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

Function calling in AI agents is the ability for a model to choose and invoke a specific software function when it needs external data or an action it cannot do from the prompt alone. In practice, it lets an AI agent turn natural language into structured API calls, database lookups, workflow steps, or policy actions.

How It Works

Think of function calling like a claims handler with a checklist and a phone directory.

The AI agent is the handler. It reads the customer request, decides what information is missing, then calls the right internal system instead of guessing. If a policyholder asks, “Is my claim eligible for emergency accommodation?” the agent does not invent an answer. It calls a function like get_policy_details, check_claim_status, or lookup_coverage_rules.

The flow usually looks like this:

• •The user asks a question in plain English.
• •The model interprets the request and identifies intent.
• •Your application exposes a set of functions with names, descriptions, and input schemas.
• •The model selects one function and passes structured arguments.
• •Your backend executes the function against real systems.
• •The result is returned to the model, which formats the final response.

A simple mental model:

For insurance teams, this matters because most useful answers depend on live systems: policy admin platforms, claims engines, CRM records, underwriting rules, document stores, and fraud signals. The model should not “know” these things from memory. It should query them.

A function definition typically includes:

• •A clear name
• •A short description
• •Input parameters with types
• •Validation rules
• •Permissions or guardrails

Example shape:

{
  "name": "get_claim_status",
  "description": "Fetch the current status of an insurance claim",
  "parameters": {
    "type": "object",
    "properties": {
      "claim_id": { "type": "string" }
    },
    "required": ["claim_id"]
  }
}

That schema matters. It constrains the model so your application gets predictable inputs instead of free-form text.

Why It Matters

• •

  It reduces hallucinations.
  Insurance workflows need factual answers tied to policy data, not model guesses.

• •

  It connects language to systems.
  Agents can move from “What’s my deductible?” to actually reading policy records or quoting coverage rules.

• •

  It supports automation without brittle intent trees.
  You can replace long FAQ flows with an agent that routes requests to functions based on context.

• •

  It makes compliance easier to control.
  You decide which actions are available, what data can be accessed, and when human review is required.

For developers in insurance, this is the difference between a chat UI and a usable assistant. A chat UI talks back. A function-calling agent can check claim status, summarize policy documents, create service tickets, and escalate edge cases into existing workflows.

Real Example

Let’s say you are building an FNOL assistant for motor insurance.

A customer types: “I had an accident yesterday in Birmingham. Can I start a claim?”

The agent should not try to answer from memory. It should collect missing details and call functions in sequence.

Possible functions:

{
  "name": "create_claim_intake",
  "description": "Create a new first notice of loss record",
  "parameters": {
    "type": "object",
    "properties": {
      "policy_number": { "type": "string" },
      "incident_date": { "type": "string" },
      "incident_location": { "type": "string" },
      "loss_type": { "type": "string" }
    },
    "required": ["policy_number", "incident_date", "incident_location", "loss_type"]
  }
}

Workflow:

1. •The user says they want to start a claim.
2. •The agent asks for their policy number if it is missing.
3. •Once provided, it calls lookup_policy to verify active cover.
4. •It calls create_claim_intake with validated details.
5. •It returns: “Your claim has been started. Your reference number is CLM-483921.”

If the policy is lapsed or the incident falls outside cover, the agent can route to a human or provide a compliant explanation instead of making something up.

Here’s why this pattern works well in insurance:

• •The model handles conversation and clarification.
• •Functions handle truth and side effects.
• •Your backend enforces business rules.
• •Audit logs capture every action taken by the agent.

That separation is what makes agents production-worthy.

Related Concepts

• •

  Tool use
  Broader term for letting models call external capabilities like APIs, search, calculators, or databases.

• •

  Structured outputs
  Forcing the model to return JSON or typed fields so downstream systems can trust the format.

• •

  Agent orchestration
  Managing multi-step workflows where the model plans, calls tools, checks results, and continues.

• •

  RAG (Retrieval-Augmented Generation)
  Pulling relevant documents into context before answering; useful for policy wording and claims manuals.

• •

  Human-in-the-loop approval
  Requiring reviewer sign-off before sensitive actions like payments, cancellations, or underwriting overrides.

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.