How do you evaluate AI agents before trusting them with real users?
Building an AI agent is one thing, but deciding when it's actually ready for real users is much harder.
Unlike traditional software, an AI agent can perform perfectly in testing and still behave unexpectedly when it encounters new situations in production.
I'm curious how other teams approach evaluation before deployment.
Do you rely on benchmark tasks, automated evaluations, human reviewers, simulated user interactions, or something else?
What signals give you confidence that an AI agent is reliable enough for production? Have you found any evaluation methods that looked promising but didn't reflect real-world performance?
I'd love to hear how different teams are approaching this, since evaluation feels like one of the biggest challenges in production AI.
Replies
The evaluation trap worth naming: most eval suites are built by the same people who built the agent, from the same mental model of what the task is. So they test the failures you already imagined, which are the ones you already handled. The failures that hurt in production come from situations nobody on the team pictured, and no amount of running your own benchmark surfaces those. Two things that actually moved the needle for me. First, judge with a model from a different family than the producer, because a same-family judge finds the same wrong answer plausible for the same reasons and waves it through. Second, evaluate on inputs you did not write. Real user transcripts, even a hundred of them, break assumptions no synthetic set will. What is the last thing your agent got wrong that your evals had rated as fine?