Agent evals

Agent evals are the tests you run to measure whether an AI agent actually does its job well, scoring its outputs against known-good answers or real-world success rather than trusting that it looks right. You assemble a set of representative tasks, run the agent, and check the results: did it get the fact right, take the correct action, stay on brand, avoid the trap.

For a founder this is how you stop flying blind. An agent can demo beautifully and still fail one time in ten in ways you only discover when a customer hits it. Evals turn that into a number you can track, so when you tweak a prompt or swap a model you can see whether quality went up or down instead of guessing. They matter most before you scale: prove the agent works on a hard, honest test set first, then let it loose. No evals, and every change to your agent is a roll of the dice.