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

Tool use in AI agents is the ability for an agent to call external functions, APIs, or systems to complete a task. In practice, it means the model does not just generate text; it can fetch data, update records, run calculations, and trigger workflows.

How It Works

Think of an AI agent like a claims handler with a checklist and access to internal systems.

The language model is the decision-maker. Tool use is how it reaches outside itself to do real work:

• •It reads the user request
• •It decides whether it needs outside information or an action
• •It selects a tool, such as a policy lookup API or claims system
• •It passes structured inputs to that tool
• •It receives the result and uses it to continue the conversation

A simple analogy: if the model is a driver, tools are the keys, GPS, and phone it uses to get things done. The driver still makes decisions, but without those tools it cannot navigate real roads.

For insurance teams, this matters because most useful workflows are not pure text problems. They involve:

• •Policy administration systems
• •Claims platforms
• •Customer identity verification
• •Pricing engines
• •Document retrieval
• •Fraud and risk checks

A good agent does not “know” everything. It knows when to ask a system that does.

Here’s the core pattern:

1. •User asks something like: “What’s the status of claim 88421?”
2. •The agent recognizes that claim status is not in its memory.
3. •It calls a get_claim_status tool with claim_id=88421.
4. •The claims system returns structured data.
5. •The agent explains the result in plain English.

This is much safer than letting the model guess. In regulated environments, guessing is how you create bad customer experiences and compliance issues.

Why It Matters

• •

  Reduces hallucinations

  • •The agent can verify facts against source systems instead of inventing answers.
  • •That matters when customers ask about coverage limits, deductibles, exclusions, or claim status.
• •

  Connects AI to real workflows

  • •An agent without tools is just a chat interface.
  • •With tools, it can open cases, retrieve documents, calculate premiums, or escalate exceptions.
• •

  Improves operational efficiency

  • •Repetitive tasks like policy lookups and FNOL triage can be automated.
  • •That frees staff for exception handling and customer-facing work.
• •

  Supports auditability

  • •Tool calls can be logged with inputs, outputs, timestamps, and user context.
  • •In insurance, that trace matters for compliance and dispute resolution.

Real Example

Let’s say you’re building an assistant for a motor insurer’s claims team.

A customer says:

“I had an accident yesterday. Is my policy active, and what should I do next?”

A useful agent might use three tools:

The flow looks like this:

User asks question
→ Agent checks policy status
→ Agent confirms cover is active
→ Agent retrieves approved FNOL steps
→ Agent offers to create a claim case
→ If user agrees, agent creates the case in the claims system

A production version should not let the model freestyle on policy rules. Instead:

• •Coverage status comes from policy admin systems
• •Next steps come from approved content or workflow rules
• •Case creation happens through a controlled API
• •Any uncertain case gets escalated to a human handler

That gives you a practical balance: natural language on top, deterministic systems underneath.

Here’s what the tool definition might look like in code:

tools = [
    {
        "name": "get_policy_details",
        "description": "Fetch policy status and key coverage fields",
        "parameters": {
            "type": "object",
            "properties": {
                "policy_number": {"type": "string"}
            },
            "required": ["policy_number"]
        }
    },
    {
        "name": "create_fnol_case",
        "description": "Create first notice of loss case",
        "parameters": {
            "type": "object",
            "properties": {
                "policy_number": {"type": "string"},
                "incident_date": {"type": "string"},
                "summary": {"type": "string"}
            },
            "required": ["policy_number", "incident_date", "summary"]
        }
    }
]

The important part is not the syntax. It’s the contract.

If your tool schema is clear, your agent becomes easier to test, monitor, and secure. If your schema is vague, you get brittle behavior and bad downstream actions.

Related Concepts

• •

  Function calling

  • •The mechanism many LLM platforms use for tool use.
  • •The model emits structured arguments instead of plain text when it wants an action taken.
• •

  RAG (Retrieval-Augmented Generation)

  • •Used when the agent needs to fetch documents or knowledge before answering.
  • •Common for policy wording, underwriting guidelines, and claims playbooks.
• •

  Workflow orchestration

  • •The layer that sequences multiple steps across systems.
  • •Useful when an insurance process needs validation, approval, then case creation.
• •

  Guardrails

  • •Rules that constrain what the agent can do.
  • •Important for preventing unauthorized actions like changing coverage or exposing PII.
• •

  Human-in-the-loop review

  • •A fallback where sensitive or ambiguous cases go to an employee.
  • •Standard pattern for high-risk insurance decisions like fraud flags or coverage disputes.

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.