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

Evaluation in AI agents is the process of measuring whether an agent does the right thing, consistently, under real conditions. In insurance, evaluation tells you if the agent gives correct answers, follows policy rules, and avoids risky behavior before customers ever see it.

How It Works

Think of evaluation like a claims QA checklist, but for an AI agent.

A claims manager does not judge a team by one good case. They review many cases against clear criteria: was the loss covered, was the documentation complete, was the payout calculation correct, did the adjuster follow process? Evaluation works the same way for AI agents. You define what “good” means, run the agent against test scenarios, and score its outputs.

For product managers, the important part is that evaluation is not just “does it sound smart?” It is a structured test against business goals.

A typical evaluation setup has these parts:

• •Test set: a collection of realistic insurance scenarios
  • •Example: FNOL intake, policy coverage questions, claim status updates, renewal objections
• •Expected outcome: what the agent should do
  • •Example: route to human adjuster, ask for missing documents, quote policy clause correctly
• •Scoring criteria: how you measure success
  • •Example: accuracy, compliance, escalation quality, response completeness
• •Thresholds: pass/fail targets
  • •Example: 95% policy accuracy on approved scenarios

In practice, engineers often run evaluations in batches. The agent is given dozens or hundreds of scripted cases. Each response is checked automatically where possible, then reviewed manually for edge cases like tone, compliance language, or hallucinated policy details.

A useful analogy is underwriting. You do not approve a policy based on one factor. You look at multiple signals and compare them to rules. Evaluation does the same thing for agent behavior across many scenarios.

Why It Matters

Product managers in insurance should care about evaluation because AI agents can create business risk very quickly if they are not measured properly.

• •It protects compliance
  • •An agent that gives incorrect coverage advice can create regulatory and legal exposure.
• •It reduces customer harm
  • •Bad answers on claims, cancellations, or exclusions can frustrate customers and increase complaints.
• •It helps you ship with confidence
  • •Evaluation gives you evidence that the agent works before rollout to production.
• •It makes trade-offs visible
  • •You can compare models or prompts using hard numbers instead of opinions.
• •It supports continuous improvement
  • •As policies change or new products launch, evaluation shows whether performance is drifting.

For insurance teams, this matters because AI agents often sit close to sensitive workflows. A small error in wording can become a complaint. A missed escalation can become a bad claim outcome. Evaluation is how you keep that under control.

Real Example

Let’s say you are building an AI agent for a life insurance carrier to handle beneficiary update requests through chat.

The goal sounds simple: help customers update beneficiary information without sending every case to support. But there are several failure modes:

• •The agent may accept incomplete identity verification
• •It may give legal advice about estate planning
• •It may fail to detect when a request needs manual review
• •It may use outdated policy language

Here is how evaluation would work:

1. •

   Create test scenarios

   • •Customer wants to change a beneficiary after marriage
   • •Customer asks whether they can name a minor as beneficiary
   • •Customer submits a request without identity verification
   • •Customer asks what happens if no beneficiary is listed
2. •

   Define expected behavior

   • •Ask for required identity checks before proceeding
   • •Provide only approved policy guidance
   • •Escalate legal or complex cases to a human specialist
   • •Never invent policy rules
3. •

   Score responses

   • •Correctness of answer
   • •Compliance with approved script
   • •Proper escalation decision
   • •Clarity and customer-friendly tone
4. •

   Review results Suppose the agent gets 92% accuracy overall but fails on two high-risk cases:

   • •It incorrectly says a minor can always be named as beneficiary
   • •It skips escalation on an ambiguous estate-related question

From a product perspective, that tells you something important: this agent may be fine for low-risk FAQs but not ready for unsupervised handling of beneficiary changes. You might ship it with tighter guardrails:

• •Only allow it to answer general questions
• •Require human review for all beneficiary changes
• •Add more test cases around family status and estate-related edge cases

That is evaluation in action. It turns vague confidence into measurable readiness.

Related Concepts

• •Testing
  • •Checks whether software behaves as expected; evaluation extends this idea to model behavior and quality.
• •Prompting
  • •The instructions you give the model; evaluation tells you whether those instructions actually work.
• •Guardrails
  • •Rules that prevent unsafe outputs; evaluation measures whether those guardrails hold up.
• •Human-in-the-loop review
  • •Manual oversight from staff; often used when stakes are high or edge cases are messy.
• •Monitoring
  • •Ongoing production tracking; evaluation happens before launch and monitoring continues after launch.

If you are managing an AI agent in insurance, think of evaluation as your pre-launch control system. It tells you whether the agent is accurate enough, compliant enough, and stable enough to trust with real customer workflows.

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.