How to Build a customer support Agent Using AutoGen in TypeScript for insurance

A customer support agent for insurance handles policy questions, claim status checks, document requests, and triage to a human adjuster when the request crosses a compliance or authority boundary. It matters because insurance support is high-volume, regulated, and full of repetitive but sensitive interactions where bad answers create risk, not just bad UX.

Architecture

• •Chat user interface
  • •Web app, mobile app, or contact-center console where the policyholder asks questions.
• •AutoGen assistant agent
  • •The main responder that classifies intent, answers from approved context, and decides when to escalate.
• •Tool layer
  • •Functions for claim lookup, policy retrieval, document generation, and ticket creation.
• •Human handoff path
  • •A fallback route to a licensed agent for disputes, coverage interpretation, complaints, and claims decisions.
• •Audit and logging service
  • •Stores prompts, tool calls, responses, and escalation reasons for compliance review.
• •Policy/knowledge source
  • •Approved insurance knowledge base, product wording, claims FAQ, and jurisdiction-specific rules.

Implementation

1) Install AutoGen and set up your runtime

For TypeScript, use the AutoGen package that exposes AssistantAgent, UserProxyAgent, and tool registration. Keep secrets out of code and route model access through environment variables.

npm install @autogenai/autogen openai zod

import { AssistantAgent } from "@autogenai/autogen";

const supportAgent = new AssistantAgent({
  name: "insurance_support_agent",
  systemMessage: `
You are an insurance customer support agent.
Answer only from approved policy knowledge or tools.
If the user asks for coverage interpretation, claim denial review,
legal advice, or anything outside approved scope, escalate to a human.
Always mention when you are unsure.
`.trim(),
  llmConfig: {
    model: "gpt-4o-mini",
    apiKey: process.env.OPENAI_API_KEY!,
    temperature: 0.2,
  },
});

2) Add real tools for policy and claim lookups

Insurance support becomes useful only when the agent can fetch structured data. Register tools for common workflows like claim status and policy summary.

import { z } from "zod";
import { UserProxyAgent } from "@autogenai/autogen";

type ClaimRecord = {
  claimId: string;
  status: "open" | "pending_docs" | "closed";
  lastUpdated: string;
};

async function getClaimStatus(claimId: string): Promise<ClaimRecord> {
  // Replace with your claims API call
  return {
    claimId,
    status: "pending_docs",
    lastUpdated: new Date().toISOString(),
  };
}

async function getPolicySummary(policyNumber: string) {
  // Replace with your policy admin system call
  return {
    policyNumber,
    product: "Auto Comprehensive",
    effectiveDate: "2025-01-01",
    deductible: "$500",
  };
}

supportAgent.registerFunction(
  {
    name: "get_claim_status",
    description: "Fetch the current status of an insurance claim.",
    parameters: z.object({
      claimId: z.string().min(1),
    }),
  },
  async ({ claimId }) => JSON.stringify(await getClaimStatus(claimId))
);

supportAgent.registerFunction(
  {
    name: "get_policy_summary",
    description: "Fetch a customer's active policy summary.",
    parameters: z.object({
      policyNumber: z.string().min(1),
    }),
  },
  async ({ policyNumber }) => JSON.stringify(await getPolicySummary(policyNumber))
);

3) Orchestrate the conversation with escalation rules

Use UserProxyAgent to represent the customer side or your app gateway. Then run the assistant in a controlled loop and inspect outputs before returning them to production channels.

const userProxy = new UserProxyAgent({
  name: "customer_proxy",
});

async function handleCustomerMessage(message: string) {
  const result = await userProxy.initiateChat(supportAgent, message);

  const responseText =
    typeof result === "string"
      ? result
      : JSON.stringify(result);

  return responseText;
}

async function demo() {
  const reply = await handleCustomerMessage(
    "What is the status of claim CLM-10482?"
  );

  console.log(reply);
}

demo();

That pattern is enough for a first pass, but in production you should add explicit guardrails before sending anything back to the customer. A simple rule engine can catch sensitive intents like complaints about denied claims or requests for legal interpretation.

function requiresHumanHandoff(message: string): boolean {
  const riskyPatterns = [
    /deny|denied|coverage|covered/i,
    /lawsuit|legal advice/i,
    /appeal/i,
    /complaint/i,
    /bad faith/i,
  ];

  return riskyPatterns.some((pattern) => pattern.test(message));
}

4) Return safe responses and log every decision

For insurance workflows you need traceability. Log the user message, model output, tool calls, escalation reason, timestamp, and tenant/jurisdiction metadata.

type AuditEvent = {
  requestId: string;
  userMessage: string;
  agentResponse?: string;
  escalatedToHuman: boolean;
};

async function supportWorkflow(requestId: string, message: string) {
  if (requiresHumanHandoff(message)) {
    const event: AuditEvent = {
      requestId,
      userMessage: message,
      escalatedToHuman: true,
      agentResponse:
        "I’m routing this to a licensed specialist who can review coverage or appeal details.",
    };
    console.log(JSON.stringify(event));
    return event.agentResponse;
    

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.