What is function calling in AI Agents? A Guide for product managers in insurance

Function calling is the ability for an AI agent to ask a system to run a specific action, like fetching a policy record, creating a claim, or checking eligibility. In practice, it means the model does not just generate text; it can choose from approved tools and return structured data that your product can use.

For insurance product managers, this is the difference between an AI that “talks about” a claim and an AI that actually helps move the claim forward.

How It Works

Think of function calling like a front-desk receptionist with a phone directory.

The receptionist does not repair the boiler, approve the claim, or update the policy system. What they do is identify the right department, call it with the right request, and pass back the result. Function calling works the same way: the AI agent decides which function to use, sends it structured inputs, and then uses the response to continue the conversation.

Here’s the flow in plain English:

• •A user asks something like: “What’s the status of my motor claim?”
• •The AI recognizes that this is not just a chat question.
• •It selects a function such as get_claim_status(claim_id).
• •Your system runs that function against your claims platform.
• •The function returns structured data like status, last updated time, and next action.
• •The AI turns that into a readable answer for the user.

For engineers, the key point is that the model is not directly touching your database or core systems. It is producing a tool request in a defined schema, usually JSON, and your application controls execution. That matters because you can validate inputs, enforce permissions, log every call, and keep business logic outside the model.

A simple example of what the model might emit:

{
  "name": "get_policy_details",
  "arguments": {
    "policy_number": "POL123456"
  }
}

Your backend receives that request, checks whether the user is allowed to see that policy, calls the policy admin system, then returns something like:

{
  "policy_number": "POL123456",
  "status": "Active",
  "renewal_date": "2026-03-14",
  "product": "Homeowners"
}

The model then explains it to the customer or agent in natural language.

Why It Matters

Product managers in insurance should care because function calling turns AI from a content generator into an operational interface.

• •

  It reduces dead-end conversations

  Without function calling, users ask for actions and get generic answers. With it, the agent can retrieve real policy data, check claim status, or trigger workflows.

• •

  It improves process automation without replacing core systems

  You do not need to rebuild your claims platform or policy admin stack. You expose approved functions around existing systems and let the agent orchestrate them.

• •

  It creates cleaner product boundaries

  The model handles language. Your services handle business rules. That separation makes it easier to test, govern, and scale.

• •

  It supports better customer and agent experiences

  A broker portal assistant can answer “Is this risk eligible?” faster when it can call underwriting rules instead of making users hunt through screens.

For insurance specifically, this is useful anywhere there is structured data plus repeatable workflow:

• •Quote generation
• •Policy lookup
• •FNOL intake
• •Claims triage
• •Document retrieval
• •Renewal reminders

The product value is not “AI chat.” The value is shorter handling time, fewer handoffs, and better completion rates on real tasks.

Real Example

Let’s say you are building an AI assistant for first notice of loss in home insurance.

A customer says: “I had water damage last night. Can I start a claim?”

The agent should not improvise. It should gather facts and call functions in sequence:

1. •verify_policy(policy_number)
2. •check_coverage(policy_number, loss_type)
3. •create_claim(policy_number, incident_details)
4. •schedule_adjuster(claim_id) if needed

A realistic flow looks like this:

{
  "name": "check_coverage",
  "arguments": {
    "policy_number": "HO987654",
    "loss_type": "water_damage",
    "incident_date": "2026-04-20"
  }
}

Your coverage service returns:

{
  "covered": true,
  "deductible": 500,
  "notes": "Sudden accidental discharge covered; gradual leakage excluded."
}

Then the assistant can respond:

Your policy covers sudden water damage events. Your deductible is $500. I can now open a claim and collect incident details.

Next it calls create_claim with structured inputs from the conversation:

{
  "name": "create_claim",
  "arguments": {
    "policy_number": "HO987654",
    "loss_type": "water_damage",
    "incident_date": "2026-04-20",
    "description": "Kitchen ceiling leak after pipe burst overnight"
  }
}

That returns a claim number and next steps. The assistant can then tell the customer exactly what happens next instead of sending them to another form or queue.

For product managers, this pattern matters because you can measure outcomes clearly:

• •More completed FNOL submissions
• •Lower average handling time
• •Fewer abandoned sessions
• •Better containment before human handoff

Related Concepts

If you are evaluating function calling for an insurance agent product, these adjacent topics matter:

• •Tool use / tool calling — broader term for letting models invoke external actions.
• •Structured outputs — forcing responses into JSON or schema-defined formats.
• •Agent orchestration — how multiple steps are chained across tools and prompts.
• •RAG (retrieval augmented generation) — pulling documents or knowledge into responses without executing actions.
• •Workflow automation — deterministic process steps that may sit behind or alongside AI agents.

Function calling is not magic. It is simply a controlled way for an AI agent to interact with real systems.

For insurance products, that control is exactly why it matters: you get conversational UX without giving up governance, auditability, or business rules.

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.