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

Tool use in AI agents is the ability for an agent to call external functions, APIs, or systems to complete a task instead of relying only on the model’s internal text generation. In insurance, that means the agent can look up policy data, check claim status, calculate premiums, create tickets, or trigger workflows inside your existing systems.

How It Works

Think of an AI agent as a claims coordinator with a desk full of forms and phone numbers.

The model itself is not the whole operation. It decides what needs to happen next, then uses tools to do the actual work: query a policy admin system, search a document store, call a rating engine, or write back to a CRM. The agent reads the result from the tool, then decides whether it has enough information or needs another step.

A simple flow looks like this:

1. •User asks: “Can you check whether this customer’s water damage claim is eligible?”
2. •Agent identifies missing facts: policy number, loss date, coverage type.
3. •Agent calls tools:
   • •get_policy(policy_number)
   • •get_claim_history(customer_id)
   • •check_coverage(policy_id, loss_type)
4. •Tools return structured data.
5. •Agent summarizes the result in plain English and may draft the next action.

The key point: the model is not guessing from memory. It is orchestrating work across systems.

For engineering managers, this matters because tool use changes the architecture from “chatbot with answers” to “workflow participant with permissions.” That means you need clear boundaries around:

• •What tools exist
• •What each tool can access
• •What data gets logged
• •When human approval is required

A useful analogy is an adjuster using a laptop during a site visit. The adjuster does not invent coverage terms from memory; they open policy records, inspect photos, check prior claims, and submit notes into the system. Tool use gives an AI agent that same operational reach.

Why It Matters

• •

  It turns AI from advisory to operational

  • •Without tools, the model can only explain.
  • •With tools, it can act inside your claims, underwriting, and servicing workflows.
• •

  It reduces manual swivel-chair work

  • •Insurance teams often bounce between portals, core systems, email, and spreadsheets.
  • •Tool use lets one agent coordinate those steps automatically.
• •

  It improves answer quality

  • •The model can fetch current policy details instead of relying on stale context.
  • •That matters for coverage questions, FNOL triage, renewals, and endorsements.
• •

  It creates governance requirements

  • •Once an agent can access systems of record, you need permissioning, audit trails, rate limits, and fallback paths.
  • •This is where engineering management gets involved fast.

Real Example

Consider a commercial property insurer handling first notice of loss after a storm.

A broker emails: “My client’s warehouse roof collapsed overnight. Is this covered and what should we do next?”

An AI agent with tool use could handle the intake like this:

• •Extract policy number and insured name from the email
• •Call lookup_policy() in the policy administration system
• •Call retrieve_endorsements() to check exclusions and flood/wind coverage
• •Call open_claim() if coverage appears active
• •Call create_task() in the claims workflow system for an adjuster review
• •Draft a response to the broker with:
  • •claim reference number
  • •immediate next steps
  • •any missing documents needed

Example tool sequence:

User email -> Agent
Agent -> lookup_policy(policy_number)
Agent <- active policy + coverage details

Agent -> retrieve_endorsements(policy_id)
Agent <- windstorm exclusion found

Agent -> open_claim(policy_id, loss_date)
Agent <- claim_id = CLM-104882

Agent -> create_task(claim_id, "Coverage review", priority="high")
Agent -> draft_reply(...)

What makes this valuable is not just speed. It also standardizes intake quality. Every claim gets checked against the same policy fields and routed through the same workflow rules.

For engineering managers in insurance, this is where ROI becomes visible:

Related Concepts

• •

  Function calling

  • •The mechanism models use to invoke specific tools with structured inputs.
• •

  Workflow orchestration

  • •Coordinating multiple steps across systems like claims platforms, CRMs, and document stores.
• •

  Retrieval-Augmented Generation (RAG)

  • •Fetching documents or records so the model answers from current company data.
• •

  Guardrails

  • •Rules that limit what an agent can do, especially around PII, payments, approvals, and regulated decisions.
• •

  Human-in-the-loop

  • •Requiring staff review before high-risk actions like claim denial drafts or payment initiation.

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.