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

Tool use in AI agents is the ability for an AI system to call external tools — like APIs, databases, calculators, or workflow systems — to complete a task. In insurance, tool use means the agent can do more than chat: it can look up policy data, check claim status, calculate premiums, or create a case in your core system.

How It Works

Think of an AI agent as a claims handler with a desk full of instruments.

The model itself is the decision-maker. The tools are the systems it can reach: policy admin, claims management, CRM, document search, pricing engines, and payment services.

A simple flow looks like this:

• •A user asks: “Can I add my daughter to my auto policy?”
• •The agent reads the request and decides it needs policy data.
• •It calls the policy API with the customer ID.
• •It checks eligibility rules and effective dates.
• •It returns an answer or triggers the next action.

The key point is that the AI is not guessing from memory. It is using live systems to ground its response.

For product managers, the easiest analogy is a good insurance broker on the phone. The broker does not know every detail upfront. They ask questions, check your policy system, maybe verify underwriting rules, then come back with a precise answer. Tool use gives an AI agent that same operating model.

Under the hood, this usually works through function calling or tool schemas. The agent receives a list of available tools and their inputs/outputs. When it decides a tool is needed, it emits a structured request instead of plain text.

Example pattern:

{
  "tool_name": "get_policy_details",
  "arguments": {
    "policy_number": "AU-482193",
    "customer_id": "C12345"
  }
}

The tool returns structured data, and the agent uses that data to continue the conversation or complete a workflow.

Why It Matters

• •

  It reduces hallucinations.
  An agent answering from live policy or claims data is far safer than one improvising from training data.

• •

  It enables real workflows, not just chat.
  The value comes when the agent can retrieve documents, update records, open cases, or route work to humans.

• •

  It improves customer experience.
  Customers want answers like “Is my claim approved?” not “I think you should contact support.” Tool use gets you closer to instant resolution.

• •

  It creates measurable automation.
  Product teams can track how often an agent resolves issues without human intervention, which tools fail most often, and where handoffs happen.

For insurance teams, this matters because most valuable interactions depend on systems of record. Policy terms live in one place, claims in another, documents somewhere else. Tool use lets one agent coordinate across those systems without forcing users to navigate them manually.

Real Example

Let’s say a customer asks through a web chat:

“My roof was damaged in last night’s storm. Can I start a claim?”

A tool-enabled insurance agent can handle this step by step:

1. •

   Identify the intent
   It recognizes this as a first notice of loss request.

2. •

   Check policy coverage
   It calls get_policy_details to confirm active home coverage and deductible.

3. •

   Verify incident eligibility
   It checks whether storm damage is covered under that product and whether there are known exclusions.

4. •

   Create the claim
   If eligible, it calls create_claim with incident details collected from the customer.

5. •

   Return next steps
   It gives the claim number and tells the customer what documents to upload.

A simplified orchestration might look like this:

policy = get_policy_details(policy_number="HO-99120")
if policy["status"] == "active" and policy["coverage"]["storm_damage"]:
    claim = create_claim(
        policy_number="HO-99120",
        loss_type="roof_damage",
        incident_date="2026-04-18"
    )
    response = f"Your claim has been created: {claim['claim_number']}"
else:
    response = "I can't start this claim because the policy is inactive or does not cover storm damage."

This is where product value shows up fast:

• •Shorter call center queues
• •Faster FNOL intake
• •Better data quality at entry
• •Less manual rekeying between systems

The important design choice is that each tool does one thing well. The agent should not be making business decisions in free text when those decisions belong in rules engines or underwriting logic already approved by the business.

Related Concepts

• •

  Function calling
  The technical mechanism that lets an LLM request a specific tool with structured inputs.

• •

  Retrieval-Augmented Generation (RAG)
  Used when the agent needs to fetch documents or knowledge before answering.

• •

  Workflow orchestration
  How multiple tools are chained together across systems and approval steps.

• •

  Guardrails
  Rules that limit what the agent can do, especially for regulated actions like claims decisions or payments.

• •

  Human-in-the-loop
  A fallback pattern where sensitive cases are escalated to an adjuster or service rep for review before execution.

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.