Agent evaluation

Agent evaluation, often called evals, is the practice of testing an AI analytics agent against questions where the correct answer is already known. You run the questions, compare the agent’s answers to the truth, and get a real accuracy number.

This is what separates a tool you hope works from a tool you can trust. Without evals, you find out the agent is wrong when someone acts on a bad number. With evals, you catch the problem before anyone relies on it.

Why it matters

An agent that is right most of the time, with no way to tell which times, is not safe to build on. Evaluation gives you that visibility, and it tells you whether a change to your context made the agent better or worse. It is also the main defense against hallucination.