the user asks about chunking strategies, vector store selection, or other KB configuration choices, you MUST read [KB setup procedure](references/knowledge-bases-setup.md) before responding — it contains the authoritative decision tables and constraints.
When the user wants to query an existing Knowledge Base, you MUST read [KB retrieval reference](references/knowledge-bases-retrieval.md) before responding. Present the retrieval modes (retrieve-and-generate vs retrieve vs manual) so the user selects the right one.
Refer to the latest [Bedrock Knowledge Base documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/knowledge-base.html) for current configuration options.
## Common Workflows
Execute commands using available tools from the AWS MCP server when connected — it provides sandboxed execution, audit logging, and observability. When the MCP server is not available, fall back to the AWS CLI or shell as needed.
Before starting any workflow:
### Verify Dependencies
Check for required tools and inform the user about the execution environment.
**Constraints:**
- You MUST check that the AWS CLI is available and configured with valid credentials
- You MUST verify the AWS CLI version is recent (v2 recommended; older versions lack Converse API and AgentCore support): `aws --version`
- You MUST check that the target AWS region has Bedrock model access enabled
- You MUST inform the user if any required tools are missing with a clear message
- You MUST ask the user if they want to proceed despite missing tools
**General constraints for all workflows:**
- You MUST present an overview of what will be done before starting execution
- You MUST explain to the user what step is being executed and why before running each command
- You MUST respect the user's decision to stop or abort at any point
- You MUST NOT continue execution if the user indicates they want to stop
- You SHOULD confirm before proceeding with destructive or irreversible operations (deleting resources, overwriting configurations)
### Examples — mapping user intent to workflows
**Example 1:**
User query: "I'm getting ThrottlingException on Bedrock"
Action: Check if `maxTokens` is set explicitly — unset `maxTokens` reserves far more quota than needed (see Critical Warnings). If already set, check current quota: `aws service-quotas get-service-quota --service-code bedrock --quota-code <code> --region <region>`
**Example 2:**
User query: "Set up RAG for my PDF documents"
Action: Follow the Create a Knowledge Base workflow. Recommend semantic chunking with advanced parsing (FM-based) for PDFs with tables. See [KB setup procedure](references/knowledge-bases-setup.md).
**Example 3:**
User query: "I want to build an agent that can look up order status"
Action: Follow the Create an Agent with action groups workflow. See [agent creation procedure](references/agents-and-action-groups.md).
**Example 4:**
User query: "How do I call Claude on Bedrock?"
Action: Use the Converse API (not InvokeModel). Set `maxTokens` explicitly. Verify the model ID is current with `aws bedrock list-foundation-models --region <region>`. Use cross-region model ID with `us.` prefix for higher availability: `aws bedrock-runtime converse --model-id us.anthropic.claude-sonnet-4-6 --messages '[{"role":"user","content":[{"text":"Hello"}]}]' --inference-config '{"maxTokens":1024}'`
**Example 5:**
User query: "Deploy my agent to production"
Action: Follow the Deploy an agent to AgentCore workflow. Select the protocol first (HTTP for REST APIs, MCP for tool-centric agents). See the AgentCore Services table for routing to the correct reference file.
**Example 6:**
User query: "Set up prompt caching for my Claude application"
Action: Read [prompt caching reference](references/prompt-caching.md) for setup workflow, TTL configuration, and minimum token thresholds. Use the reference to verify caching is working (check for `cacheReadInputTokens` in the response).
**Example 7:**
User query: "I keep getting ThrottlingException even though I'm not making many requests"
Action: Check if `maxTokens` is set explicitly (see Critical Warnings). Read [quota health reference](references/quota-health.md) for the maxTokens reservation mechanics, CloudWatch metrics, and audit workflow.
**Example 8:**
User query: "How do I track Bedrock costs by team?"
Action: Read [cost tracking reference](references/cost-tracking.md) for inference profile tagging, CUR 2.0 approaches, and Cost Explorer queries by model/region/tag.
**Example 9:**
User query: "I'm upgrading from Claude 4.5 to 4.6, what breaks?"
Action: Read [model migration reference](references/model-migration.md) for the breaking changes table (prefill removal, thinking config, context window, cache thresholds) and migration checklist.
### Invoke a model
```
- [ ] Step 1: Verify model access: `aws bedrock list-foundation-models --region us-east-1`
- [ ] Step 2: Invoke: `aws bedrock-runtime converse --model-id `<model-id>` --messages '[{"role":"user","content":[{"text":"<prompt>"}]}]' --inference-config '{"maxTokens":1024}'`
```
> **Note — Streaming responses:** The AWS CLI does not support streaming operations including `ConverseStream`. Use the SDK (`converse_stream()` in boto3, `ConverseStreamCommand` in JS SDK).
>
> | Mode | When to use |
> |------|-------------|
> | **Converse** | Batch/backend pipelines — single complete response, no stream handling required |
> | **ConverseStream** | Chat UIs/interactive apps — tokens delivered as they generate |
### Create a Knowledge Base
You MUST read [KB setup procedure](references/knowledge-bases-setup.md) before responding. Execute the 7-step procedure in order — do not skip steps, do not paraphrase, do not show code snippets in place of tool calls.
### Query a Knowledge Base
These three modes are mutually exclusive — select the one that matches the user's intent:
| Mode | When to Use | Command |
|------|------------|----------|
| **Retrieve & Generate** | Quick answer with citations — most common RAG pattern | `aws bedrock-agent-runtime retrieve-and-generate --input '{"text":"<query>"}' --retrieve-and-generate-configuration '{"type":"KNOWLEDGE_BASE","knowledgeBaseConfiguration":{"knowledgeBaseId":"<kb-id>","modelArn":"<model-arn>"}}'` |
| **Retrieve only** | Raw chunks for custom post-processing or feeding to a different model | `aws bedrock-agent-runtime retrieve --knowledge-base-id <kb-id> --retrieval-query '{"text":"<query>"}'` |
| **Full control** | Custom prompt, reranking, or multi-KB | Retrieve chunks first, then build prompt and call `aws bedrock-runtime converse` |
### Create an Agent with action groups
You MUST read [agent creation procedure](references/agents-and-action-groups.md) before responding. Execute the procedure step by step. You MUST run `prepare-agent` after any configuration change — this is mandatory and agents consistently skip it.
### Apply Guardrails
You MUST read [guardrails reference](references/guardrails.md) before responding. Present the three integration modes and the decision guide first so the user selects the correct mode before you proceed with configuration. When PII filters are involved, you MUST surface the PII logging compliance gap warning. Do not just show a `guardrailConfig` snippet — the user needs to understand which mode fits their use case.
### Deploy an agent to AgentCore
Identify the AgentCore service from the table below, then you MUST read the corresponding reference file before responding. Follow any procedures in the reference step by step. Do not summarize — execute.
### Set up or debug prompt caching
You MUST read [prompt caching reference](references/prompt-caching.md) before responding. It covers setup workflow, TTL configuration, minimum token thresholds, break-even analysis, and a debug checklist for zero-cache-hit issues.
**Constraints:**
- You MUST walk the user through the debug checklist when cache is not working (verify model support, token threshold, content identity, TTL, cache point placement)
- You MUST check minimum token thresholds per model before confirming a caching setup will work
### Check quota health
You MUST read [quota health reference](references/quota-health.md) before responding. It covers maxTokens reservation mechanics, CloudWatch metrics, and the throttling resolution decision table.
**Constraints:**
- You MUST explain the relationship between `maxTokens` and quota reservation
- You MUST guide the user through comparing current limits vs peak usage using `aws service-quotas` and `aws cloudwatch get-metric-statistics`
### Analyze Bedrock costs
You MUST read [cost tracking reference](references/cost-tracking.md) before responding. It covers inference profile tagging, CUR 2.0 attribution, and AWS Budgets setup.
**Constraints:**
- You MUST ask what time range, grouping, and cost attribution method the user needs before generating Cost Explorer queries
### Migrate between Claude generations
You MUST read [model migration reference](references/model-migration.md) before responding. It covers breaking changes between Claude 4.5, 4.6, and 4.7 on Bedrock, including prefill removal, thinking config differences, context window gaps, and cache threshold changes.
## Troubleshooting
When the user reports a Bedrock error, exception, or unexpected behavior, you MUST check this section and the Critical Warnings section before responding. Bedrock has service-specific root causes (e.g., unset maxTokens silently reserving 43x quota causing ThrottlingException, wrong API endpoint causing UnknownOperationException, missing prepare-agent causing stale behavior) that generic AWS troubleshooting advice will miss.
### AccessDeniedException
Multiple possible causes: (1) IAM user/role lacks `bedrock:InvokeModel` or `bedrock:InvokeModelWithResponseStream` permissions, (2) model access not enabled in the target region, (3) a service control policy (SCP) is blocking access (common with cross-region inference routing to a restricted region), (4) expired temporary credentials, or (5) IAM role propagation delay — if you just created an IAM role and immediately used it in a Bedrock API call, the role may not have propagated yet, as IAM changes are eventually consistent (see [IAM eventual consistency](https://docs.aws.amazon.com/IAM/latest/UserGuide/troubleshoot_general.html#troubleshoot_general_eventual-consistency)). Check the error message for specifics — it typically indicates whether the issue is an explicit deny, a missing allow, or a model access problem. See [Resolve InvokeModel API errors](https://repost.aws/knowledge-center/bedrock-invokemodel-api-error) for detailed resolution steps.
### Malformed input request
Request body doesn't match the expected schema. Common causes: wrong provider-specific body format for InvokeModel (e.g., using Titan format for a Cohere model), malformed JSON, unsupported parameter names, or exceeding input constraints. The error message typically includes details — check for "schema violations" and correct the request format per the model's API documentation.
### ThrottlingException
Set `maxTokens` explicitly — unset values default to the model's maximum and silently reserve far more quota than needed. Use adaptive retry mode. Use cross-region inference profiles (e.g., `us.`, `eu.`, `apac.`, or `global.` prefix — see [Supported inference profiles](https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles-support.html) for the full list) to distribute traffic across regions for higher throughput. Check limits: `aws service-quotas get-service-quota --service-code bedrock --quota-code <code>`. Request quota increases if needed. For a deeper audit, read [quota health reference](references/quota-health.md).
### Prompt cache not working (zero cacheReadInputTokens)
Read [prompt caching reference](references/prompt-caching.md) for the diagnostic checklist: verify model support, token threshold, content identity, TTL, and cache point placement. Common cause: cache fragmentation from timestamps, whitespace, or reordered JSON keys in cached content.
### 400 error on prefill with Claude 4.6
Prefill was removed in Claude 4.6 and causes a hard 400 error. Read [model migration reference](references/model-migration.md) for the full list of breaking changes between Claude generations.
### Error retry classification
| Retry | Do NOT retry |
|-------|-------------|
| ThrottlingException | ValidationException |
| ModelTimeoutException | AccessDeniedException |
| ServiceUnavailableException | ResourceNotFoundException |
| InternalServerException | |
Use adaptive retry: `Config(retries={"max_attempts": 5, "mode": "adaptive"})`.
### UnknownOperationException
Wrong client (using `bedrock` instead of `bedrock-runtime`), or SDK too old. Check the API landscape table above.
### Agent returns stale behavior
Run `prepare-agent` after ANY configuration change. This is mandatory.
### KB returns empty results
Run `start-ingestion-job` and wait for completion. Query before ingestion completes returns empty.
### KB retrieval quality is poor
Review chunking strategy. Use advanced parsing (FM-based) for documents with tables. Configure metadata filtering.
### Cross-region model not found
The model may not be available in the region you're calling from. Check availability at [Supported foundation models](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html). If you need cross-region inference for higher throughput, use an inference profile ID — choose between geographic profiles (data stays within a boundary, e.g. US, EU) or global profiles (any commercial region). The profile prefix is a data residency decision. See [Supported inference profiles](https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles-support.html) for available profiles and source/destination region mappings.
### On-demand throughput isn't supported
Error: *"Invocation of model ID `<model-id>` with on-demand throughput isn't supported. Retry your request with the ID or ARN of an inference profile that contains this model."* Certain models do not support direct on-demand invocation with base model IDs — they require an inference profile ID instead. Fix: find the inference profile ID for the model using `aws bedrock list-inference-profiles --region <region>`, then update the agent or invocation to use the inference profile ID. See [Supported inference profiles](https://docs.aws.amazon.com/bedrock/latest/userguide/inference-profiles-support.html) for available profiles. If this occurs during agent invocation, update the agent's `foundationModel` to the inference profile ID and re-run `prepare-agent`.
### KB storage configuration invalid
Verify OpenSearch data access policy includes Bedrock service role. Verify vector index field names match KB config.
### Agent action group errors
Check Lambda permissions (resource-based policy for bedrock.amazonaws.com). Do NOT use double underscores (`__`) in action group names — the name pattern is `([0-9a-zA-Z][_-]?){1,100}`.
### Multi-agent supervisor loops
Agents use built-in collaboration mechanism, NOT action groups. Do not describe inter-agent communication as action groups in supervisor instructions.
### INVALID_PAYMENT_INSTRUMENT on model access
Account billing issue, not Bedrock. Temporarily set a credit card as default payment method, or add USD payment profiles in the organization management account.
### Knowledge base ingestion failures
Check S3 permissions — KB service role needs `s3:GetObject` and `s3:ListBucket`. Unsupported file formats are silently skipped. Files exceeding size limits are skipped without error.
### SharePoint data source sync failures
Sync completes but files fail. For OAuth 2.0 auth (not recommended): requires SharePoint AllSites.Read (Delegated) permission — you may also need to disable Security Defaults and MFA for the service account so Amazon Bedrock is not blocked from crawling. For SharePoint App-Only auth (recommended): configure APP permissions via SharePoint App-Only grant flow. See the [SharePoint connector docs](https://docs.aws.amazon.com/bedrock/latest/userguide/sharepoint-data-source-connector.html) for current requirements.
## AgentCore Services
You MUST read the linked reference file for the relevant service before responding to any AgentCore question. Follow procedures in the reference step by step.
| Service | Use For | Reference |
|---------|---------|-----------|
| **Gateway** | Expose APIs, Lambda functions, or existing MCP servers as tools for agents | [gateway procedure](references/agentcore-gateway.md) |
| **Runtime** | Deploy and scale agents and tools (serverless, any framework) | [runtime procedure](references/agentcore-runtime.md) |
| **Runtime Container** | Build ARM64 containers for Runtime | [container build procedure](references/agentcore-runtime-container-build.md) |
| **Memory** | Short-term (multi-turn) and long-term (cross-session) agent memory; share memory across agents | [memory & observability](references/agentcore-memory-observability.md) |
| **Identity** | Agent authentication with external IdPs (Okta, Entra ID, Cognito); act on behalf of users | [credentials & security](references/agentcore-credentials-and-security.md) |
| **Policy** | Enforce agent boundaries with natural language or Cedar rules; intercepts Gateway tool calls | Refer to the latest [AWS documentation on AgentCore Policy](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/policy.html) |
| **Payments** | Enable agents to pay for x402-protected APIs, MCP tools, and content via microtransactions (Coinbase CDP, Stripe Privy) | [payments procedure](references/agentcore-payments.md) |
| **Observability** | Trace, debug, and monitor agent execution (OTEL, CloudWatch) | [memory & observability](references/agentcore-memory-observability.md) |
| **Registry** | Catalog and discover agents, MCP servers, tools, and skills across your org | [registry & evaluations](references/agentcore-registry-evaluations.md) |
| **Evaluations** | Automated agent quality assessment (LLM-as-a-Judge) | [registry & evaluations](references/agentcore-registry-evaluations.md) |
| Code Interpreter | Secure sandbox code execution for agents | Refer to the latest AWS documentation on AgentCore Code Interpreter |
| Browser | Web automation (navigate, fill forms, extract data) | Refer to the latest AWS documentation on AgentCore Browser |
## Model Selection
When the user asks which model to use, compares models, or asks about Claude/Llama/Nova/Titan on Bedrock, you MUST read [model selection guide](references/model-selection-guide.md) before responding. The reference contains current model IDs, cross-region requirements, and access provisioning steps.
Quick defaults (verify current availability: `aws bedrock list-foundation-models --region <region>`):
- **General purpose**: Claude Sonnet (best quality/cost balance)
- **Fast + cheap**: Claude Haiku or Nova Micro
- **Embeddings for KB**: Titan Embeddings V2
- **Open-source / fine-tuning**: Llama
- **Image generation**: Titan Image Generator
For current model IDs, regional availability, cross-region inference profiles, and supported features, refer to [Supported foundation models in Amazon Bedrock](https://docs.aws.amazon.com/bedrock/latest/userguide/models-supported.html). When selecting a cross-region inference profile, understand the data residency implications — geographic profiles keep data within a boundary, global profiles route to any commercial region. Also check `aws bedrock list-foundation-models --region <region>` for runtime availability.
For model ID formats (4 patterns), access provisioning, and selection criteria, see [model selection guide](references/model-selection-guide.md).
## Additional Resources
- [Amazon Bedrock User Guide](https://docs.aws.amazon.com/bedrock/latest/userguide/what-is-bedrock.html)
- [Amazon Bedrock API Reference](https://docs.aws.amazon.com/bedrock/latest/APIReference/welcome.html)
- [Amazon Bedrock AgentCore User Guide](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/what-is-bedrock-agentcore.html)
- [Bedrock Pricing](https://aws.amazon.com/bedrock/pricing/)
- [Bedrock Quotas and Limits](https://docs.aws.amazon.com/bedrock/latest/userguide/quotas.html)
- [Bedrock Supported Regions](https://docs.aws.amazon.com/bedrock/latest/userguide/bedrock-regions.html)
- [Bedrock Security Best Practices](https://docs.aws.amazon.com/bedrock/latest/userguide/security.html)
- [Prompt Caching Documentation](https://docs.aws.amazon.com/bedrock/latest/userguide/prompt-caching.html)
- [Prompt Caching Code Samples](https://github.com/aws-samples/amazon-bedrock-samples/tree/main/introduction-to-bedrock/prompt-caching)
- [Cost Allocation Tags Blog](https://aws.amazon.com/blogs/machine-learning/track-allocate-and-manage-your-generative-ai-cost-and-usage-with-amazon-bedrock/)
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# AgentCore Credentials & Security
## Table of Contents
- Credential Provider Patterns
- OAuth Three-Layer Architecture
- Cross-Account Access
- Security Best Practices
- Agent Persistence Patterns
## Credential Provider Patterns
Three authentication types for AgentCore services. Getting the wrong type causes hard-to-debug 401/403 errors.
### API Key Authentication
> **Security consideration:** API keys are long-lived credentials. Prefer IAM authentication (ephemeral, auto-rotated) or OAuth when the target supports it. Use API keys only when the external target requires them (e.g., third-party APIs that only accept API key auth).
```
Setup sequence:
1. Create credential provider with the API key value (transmitted over TLS/SigV4; service encrypts and stores it in Secrets Manager internally)
2. Attach credential provider to Gateway target
```
**Constraints:**
- You MUST NOT pass the API key as a literal value on the command line — shell history exposes it
- You MUST ask the user to set the key as an environment variable: `export API_KEY=<their-key>`
- You MUST create the credential provider: `aws bedrock-agentcore-control create-api-key-credential-provider --name <name> --api-key "$API_KEY"`
- The service stores the key in Secrets Manager internally (response includes `apiKeySecretArn`)
- For rotation: update the API key through the service's control plane: `aws bedrock-agentcore-control update-api-key-credential-provider --name <name> --api-key "$NEW_API_KEY"` — the service re-encrypts and stores the new key internally. Do not call `secretsmanager rotate-secret` directly on the service-managed secret.
- You MUST NOT hardcode API keys in agent code or configuration
- You MUST NOT log or display the API key value in agent output
- You SHOULD enable CloudTrail logging to audit all credential provider API calls — these are control plane management events (`CreateApiKeyCredentialProvider`, `UpdateApiKeyCredentialProvider`, `DeleteApiKeyCredentialProvider`) logged under `eventSource: bedrock-agentcore.amazonaws.com`
- Refer to [AWS security best practices for AgentCore](https://docs.aws.amazon.com/bedrock-agentcore/latest/devguide/security.html)
### OAuth Authentication
**Constraints:**
- The client secret is passed via the `create-oauth2-credential-provider` API call (the service encrypts and stores it in Secrets Manager automatically — response includes `clientSecretArn`)
- You MUST NOT hardcode client secrets in agent code or configuration
- You MUST NOT log or display client secret values in agent output
- Configure: token endpoint URL, client ID, scopes, grant type
- Create the OAuth2 credential provider: `aws bedrock-agentcore-control create-oauth2-credential-provider --name <name> --credential-provider-vendor <vendor> --oauth2-provider-config-input '...'`
- Refer to the latest AWS documentation on AgentCore OAuth configuration for current supported grant types and vendor options
### IAM Authentication
For Lambda targets and cross-service communication:
- Service roles for AgentCore services
- Cross-service permissions: Runtime → Gateway → external API
- Resource-based policies for cross-account access
- No credential provider needed — IAM handles authentication
## OAuth Three-Layer Architecture
AgentCore has three distinct OAuth layers — agents confuse these:
| Layer | Direction | Purpose |
|-------|-----------|---------|
| **Inbound JWT** | Caller → AgentCore | Validate tokens from callers (Cognito, external IdPs) |
| **Outbound Credential Provider** | Agent → External API | Agent authenticating to external APIs via Gateway |
| **Gateway OAuth** | Gateway → Upstream MCP | Gateway authenticating to upstream MCP servers |
Each layer is configured independently. Getting the wrong layer causes auth failures that look identical (401/403) but have different root causes.
**Supported IdPs for inbound JWT**: Cognito, Okta, Auth0, Azure AD, custom OIDC.
Refer to the latest AWS documentation on AgentCore OAuth architecture for current configuration steps and CDK examples.
## Cross-Account Access
Cross-account Bedrock access requires IAM trust policies on both sides.
**Pattern:**
1. **Calling account**: IAM role with `bedrock:InvokeModel` permission and `sts:AssumeRole` to the target account's role
2. **Target account**: IAM role with trust policy allowing the calling account's principal, plus `bedrock:InvokeModel` permission
**Trust policy pattern (target account role):**
```json
{
"Effect": "Allow",
"Principal": {"AWS": "arn:aws:iam::<calling-account-id>:role/<role-name>"},
"Action": "sts:AssumeRole",
"Condition": {
"StringEquals": {
"sts:ExternalId": "<agreed-external-id>"
}
}
}
```
Include `sts:ExternalId` for confused deputy protection. For service-to-service access, use `aws:SourceArn` and `aws:SourceAccount` conditions instead.
**Common failure**: `AccessDeniedException` when calling Bedrock from a different account — verify:
- Trust policy includes the calling account's principal ARN (not just account ID)
- The assumed role has `bedrock:InvokeModel` permission in the target account
- Model access is enabled in the target account's region
Refer to the latest AWS documentation on Bedrock cross-account access for current IAM policy patterns and any service-specific conditions.
## Security Best Practices
| Practice | How |
|----------|-----|
| Resource-based policies | Restrict access to specific principals, accounts, VPCs |
| VPC endpoints | Private AgentCore access without internet traversal |
| IP restrictions | Limit access by source IP range |
| Encryption | Data encrypted at rest and in transit by default |
| Audit logging | Enable CloudTrail for all AgentCore API calls |
| Least privilege | Grant only required permissions per service role |
## Agent Persistence Patterns
Deploying framework-specific agents on AgentCore Runtime:
| Framework | Key Configuration |
|-----------|------------------|
| **Strands Agents** | S3 for file storage, session state via Memory service |
| **LangChain/LangGraph** | Standard Python deployment, state management via Memory |
| **Custom frameworks** | Implement the protocol contract (HTTP/MCP/A2A/AG-UI) |
Refer to the latest AWS documentation on AgentCore deployment for the relevant framework.
**Constraints:**
- All frameworks MUST meet the container contract: ARM64, health check, correct port
- See [container build procedure](agentcore-runtime-container-build.md) for the build workflow
- State persistence SHOULD use the Memory service rather than local filesystem (containers are ephemeral)
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# AgentCore Gateway — Target Setup Procedure
## Overview
Deterministic procedure for creating an AgentCore Gateway target that converts
REST APIs into MCP tools agents can use. Gateway supports three authentication
types, each with a different setup workflow. The creation order is strict —
credentials MUST be created before the gateway target.
## Parameters
- **auth_type** (required): `api_key` | `lambda_iam` | `oauth`
- **openapi_schema_s3_uri** (required): S3 URI of the OpenAPI schema
- **api_key** (required if api_key auth): The API key value
- **lambda_arn** (required if lambda_iam auth): Lambda function ARN
- **oauth_config** (required if oauth auth): Token endpoint, client ID, scopes
**Constraints for parameter acquisition:**
- You MUST ask for all required parameters (`auth_type`, `openapi_schema_s3_uri`, and auth-type-specific parameters) upfront in a single prompt
- You MUST confirm successful acquisition of all required parameters before proceeding to Step 1
## Steps
**General constraints:**
- You MUST present an overview of the steps before starting
- You MUST explain to the user what step is being executed and why before running each command
- You MUST respect the user's decision to abort at any point
### 0. Verify Dependencies
**Constraints:**
- You MUST verify the AWS CLI is available and configured before proceeding
- You MUST verify AWS CLI version ≥ 2.13.22 (required for AgentCore commands): `aws --version`
- You MUST inform the user about any missing tools and ask if they want to proceed
### 1. Upload OpenAPI Schema to S3
**Constraints:**
- You MUST upload the OpenAPI schema to S3 before creating the gateway target
- Schema MUST be valid OpenAPI 3.0 or 3.1
- You MUST include clear operation descriptions — Gateway uses these to generate MCP tool descriptions
- Upload the schema: `aws s3api put-object --bucket <bucket> --key <key> --body <schema-file>`
- Refer to the latest AWS documentation on AgentCore Gateway OpenAPI schema requirements
### 2. Create Credential Provider (if API key or OAuth)
**Constraints:**
- You MUST create the credential provider BEFORE creating the gateway target — this ordering is mandatory
- Creating a target without credentials results in a "credential provider not found" error
**For API key authentication:**
- You MUST NOT pass the API key as a literal value on the command line — shell history exposes it
- You MUST ask the user to set the key as an environment variable: `export API_KEY=<their-key>`
- Create the credential provider: `aws bedrock-agentcore-control create-api-key-credential-provider --name <name> --api-key "$API_KEY"` — the service encrypts and stores the key in Secrets Manager internally (response includes `apiKeySecretArn`). Do NOT manually create a Secrets Manager secret; the service manages this.
- For key rotation: `aws bedrock-agentcore-control update-api-key-credential-provider --name <name> --api-key "$NEW_API_KEY"` — do NOT call `secretsmanager rotate-secret` directly on the service-managed secret
**For OAuth authentication:**
- The client secret is passed via the `create-oauth2-credential-provider` API call — the service encrypts and stores it in Secrets Manager automatically (response includes `clientSecretArn`). Do NOT manually create a Secrets Manager secret.
- You MUST NOT hardcode client secrets in agent code or configuration
- Configure token endpoint, client ID, client secret, and scopes
- Create the OAuth2 credential provider: `aws bedrock-agentcore-control create-oauth2-credential-provider --name <name> --credential-provider-vendor <vendor> --oauth2-provider-config-input '...'`
- Refer to the latest AWS documentation on AgentCore Gateway OAuth configuration options
**For Lambda/IAM authentication:**
- No credential provider needed — skip to Step 3
- The Gateway uses IAM role-based authentication to invoke the Lambda
- The Lambda MUST have a resource-based policy allowing the Gateway service role to invoke it, with `aws:SourceAccount` and `aws:SourceArn` conditions to prevent confused deputy. Refer to the latest AWS documentation on AgentCore Gateway permissions for current policy patterns.
### 3. Create Gateway Target
**Constraints:**
- Create the target: `aws bedrock-agentcore-control create-gateway-target --gateway-identifier <gateway-id> --name <name> --target-configuration '...' --credential-provider-configurations '...'`
- You MUST link the OpenAPI schema S3 URI from Step 1
- If using API key or OAuth: You MUST link the credential provider ARN from Step 2
- If using Lambda: You MUST specify the Lambda ARN and configure IAM role with `lambda:InvokeFunction` scoped to the specific Lambda ARN — avoid `Resource: "*"`
- You MUST NOT create the target before the credential provider exists (for API key/OAuth)
### 4. Verify Target Status
**Constraints:**
- Poll target status: `aws bedrock-agentcore-control get-gateway-target --gateway-identifier <gateway-id> --target-id <target-id>`
- Wait for status `ACTIVE` before using the target
- If status is `FAILED`:
- Check IAM permissions
- Verify OpenAPI schema is valid
- Verify credential provider exists and is accessible
- Check CloudTrail for detailed error messages
- If status is stuck in `CREATING` for >10 minutes:
- Contact AWS Support with the gateway-id and target-id for investigation
- Refer to the latest AWS documentation or support channels for known issues
### 5. Test Connectivity
**Constraints:**
- You MUST test the gateway target with a sample request before using in production
- Verify the MCP tools generated from the OpenAPI schema match expectations
- You SHOULD report the list of generated MCP tools to the user
## Security Considerations
- **Encryption:** S3 encrypts objects at rest by default (SSE-S3). For sensitive schemas, use SSE-KMS with a customer managed key. Target endpoints MUST use HTTPS — Gateway rejects HTTP endpoints.
- **Least privilege:** Scope IAM roles to specific resource ARNs — the Gateway service role should only access the specific S3 bucket, Secrets Manager secret, and Lambda function needed. Avoid `Resource: "*"`.
- **Sensitive data in logs:** API keys and OAuth tokens may appear in CloudTrail logs. Enable CloudTrail log encryption with KMS. Do NOT log credential values in agent output.
- **Monitoring:** Enable CloudWatch alarms for gateway target errors (5xx rates, latency). Enable CloudTrail for audit logging of all `bedrock-agentcore-control` API calls.
- **TLS:** All target endpoints must use TLS 1.2+. Use ACM certificates for custom domains.
- Refer to the latest AWS documentation on Bedrock security best practices.

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