wehub-resource-sync
db620d33df
2.4 KiB
Skill Tool Approval — Human-in-the-Loop for Skill Tools
This sample demonstrates the manual human-in-the-loop approval pattern for
skill tools. Every tool exposed by SkillsProvider (load_skill,
read_skill_resource, and run_skill_script) requires host approval by
default, so the agent pauses and returns approval requests that your
application approves or rejects.
How It Works
By default, skill tools require approval. The agent pauses before running any of them and returns approval requests instead:
- The agent tries to call a skill tool (e.g.
load_skillorrun_skill_script) — execution is paused result.user_input_requestscontains approval request(s) with function name and arguments- The application inspects each request and decides to approve or reject
request.to_function_approval_response(approved=True|False)creates the response- The response is sent back via
agent.run(approval_response, session=session) - If approved, the tool runs; if rejected, the agent receives an error
Key Components
- Approval-by-default — All skill tools require host approval; no extra configuration is needed
result.user_input_requests— Contains pending approval requests afteragent.run()request.to_function_approval_response()— Creates an approval or rejection response
To approve skill tools automatically instead of prompting for each one, use
ToolApprovalMiddleware with one of the static auto-approval rules — see the
Skills Auto-Approval Sample.
Running the Sample
Prerequisites
- An Azure AI Foundry project with a deployed model (e.g.
gpt-4o-mini)
Environment Variables
Set the required environment variables in a .env file (see python/.env.example):
FOUNDRY_PROJECT_ENDPOINT: Your Azure AI Foundry project endpointFOUNDRY_MODEL: The name of your model deployment (defaults togpt-4o-mini)
Authentication
This sample uses AzureCliCredential for authentication. Run az login in your terminal before running the sample.
Run
cd python
uv run samples/02-agents/skills/script_approval/script_approval.py