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

A policy Q&A agent for payments answers questions like “Can we refund this card-present transaction?” or “What’s the chargeback window for this region?” by grounding responses in your internal policy docs and routing uncertain cases to a human. It matters because payment ops teams lose time on repetitive policy lookups, and bad answers create compliance risk, customer friction, and avoidable losses.

Architecture

• •

  User interface layer

  • •Slack, web app, internal ops console, or ticketing integration.
  • •Keep it thin. The agent should not own business logic.
• •

  Policy retrieval layer

  • •Pulls from PCI, refund, chargeback, dispute, KYC/AML, and regional payment policy docs.
  • •Use chunked documents with metadata like region, payment_method, effective_date, and policy_owner.
• •

  AutoGen assistant agent

  • •Uses AssistantAgent to reason over retrieved policy snippets.
  • •Produces short answers with citations and escalation signals.
• •

  Tooling / function layer

  • •Functions for fetching policies, checking case context, and creating escalation tickets.
  • •In AutoGen, expose these as callable tools through the agent config.
• •

  Human review path

  • •A UserProxyAgent or downstream workflow handles ambiguous or high-risk questions.
  • •Mandatory for edge cases involving disputes, sanctions screening, fraud holds, or legal interpretation.
• •

  Audit and observability

  • •Log prompt inputs, retrieved sources, model outputs, tool calls, and final decisions.
  • •Required for payments compliance reviews and incident analysis.

Implementation

1) Install AutoGen and define your policy lookup tools

For a payments agent, don’t let the model browse arbitrary text. Give it a controlled retrieval function that filters by policy domain and region.

from typing import List, Dict
from autogen import AssistantAgent, UserProxyAgent

POLICIES = [
    {
        "id": "refund_card_present_us",
        "domain": "refunds",
        "region": "US",
        "text": "Card-present refunds must be issued to the original card within 30 days unless fraud is suspected.",
    },
    {
        "id": "chargeback_eu_sepa",
        "domain": "chargebacks",
        "region": "EU",
        "text": "SEPA dispute requests must be reviewed within 5 business days and escalated if evidence is incomplete.",
    },
]

def search_policies(query: str) -> List[Dict]:
    q = query.lower()
    results = []
    for p in POLICIES:
        if p["domain"] in q or p["region"].lower() in q:
            results.append(p)
    return results[:3]

2) Create an AssistantAgent with a strict system message

Use the assistant to answer only from provided policy context. For payments workflows, force concise answers plus an escalation recommendation when confidence is low.

llm_config = {
    "model": "gpt-4o-mini",
    "api_key": "<YOUR_OPENAI_API_KEY>",
}

assistant = AssistantAgent(
    name="payments_policy_assistant",
    llm_config=llm_config,
    system_message=(
        "You are a payments policy Q&A agent. "
        "Answer only from supplied policy context. "
        "If the context is insufficient or the question touches legal/compliance ambiguity, say 'ESCALATE'. "
        "Always mention the applicable region if present. "
        "Never invent policy."
    ),
)

3) Wire in a UserProxyAgent to execute the lookup and run the chat loop

This pattern keeps retrieval deterministic while letting AutoGen handle the conversation state. In production you’d replace the toy lookup with Elasticsearch, OpenSearch, pgvector, or your document service.

user_proxy = UserProxyAgent(
    name="payments_ops_user",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=1,
)

question = "What is our refund policy for card-present transactions in the US?"

policy_hits = search_policies(question)
context = "\n".join([f"- {p['id']}: {p['text']}" for p in policy_hits]) or "- No matching policy found."

prompt = f"""
Question: {question}

Policy context:
{context}

Instructions:
- Answer in 3 sentences max.
- Cite the relevant policy id(s).
- If no matching policy exists or the answer is ambiguous, respond with ESCALATE.
"""

result = user_proxy.initiate_chat(
    assistant,
    message=prompt,
)

print(result.summary)

4) Add a hard escalation rule for risky payment topics

For payments you need deterministic guardrails around disputes, fraud holds, sanctions screening, PCI scope questions, and data residency. The model should not decide those alone.

RISKY_TOPICS = ["pci", "sanctions", "aml", "fraud hold", "legal", "regulatory", "data residency"]

def needs_escalation(text: str) -> bool:
    t = text.lower()
    return any(topic in t for topic in RISKY_TOPICS)

incoming_question = "Can we store full PANs in our analytics warehouse?"
if needs_escalation(incoming_question):
    print("ESCALATE: requires compliance review")
else:
    print("Proceed to agent")

Production Considerations

• •

  Deployment

  • •Keep retrieval services inside your approved payment data boundary.
  • •If policies contain regional restrictions or customer data references, pin storage and inference to approved regions for data residency.
• •

  Monitoring

  • •Track answer accuracy by policy domain: refunds, disputes, chargebacks, PCI.
  • •Log source document IDs and model output so audit teams can reconstruct why a response was given.
• •

  Guardrails

  • •Block direct answers when the question asks for legal interpretation or anything that could affect regulated operations.
  • •Require escalation on missing metadata like region or payment rail; “unknown region” is not safe enough for production.
• •

  Access control

  • •Restrict who can query sensitive payment policies.
  • •Some docs may include internal fraud thresholds or partner-specific settlement rules that should never be exposed broadly.

Common Pitfalls

1. •

   Letting the model answer without grounded context

   • •This turns your agent into a confident guesser.
   • •Fix it by requiring retrieved policy snippets in every prompt and forcing ESCALATE when retrieval returns nothing relevant.
2. •

   Mixing global policies with regional exceptions

   • •Payments rules often differ by country, scheme, issuer network, or product line.
   • •Fix it by tagging every document with metadata like region, rail, product, and filtering before generation.
3. •

   Skipping audit logs

   • •In payments you will eventually need to explain why an answer was given to risk, compliance, or operations.
   • •Fix it by logging user question, retrieved policies, final response, timestamp, model version, and escalation outcome.
4. •

   Using one agent for everything

   • •Refunds logic is not chargeback logic. PCI questions are not support macros.
   • •Fix it by splitting domains or at least routing queries into specialized policy buckets before calling AssistantAgent.

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.