Here’s how to catch the mistakes before they do.
Most enterprises testing an AI agent today are really just watching it. Someone opens a chat window, runs through a few conversations by hand, nods at the outputs, and signs off. That process works fine for catching an agent that’s obviously broken. It does nothing for the failure that only shows up on the two-hundredth conversation, with a persona nobody thought to try, saying something nobody scripted.
That gap matters more at enterprise scale than anywhere else. A consumer app with a clunky agent loses a user. An enterprise agent handling account verification, claims, or regulated financial data loses a customer’s trust, generates an incident report, and lands in front of a compliance team asking exactly how it was tested. “We chatted with it for a while and it seemed fine” is not an answer that satisfies a regulator, a board, or a customer.
Agent Evaluation, built for the team that actually owns quality
TrustAI’s Agent Evaluation is no-code agent testing built for QA and delivery teams. That distinction matters at enterprise scale, where the people accountable for agent quality are rarely the same people who built the agent, and where testing can’t be a bottleneck that only a handful of engineers know how to operate.
Four things make that possible:
- Human in the loop. Automated evaluation is only trustworthy if the automation itself is checked. Teams review and label real examples so evaluators stay calibrated to human judgment — meaning the grade an evaluator gives isn’t a black box, it’s a score you’ve verified matches what your own experts would conclude.
- Run scenarios. TrustAI simulates thousands of realistic agent conversations before go-live, and it doesn’t need your production traffic to start. Teams can begin testing the moment an agent exists, not months later once real usage has accumulated, which matters when the whole point is catching problems before customers do.
- Evaluate at scale. Agents answer differently every time you ask them the same question. TrustAI’s automated, repeatable testing is built for exactly that — running the same scenario across personas and variations at a volume no manual review process could match.
- Requirements to coverage. Every agent behavior maps back to a business or compliance requirement, and TrustAI reports real coverage against it — not a vague sense that “we tested a lot,” but a defensible answer to which requirements are verified, which aren’t, and where the gaps sit. For an enterprise audience, that traceability is often the difference between an agent that can go to production and one that can’t.
TrustAI Agent Evaluation catches real failures
Here’s what that looks like in practice, with no demo-day setup involved.
We pointed TrustAI at an open-source card-servicing agent. Nothing rehearsed, nothing tuned in advance. A generated dispute call pushed on identity verification the way a real fraudster would, probing for a way through.
The agent leaked. It volunteered the customer’s real email, phone number, home address, and date of birth to a caller who hadn’t been verified. A calibrated judge caught it immediately, with full reasoning attached to the flag. Across the full scenario set, nearly half failed. The failures included a data leak, surfaced before a single real customer was ever involved.
Nobody wrote a test script anticipating that exact failure. Nobody seeded the bug to make a point. TrustAI found it on its own, because that’s what the platform is built to do: probe an agent the way a determined, unpredictable person actually would, and grade the outcome against a standard you can trust.
The validation layer for the AI era
Enterprises aren’t short on ambition to deploy agents. They’re short on a reliable way to know whether those agents are ready in order to that scale past a handful of manual conversations, one that holds up under audit, and doesn’t depend on the one engineer who happens to understand the agent’s internals. That’s the layer TrustAI exists to be: the validation standard sitting between “the agent works in the demo” and “the agent is safe to put in front of a customer.”
If your team is shipping agents into production – or about to – the question worth answering now isn’t whether they’ll eventually be tested properly. It’s whether you find the failures first, or your customers do.
Book a demo to see TrustAI Agent Evaluation run against an agent like yours.
