Evals

An eval is to an agent what a test suite is to ordinary software. You collect a set of realistic inputs, define what a good result looks like for each, run your agent on all of them, and score the outputs.

Scoring can be exact, like checking that the right tool was called or that a number matches, or it can use rubrics graded by a person or by another model. Most useful eval sets start small, with a few dozen cases drawn from real usage and real failures, and grow over time.

Without evals, every prompt tweak or model upgrade is a guess. With them, you can make changes confidently, catch regressions before users do, and compare models on your own tasks rather than on generic benchmarks.