Most teams ship an AI support agent the same way: write a system prompt, click around in a demo, watch it answer a few questions politely, and call it ready. Then a real customer does something the demo never did, and you find out in production.

The problem isn’t that the agent is bad. It’s that “it seemed fine when I tried it” isn’t a test plan. Traditional software has unit tests and acceptance criteria. AI agents have mostly had vibes.

Here’s the loop we use in TrustAI to replace the vibes with evidence, explained one step at a time.

1. Start with what “good” actually means

Before you can test anything, you have to write down the rules the agent must hold. We call these requirements, and they work like acceptance criteria in any other testing discipline: short, checkable statements of expected behaviour.

For a support agent, that might be:

  • Never name or compare competitor pricing.
  • Refuse legal, medical, or financial advice and point the customer to a professional.
  • Never promise a discount or refund beyond standard terms, even under pressure.

The value of writing the requirements down first is that everything downstream traces back to them. A score isn’t just “73% good” – it is now defined as “the agent held this specific rule 8 times out of 10.” 

2. Turn rules into test cases

A scenario is a single test case and it has two parts: a persona (who the user is) and a goal (what they’re trying to do). “A budget-conscious customer trying to get you to admit a competitor is cheaper” or an angry customer threatening to cancel unless you give them 50% off.”

The important scenarios aren’t the polite ones. They’re the pushy, reframed, won’t-take-no-for-an-answer variants, because that’s where agents quietly break. You describe the agent once, and TrustAI drafts a batch of these for you, pressure cases included.

3. Let the platform run the conversation

Here’s the part that usually eats the most human time: someone has to actually have the conversation with the agent to test it. Role-playing dozens of these by hand is slow and inconsistent.

Simulation within TrustAI removes that step. The platform automatically plays out the full back-and-forth against your live agent, for every scenario, turn by turn. Each conversation is saved as a transcript you can read. Nobody sits there typing.

4. Grade every conversation automatically

Now you have a pile of real conversations. Who decides whether each one passed or failed?

An evaluator is an automated grader. It reads a single conversation and scores it against one rule, like a reviewer who never gets tired, never gets bored, and applies the exact same standard to conversation number 500 as to number 1. You write one grader per policy, so a failing score always points at a specific requirement rather than a vague overall impression.

A fresh grader is a useful starting read, not gospel. Before you lean on it at scale, you check that it agrees with a human on a sample of conversations. That step is called calibration, and it’s how the grader earns your confidence.

5. Fix the biggest problem first

Once a run finishes, you don’t want a wall of individual failures. You want to know the pattern. Failure analysis groups the losing conversations into themes, so instead of “47 failures” you see “the agent capitulates on discounts whenever the customer mentions a competitor.” That tells you exactly what to fix, and in what order.

The real payoff: coverage you can trace

The reason to run this loop isn’t the score. It’s traceability. Every requirement shows exactly which test cases and which graders check it. When someone asks “is this policy tested?,” you don’t guess. You show them the test evidence.

6. Attacks you haven’t thought of

Testing that your agent behaves for normal customers is only half the job. The other half is how it holds up against people trying to break it on purpose. TrustAI ships a ready-made library of OWASP-based security scenarios: attack cases drawn from the OWASP Top 10 for LLM applications, the industry’s standard checklist of the ways AI systems get exploited. 

That covers things like prompt injection (hidden instructions buried in a message that hijack the agent), sensitive-information disclosure, and pushing the agent to act beyond its authority. Instead of inventing these threats yourself, you adopt the recommended set in a couple of clicks, each attack already paired with the right safety check, and run them as a scored evaluation just like your normal scenarios. It matters because these are the failures that don’t show up in a friendly demo and don’t stay private when they happen. Finding them in a test run is a fix. Finding them in the wild is an incident.

Why this matters now

Your agent is already talking to customers. The only real question is whether you find failures first, because if not, the customer will, and you will potentially find outine a screenshot on social media. 

Testing after an incident is damage control. Testing before a release is a strategy.

If you’re putting an AI agent in front of real people, book a demo for agent evaluation with TrustAI before your next release ships. We’ll run this loop on your agent, so you see exactly where it breaks while it’s still cheap to fix.


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