Evals are how you know an agent works before your customers find out it doesn’t. An eval suite is a repeatable set of test cases that measures an agent’s accuracy, cost, and behavior — the closest thing agent development has to a unit test, and the only honest way to compare two versions.
These posts cover how to build eval suites that catch real regressions, how to wire them into CI so a bad prompt change fails the build instead of reaching production, and how to measure the trade-offs — accuracy against latency, quality against cost — that every agent decision involves.
Topics include designing representative test sets, grading non-deterministic outputs, running regression tests on every change, tracking quality and cost over time, and keeping evals green once an agent is live. The principle is simple: an agent you cannot measure is an agent you cannot trust, and shipping without evals is shipping blind.