Healthcare support

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:

1. 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.
2. 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.