How to Build a customer support Agent Using AutoGen in Python for pension funds

A customer support agent for a pension fund answers member questions, triages requests, and drafts compliant responses without exposing sensitive data or inventing policy details. It matters because pension support is high-trust work: members ask about contributions, withdrawals, retirement dates, beneficiaries, and complaints, and every answer needs to be accurate, auditable, and aligned with local regulations.

Architecture

• •Member-facing assistant
  • •Handles FAQs like contribution status, statement requests, retirement eligibility, and contact updates.
• •Policy retrieval layer
  • •Pulls answers from approved pension rules, product docs, and internal SOPs.
• •Agent orchestration
  • •Uses AssistantAgent plus one or more tool-capable agents to route questions and draft responses.
• •Compliance guardrail
  • •Blocks unsupported advice, forces escalation for regulated topics, and keeps responses within approved language.
• •Audit logging
  • •Stores prompts, tool calls, retrieved documents, and final answers for review.
• •Human handoff
  • •Escalates complaints, exceptions, benefit disputes, and identity-sensitive requests to a case worker.

Implementation

1. •Install AutoGen and define your agents

For a support workflow like this, I use one assistant agent for conversation control and a user proxy for execution/testing. The assistant should not freewheel; it should answer only from policy context or escalate.

from autogen import AssistantAgent, UserProxyAgent

llm_config = {
    "model": "gpt-4o-mini",
    "api_key": "YOUR_OPENAI_API_KEY",
    "temperature": 0,
}

support_agent = AssistantAgent(
    name="pension_support_agent",
    llm_config=llm_config,
    system_message=(
        "You are a pension fund customer support agent. "
        "Answer only using provided policy context. "
        "Do not provide financial advice. "
        "If the request involves complaints, withdrawals exceptions, beneficiary disputes, "
        "or identity verification issues, escalate to a human agent."
    ),
)

user_proxy = UserProxyAgent(
    name="member_proxy",
    human_input_mode="NEVER",
)

2. •Add a policy retrieval function as a tool

Pension support lives or dies on retrieval quality. In production you would query a controlled document store or vector index; here I’m keeping it simple with an allowlisted policy lookup that still shows the AutoGen pattern.

POLICY_DB = {
    "retirement_age": (
        "Members may request retirement benefit estimates from age 55 subject to plan rules."
    ),
    "beneficiary_update": (
        "Beneficiary changes require identity verification before submission."
    ),
    "contribution_query": (
        "Contribution records are available in monthly statements after payroll reconciliation."
    ),
}

def lookup_policy(topic: str) -> str:
    topic = topic.lower().strip()
    return POLICY_DB.get(topic, "No approved policy found for this topic.")

3. •Register the tool and run the agent chat

This is the core pattern: let the agent decide when to call the tool using register_for_llm, then return only approved content. Keep temperature at zero so support responses stay deterministic.

from autogen import register_function

@support_agent.register_for_llm(description="Look up approved pension fund policy by topic.")
def get_policy(topic: str) -> str:
    return lookup_policy(topic)

# Optional: let the user proxy execute registered functions if needed
register_function(
    get_policy,
    caller=support_agent,
    executor=user_proxy,
)

message = (
    "Can I update my beneficiary details by email? "
    "Also tell me when I can retire."
)

chat_result = user_proxy.initiate_chat(
    support_agent,
    message=message,
)

print(chat_result)

4. •Add an escalation rule for regulated cases

For pension funds, you need hard stops. If the question involves advice on withdrawals, tax treatment, divorce orders, death claims, or disputed records, route to a human queue instead of generating an answer.

def requires_escalation(text: str) -> bool:
    triggers = [
        "withdrawal",
        "tax",
        "divorce order",
        "death claim",
        "complaint",
        "beneficiary dispute",
        "identity verification",
    ]
    text = text.lower()
    return any(t in text for t in triggers)

incoming_query = "I want to withdraw my pension early because I need cash."

if requires_escalation(incoming_query):
    print("ESCALATE_TO_HUMAN")
else:
    user_proxy.initiate_chat(support_agent, message=incoming_query)

Production Considerations

• •Data residency
  • •Keep member data in-region. If your pension fund operates under local residency rules, pin model endpoints and document stores to approved jurisdictions.
• •Auditability
  • •Log every prompt, retrieved policy snippet, tool call result, and final answer with timestamps and case IDs.
• •Guardrails
  • •Block unsupported advice on investment decisions or withdrawals. Use allowlisted topics and escalation triggers rather than trusting free-form generation.
• •Monitoring
  • •Track escalation rate, hallucination reports from QA reviews, response latency by intent type, and retrieval hit rate against approved policies.

Common Pitfalls

1. •

   Letting the model answer from memory

   • •Pension policies change often. If you skip retrieval grounding, you will ship stale or incorrect guidance.
   • •Fix it by forcing answers through approved documents or structured policy lookups.
2. •

   Treating all questions as support questions

   • •Some requests are actually regulated actions: beneficiary changes, benefit disputes, death claims.
   • •Fix it with intent classification plus explicit escalation paths.
3. •

   Ignoring compliance logging

   • •If you cannot reconstruct why the agent said something, audits become painful fast.
   • •Fix it by persisting conversation state, tool outputs, document IDs used in the answer chain.
4. •

   Using relaxed generation settings

   • •High temperature makes support answers inconsistent.
   • •Fix it by setting temperature=0 and keeping system prompts narrow and enforceable.

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.