This example shows how to build a healthcare support workflow with Agents SDK using both standard agents and a sandbox agent. The scenario is intentionally synthetic and generic: a patient asks a billing or coverage question, the workflow checks local records, inspects policy documents in an isolated sandbox workspace, writes support artifacts, and optionally routes one ambiguous case to a human reviewer.

What this example demonstrates

Standard agent orchestration with a top-level support orchestrator and a benefits subagent.
Sandbox agents with a mounted workspace, shell commands, a generated output folder, and runtime-selected sandbox config.
Sandbox capabilities including Shell, Filesystem, and lazy-loaded Skills.
Human-in-the-loop approvals using an approval-gated queue-routing tool.
Persistent memory with SQLiteSession, shared across scenario runs.
Structured outputs for each specialist agent and the final case resolution.
Tracing so you can inspect every model call and tool call in the OpenAI trace viewer.
CLI-first workflow that can be run scenario by scenario from the repository checkout.

Architecture

The workflow has two execution modes working together:

A standard orchestrator agent runs in the normal Agents SDK loop, calls the benefits subagent first, then calls a sandbox agent tool, and decides whether to request a human handoff.
A sandbox policy agent runs behind agents.sandbox, reads the mounted case files and policy documents, uses shell commands plus a lazily loaded skill, writes markdown artifacts into output/, and returns a structured policy summary.

The local fixture data lives in data/scenarios/*.json and data/fixtures/*.json. The sandbox policy library lives in policies/*.md. Generated artifacts are copied to .cache/healthcare_support/output/<scenario_id>/.

Scenarios

The built-in scenarios increase in complexity:

eligibility_verification_basic checks a straightforward benefits question.
referral_status_check adds a referral lookup.
blue_cross_pt_benefits shows a follow-up turn that benefits from the shared SQLite memory.
prior_auth_confusion_ct focuses on prior-authorization and intake-routing confusion.
billing_coverage_clarification combines benefits lookup with sandbox policy search and document generation.
messy_ambiguous_knee_case triggers the human approval flow before queueing a handoff.

Run the CLI demo

From the repository root:

uv run python examples/sandbox/healthcare_support/main.py

Useful options:

uv run python examples/sandbox/healthcare_support/main.py --list-scenarios
uv run python examples/sandbox/healthcare_support/main.py --scenario blue_cross_pt_benefits
uv run python examples/sandbox/healthcare_support/main.py --scenario messy_ambiguous_knee_case
uv run python examples/sandbox/healthcare_support/main.py --reset-memory

For unattended runs, set EXAMPLES_INTERACTIVE_MODE=auto to auto-answer prompts:

EXAMPLES_INTERACTIVE_MODE=auto uv run python examples/sandbox/healthcare_support/main.py --scenario messy_ambiguous_knee_case

Files to read first

main.py runs the standalone CLI demo.
workflow.py contains the shared workflow execution logic, sandbox setup, artifact copying, tracing, and approval resume loop.
support_agents.py defines the orchestrator, benefits subagent, sandbox policy agent, and memory recap agent.
tools.py defines the local lookup tools and the approval-gated human handoff tool.
skills/prior-auth-packet-builder/SKILL.md is the sandbox skill loaded at runtime.

Notes

This is a demo workflow, not a production healthcare system.
All patient, payer, and policy data in this example is synthetic.
The example loads environment defaults from the repository-root .env file and from this demo's optional local .env file.