How do you evaluate whether an AI agent is actually reliable?
One challenge I've been thinking about is how to measure whether an AI agent is genuinely reliable.
It's easy to know when a traditional application works correctly because the expected output is usually well defined. AI agents are different—they reason, call tools, make decisions, and sometimes produce different outputs for the same task.
Before deploying an agent, how do you decide it's ready for real users?
Do you use benchmark tasks, automated evaluations, human reviewers, production metrics, or something entirely different?
I'm also interested in how you track reliability after deployment. Are there specific metrics or signals that tell you an agent is improving—or getting worse over time?
I'd love to learn how other teams are approaching this problem because evaluation feels like one of the biggest challenges in production AI.
Replies
The test I use: does it know when to stop? Any model can produce an answer. The reliable ones abstain when the input is ambiguous or the source is missing.
In my case that means an assessment saying "no expiry date found on this certificate, confirm manually" rather than guessing one. Measuring how often it correctly refuses has been more useful to me than measuring accuracy on the cases it does answer.