Evals for AI agents
Evals are systematic tests that measure how well an AI agent performs a task — a test suite for non-deterministic systems, used to catch regressions and compare changes.
Because agents are non-deterministic, you cannot judge a change by trying it once. Evals run the agent against a fixed set of tasks with defined success criteria and score the outcomes — automatically, by another model acting as judge, or by humans. A good eval set turns "it feels better" into a number you can track, so you can tell whether a new prompt, model, or tool actually improved things or quietly broke something.
Key points
- A repeatable task set with success criteria, scored to measure agent quality.
- Essential because agents are non-deterministic — a single run proves nothing.
- Scoring can be automated, model-judged (LLM-as-judge), or human.
- They turn subjective "seems better" into a tracked metric and catch regressions before users do.
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