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

Function calling is a way for an AI agent to ask your software to run a specific function, such as fetching a policy record, checking claim status, or calculating a premium. In practice, it lets the model produce structured requests that your systems can execute instead of relying on free-form text alone.

For engineering managers in insurance, the important part is this: function calling turns an AI assistant from a chat interface into a controlled workflow participant. The model decides what action to request, and your application decides whether and how to execute it.

How It Works

Think of function calling like a call center agent with a tightly defined script and access to internal systems.

• •The agent hears the customer request.
• •It identifies the right internal action.
• •It fills in the required fields.
• •Your backend executes the action.
• •The result goes back to the model, which turns it into a human-readable response.

In insurance terms, imagine a policy servicing desk. A customer says, “What’s my deductible and does my plan cover windshield damage?” The AI should not invent an answer. Instead, it should call functions like get_policy_details(policy_id) and lookup_coverage(policy_id, incident_type).

That is the core idea: the model becomes an orchestrator, not the source of truth.

A simple flow looks like this:

1. •User asks a question.
2. •The LLM recognizes it needs external data or an action.
3. •The LLM emits a structured function call.
4. •Your application validates input and executes the function.
5. •The tool result is returned to the LLM.
6. •The LLM drafts the final response for the user.

{
  "name": "lookup_claim_status",
  "arguments": {
    "claim_id": "CLM-48291"
  }
}

The output is usually JSON because machines need structure. That structure matters in regulated environments where auditability, validation, and deterministic execution are more important than clever wording.

Analogy: insurance operations as dispatch

A good analogy is dispatch in an insurance claims office.

• •A dispatcher does not repair cars or approve payouts directly.
• •They route each case to the right team with the right details.
• •They check that required information exists before sending it on.

Function calling works the same way. The AI routes work to your systems using predefined interfaces. It does not get direct access to everything; it only gets access to functions you expose.

Why It Matters

Engineering managers in insurance should care because function calling changes where AI fits into your stack.

• •

  It reduces hallucinations

  The model no longer has to guess claim status, coverage terms, or payment dates. It can retrieve facts from authoritative systems.

• •

  It supports controlled automation

  You can let an agent initiate low-risk actions like “create callback ticket” or “fetch FNOL details” while keeping approvals and payments behind stricter controls.

• •

  It improves auditability

  Every function call can be logged: who asked, what was called, what inputs were used, and what came back. That matters for compliance and dispute handling.

• •

  It makes integrations practical

  Instead of building one-off prompt logic for every backend system, you expose stable functions around policy admin, claims, billing, CRM, and document services.

For managers, this means you are not buying “AI magic.” You are designing a safer integration layer between language models and enterprise systems.

Real Example

Here’s a concrete insurance scenario: a customer asks through a web assistant, “Can I add roadside assistance to my auto policy?”

Without function calling, the model might answer from general knowledge or ask follow-up questions inconsistently. With function calling, you define tools like:

• •get_policy_summary(customer_id)
• •check_endorsement_eligibility(policy_id, endorsement_code)
• •create_endorsement_quote(policy_id, endorsement_code)

The flow becomes:

1. •The user identifies themselves.
2. •The model calls get_policy_summary(customer_id) to find the active auto policy.
3. •It calls check_endorsement_eligibility(policy_id, "ROADSIDE").
4. •If eligible, it calls create_endorsement_quote(...).
5. •The final response tells the customer whether they qualify and what happens next.

tools = [
    {
        "name": "check_endorsement_eligibility",
        "description": "Checks if an endorsement can be added to a policy",
        "parameters": {
            "type": "object",
            "properties": {
                "policy_id": {"type": "string"},
                "endorsement_code": {"type": "string"}
            },
            "required": ["policy_id", "endorsement_code"]
        }
    }
]

This pattern is useful because it keeps business rules in your services where they belong.

If eligibility rules change due to underwriting guidelines or state-specific restrictions, you update one service instead of retraining prompts across multiple assistants. That is much easier to govern in an insurance environment where products vary by jurisdiction and line of business.

Related Concepts

• •

  Tool use

  Broader term for letting models call external capabilities; function calling is one implementation pattern.

• •

  Structured outputs

  Getting JSON or schema-constrained responses from models so downstream systems can parse them reliably.

• •

  Agent orchestration

  Managing multi-step workflows where the model chooses actions across tools until a task is complete.

• •

  RAG (Retrieval-Augmented Generation)

  Pulling documents or records into context so answers are grounded in current source material rather than model memory.

• •

  Guardrails and policy enforcement

  Validation layers that block unsafe actions, enforce permissions, and keep regulated workflows within approved boundaries.

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.