How to Build a policy Q&A Agent Using AutoGen in Python for healthcare

A policy Q&A agent for healthcare answers questions about benefits, coverage rules, prior authorization, claims handling, HIPAA-related workflows, and internal SOPs. The point is not just speed; it’s consistency, auditability, and reducing the number of policy questions that bounce between support, operations, and compliance teams.

Architecture

• •

  User interface or API layer

  • •Receives clinician, member-services, or operations questions.
  • •Passes along metadata like tenant, jurisdiction, and requestor role.
• •

  Policy retrieval layer

  • •Pulls from approved policy documents, plan summaries, SOPs, and regulatory references.
  • •Uses a controlled corpus only; no open web by default.
• •

  AutoGen assistant agent

  • •Drafts answers grounded in retrieved policy text.
  • •Refuses to answer when the policy source is missing or ambiguous.
• •

  Compliance / guardrail agent

  • •Checks for PHI leakage, unsupported medical advice, and disallowed claims.
  • •Forces escalation when confidence is low or the request crosses policy boundaries.
• •

  Audit logger

  • •Stores question, retrieved sources, final answer, timestamps, and agent decisions.
  • •Supports post-incident review and compliance audits.
• •

  Human escalation path

  • •Routes edge cases to a compliance analyst or licensed reviewer.
  • •Required for ambiguous coverage decisions and anything involving patient-specific clinical judgment.

Implementation

1. Install AutoGen and define your policy corpus

Use the current AutoGen package and keep your source material local. In healthcare, your retrieval set should be curated PDFs, policy pages, and internal docs that are approved for use.

pip install pyautogen

from autogen import AssistantAgent
import os

POLICY_SNIPPETS = [
    "Prior authorization is required for elective MRI procedures unless emergent.",
    "Telehealth behavioral health visits are covered in-network at parity with in-person visits.",
    "Claims older than 180 days require manual review before payment adjudication."
]

config_list = [
    {
        "model": os.environ["OPENAI_MODEL"],
        "api_key": os.environ["OPENAI_API_KEY"],
    }
]

2. Create a policy-answering assistant with strict instructions

The assistant should answer only from provided policy text. If the evidence is missing, it should say so and escalate instead of inventing a rule.

policy_agent = AssistantAgent(
    name="policy_qna_agent",
    llm_config={"config_list": config_list},
    system_message=(
        "You answer healthcare policy questions using only provided policy snippets. "
        "Do not provide medical advice. Do not infer coverage rules not explicitly stated. "
        "If the answer is unclear or missing from the snippets, say 'Escalate to compliance.' "
        "Always cite which snippet supports the answer."
    ),
)

3. Wrap retrieval + answer generation in a single function

This pattern keeps the agent grounded. In production you would replace POLICY_SNIPPETS with vector search over approved documents filtered by tenant and region.

def build_context(question: str) -> str:
    # Replace with real retrieval: vector DB + jurisdiction filters + document ACLs.
    relevant = []
    q = question.lower()

    if "mri" in q or "prior authorization" in q:
        relevant.append(POLICY_SNIPPETS[0])
    if "telehealth" in q or "behavioral health" in q:
        relevant.append(POLICY_SNIPPETS[1])
    if "claims" in q or "180 days" in q:
        relevant.append(POLICY_SNIPPETS[2])

    if not relevant:
        return "No matching approved policy snippet found."

    return "\n".join(f"- {item}" for item in relevant)


def answer_policy_question(question: str) -> str:
    context = build_context(question)

    prompt = f"""
Question: {question}

Approved policy context:
{context}

Instructions:
- Answer only from the approved context.
- Cite the exact bullet(s) used.
- If context is insufficient, say: Escalate to compliance.
"""

    result = policy_agent.generate_reply(messages=[{"role": "user", "content": prompt}])
    return result if isinstance(result, str) else result.get("content", "")

4. Add a second pass for compliance review

For healthcare workflows, one agent answering is not enough. A reviewer agent can check whether the draft leaks PHI or makes unsupported statements.

compliance_agent = AssistantAgent(
    name="compliance_reviewer",
    llm_config={"config_list": config_list},
    system_message=(
        "Review answers for healthcare compliance risks. "
        "Flag PHI exposure, unsupported coverage claims, medical advice, or missing citations. "
        "Return either APPROVED or REJECTED with a short reason."
    ),
)

def review_answer(question: str, draft_answer: str) -> str:
    review_prompt = f"""
Question: {question}
Draft answer: {draft_answer}

Check for:
- HIPAA/PHI issues
- Unsupported policy claims
- Missing citations
- Need for escalation
"""
    result = compliance_agent.generate_reply(messages=[{"role": "user", "content": review_prompt}])
    return result if isinstance(result, str) else result.get("content", "")


if __name__ == "__main__":
    q = "Does telehealth behavioral health require prior authorization?"
    draft = answer_policy_question(q)
    review = review_answer(q, draft)

    print("DRAFT:", draft)
    print("REVIEW:", review)

Production Considerations

• •

  Data residency

  • •Keep document stores and logs inside the required region.
  • •If you operate across states or countries, filter retrieval by tenant and jurisdiction before any model call.
• •

  Audit trail

  • •Store question text, retrieved snippets, model version, prompt version, final answer, and reviewer output.
  • •For regulated environments, make logs immutable and searchable by case ID.
• •

  Guardrails

  • •Block patient-specific clinical recommendations unless a licensed workflow explicitly allows them.
  • •Redact PHI before sending prompts to the model when possible.
  • •Force escalation on ambiguous coverage language like “may be covered” or “subject to medical necessity.”
• •

  Monitoring

  • •Track refusal rate, escalation rate, citation coverage, hallucination reports, and turnaround time.
  • •Review failures by policy domain: pharmacy benefits behave differently from utilization management or claims rules.

Common Pitfalls

1. •

   Using general-purpose retrieval without access control

   • •If every user can query every document chunk, you will leak restricted policies across tenants or regions.
   • •Fix it by filtering retrieval on role-based access control before building context.
2. •

   Letting the model answer without citations

   • •In healthcare ops this becomes untraceable fast.
   • •Fix it by requiring exact source bullets in every response and rejecting uncited answers at runtime.
3. •

   Mixing policy Q&A with clinical advice

   • •A member asking “Should I get this procedure?” is not a coverage question; it’s clinical guidance.
   • •Fix it by classifying intent first and routing clinical questions to an approved care pathway or human reviewer.
4. •

   Ignoring state-specific rules and payer variation

   • •A rule that applies to one plan line may not apply to another product or jurisdiction.
   • •Fix it by tagging every snippet with plan ID, state/country code, effective date, and line of business before retrieval.

Keep learning

• •The complete AI Agents Roadmap — my full 8-step breakdown
• •Free: The AI Agent Starter Kit — PDF checklist + starter code
• •Work with me — I build AI for banks and insurance companies

By Cyprian Aarons, AI Consultant at Topiax.