What is tool use in AI Agents? A Guide for CTOs in insurance

Tool use in AI agents is the ability for an AI system to call external tools — such as APIs, databases, calculators, or workflow systems — to complete a task it cannot do from language alone. In insurance, tool use is what lets an agent move from “I understand the request” to “I checked the policy, validated the claim, and started the next step.”

How It Works

Think of an AI agent like a claims handler at a desk.

The model itself is the person’s judgment and conversation skill. The tools are the systems on the desk: policy admin, claims management, CRM, document search, pricing engine, fraud checks. Without those systems, the handler can only talk about the case. With them, they can actually do work.

A simple flow looks like this:

• •A user asks: “Is this claim covered?”
• •The agent reads the request and decides it needs facts from systems of record.
• •It calls a tool like get_policy_details(policy_id).
• •It may call another tool like get_claim_history(claim_id) or search_policy_wording(query).
• •The model combines the returned data and produces an answer or takes action.

The key point: the model does not “know” your policy system internals. It uses structured tool calls with inputs and outputs defined by you.

Here’s a practical way to think about it:

For CTOs, the important distinction is this: tool use turns an LLM from a chat interface into a controlled execution layer. That means you can keep business logic in your systems while letting the agent handle language-heavy tasks like triage, lookup, summarization, and routing.

A good analogy is online banking with card controls.

You do not want the app “guessing” your balance. You want it to query the ledger through a secure API. Tool use works the same way: the agent asks for facts instead of inventing them.

Why It Matters

• •

  It reduces hallucinations.
  The agent can verify policy status, coverage limits, claim history, or customer identity against source systems instead of making up answers.

• •

  It makes automation useful in real workflows.
  Insurance work depends on actions: checking eligibility, opening claims, retrieving documents, issuing letters. Tool use is how agents cross that gap.

• •

  It fits regulated environments better than free-form chat.
  You can log every call, constrain inputs and outputs, require approvals for sensitive actions, and keep audit trails for compliance teams.

• •

  It improves customer and staff experience at once.
  Front-office teams get faster responses on routine questions. Customers get shorter wait times and more consistent service.

• •

  It lets you incrementally modernize legacy estates.
  You do not need to replace core platforms first. Expose stable APIs around existing systems and let agents call them safely.

For insurance specifically, tool use matters because most high-value tasks depend on external truth: policy wording, underwriting rules, claims status, document repositories, payment history. Language models are good at interpreting requests; they are not a replacement for your systems of record.

Real Example

Consider a motor claims intake assistant for a mid-size insurer.

A customer says: “I had a minor accident yesterday. Is my rental car covered?”

Without tool use, a chatbot might answer vaguely based on generic product knowledge. With tool use, the agent can do something much more useful:

1. •Identify the customer and policy.
2. •Call get_policy_details(customer_id) to retrieve cover type.
3. •Call search_policy_wording(policy_id, "rental car") to find applicable clauses.
4. •Call get_claim_status(claim_id) if there is already an open claim.
5. •Summarize whether rental coverage exists and whether any deductible or conditions apply.
6. •If appropriate, create a task for a claims adjuster or start a rental authorization workflow.

A simplified version of that interaction might look like this:

{
  "tool": "search_policy_wording",
  "input": {
    "policy_id": "POL-48291",
    "query": "rental car coverage"
  }
}

Returned result:

{
  "coverage_found": true,
  "summary": "Rental vehicle expenses covered up to 10 days if vehicle is undrivable after insured event.",
  "conditions": [
    "Claim must be reported within 24 hours",
    "Pre-authorization required for rentals over $50/day"
  ]
}

The agent then responds in plain English:

Your policy includes rental car coverage if your vehicle is undrivable after an insured event. It covers up to 10 days, subject to pre-authorization for rentals above $50 per day.

That is materially better than a generic chatbot answer because it is grounded in actual policy data.

From an engineering standpoint, this pattern also gives you control points:

• •You decide which tools exist.
• •You define what each tool can read or write.
• •You enforce permissions by role.
• •You log every request and response for audit.
• •You keep high-risk actions behind human approval where needed.

That is how you make AI agents production-grade in insurance rather than just conversational.

Related Concepts

• •

  Function calling
  The mechanism many model providers use to let an LLM request structured tool execution.

• •

  Retrieval-Augmented Generation (RAG)
  A pattern where the agent retrieves documents or records before answering; often paired with tool use but not the same thing.

• •

  Agent orchestration
  The logic that decides which tools run, in what order, and under what constraints.

• •

  Workflow automation
  Traditional rules-based process automation that can be combined with AI-driven decisioning.

• •

  Guardrails and policy enforcement
  Controls that limit what an agent can access or execute in regulated environments like insurance.

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.