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

Function calling is a way for an AI model to request that your application run a specific function, instead of trying to answer everything in plain text. In AI agents, function calling lets the model choose structured actions like get_policy_details, check_claim_status, or schedule_callback so the system can do real work.

How It Works

Think of function calling like a claims handler with a strict playbook.

The AI model is the person at the desk. It understands the customer’s request, decides what needs to happen next, and then fills out a form for the back-office system to execute. The model does not directly touch your policy admin system or claims engine; it asks your application to call a function with specific inputs.

A simple flow looks like this:

• •Customer asks: “Is my home claim approved?”
• •The model sees that it needs claim data, not a free-form answer.
• •It returns something like:
  • •function: get_claim_status
  • •arguments: { "claim_id": "CLM-48291" }
• •Your backend runs that function against the claims API.
• •The result comes back to the model.
• •The model turns that structured result into a customer-facing response.

This matters because the agent stays grounded in your systems of record. It is not guessing. It is orchestrating.

For insurance teams, the analogy is an operations desk with controlled handoffs:

The key technical idea is that the model outputs structured intent, usually JSON-like arguments, rather than prose. That makes it easy for your application layer to validate inputs, enforce permissions, log actions, and route requests safely.

A good implementation also separates concerns:

• •The model decides what action is needed.
• •Your code decides whether it is allowed.
• •Your systems decide what actually happened.

That separation is why function calling fits regulated environments. You can keep humans, audit trails, and business rules in control while still using the model for language understanding and workflow orchestration.

Why It Matters

CTOs in insurance should care because function calling turns an LLM from a chat interface into an operational component.

• •

  It reduces hallucination risk

  • •The model can query policy data or claims status instead of inventing answers from memory.
  • •That is critical when customers ask about coverage limits, exclusions, or payment status.
• •

  It enables real workflows

  • •An agent can collect missing information, create tasks, update CRM records, or trigger document retrieval.
  • •This is how you move from “chatbot” to actual service automation.
• •

  It improves governance

  • •Every tool call can be logged, validated, approved, or blocked.
  • •That gives compliance teams something concrete to audit.
• •

  It supports better customer experience

  • •Customers get faster resolution on repetitive requests like claim updates, policy changes, and billing questions.
  • •Human agents only handle exceptions and high-value cases.

For insurance specifically, this pattern works well in areas where language is messy but operations are structured:

• •FNOL intake
• •Claims status checks
• •Policy verification
• •Document collection
• •Renewal reminders
• •Underwriting triage

The CTO question is not “Can the model talk?” It is “Can the model safely trigger business actions inside our control plane?”

Real Example

Let’s say a policyholder messages your assistant:

“I had water damage last week. Can you tell me if my claim has been assigned and who my adjuster is?”

A basic chatbot might respond with generic sympathy and ask them to call support. A function-calling agent does something more useful.

Step 1: Identify intent

The model recognizes two needs:

• •fetch claim status
• •fetch assigned adjuster details

Step 2: Call backend functions

Your agent exposes approved tools such as:

{
  "name": "get_claim_status",
  "description": "Fetch current claim status by claim ID",
  "parameters": {
    "type": "object",
    "properties": {
      "claim_id": { "type": "string" }
    },
    "required": ["claim_id"]
  }
}

If the user did not provide a claim ID, the agent can ask for it or look it up using authenticated context from your portal session.

Then it may call:

{
  "name": "get_assigned_adjuster",
  "description": "Fetch assigned adjuster for a claim",
  "parameters": {
    "type": "object",
    "properties": {
      "claim_id": { "type": "string" }
    },
    "required": ["claim_id"]
  }
}

Step 3: Return grounded response

Your backend returns:

{
  "status": "In Review",
  "adjuster_name": "Nadia Patel",
  "adjuster_phone": "+1-555-0134"
}

The assistant then responds:

Your claim is currently in review. Your assigned adjuster is Nadia Patel at +1-555-0134. If you want, I can also help you upload supporting documents or check what’s still pending.

That response feels conversational, but under the hood it was driven by structured tool calls against trusted systems.

Why this example matters

This pattern avoids exposing internal APIs directly to users. It also lets you insert controls like:

• •authentication checks before tool execution
• •PII redaction in logs
• •rate limits on high-risk actions
• •human approval for sensitive updates

For an insurance CTO, that means you can automate without surrendering control.

Related Concepts

If you are evaluating function calling for AI agents, these adjacent topics matter:

• •

  Tool use / tool calling

  • •The broader category that includes database queries, API calls, search tools, and workflow triggers.
• •

  Agent orchestration

  • •How multiple steps are chained together: classify intent, gather context, call tools, verify results.
• •

  Structured outputs

  • •Constraining model responses into JSON or schemas so downstream systems can trust them.
• •

  Retrieval-Augmented Generation (RAG)

  • •Useful when the agent needs policy wording, underwriting guidelines, or product documentation before answering.
• •

  Human-in-the-loop controls

  • •Essential for claims exceptions, adverse decisions, fraud flags, and any action with regulatory impact.

Function calling is not magic. It is a clean interface between language understanding and enterprise systems. For insurance organizations building AI agents now, that interface is where reliability starts.

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.