Evals, short for evaluations, are the structured tests used to measure how well an AI system performs at a specific job. Just as you would not promote a new employee without seeing their work, you should not trust an AI agent without evidence that it does its task correctly. Evals provide that evidence. They are a defined set of test cases, each with a known correct answer, that the AI is run against so you can score how often and how accurately it gets things right.
A good analogy is a driving test. A new driver does not simply claim they can drive. They are put through a standardized set of scenarios, parking, merging, stopping at signs, and graded on each. Evals do the same for an AI agent. For example, to evaluate an invoice-processing agent, you would assemble a collection of real invoices where you already know the correct vendor, amount, and due date, then check how many the agent extracts correctly.
For a business, evals are how you turn "the AI seems to work" into "the AI is correct 98 percent of the time on these cases." They let you catch mistakes before they reach production, compare different approaches objectively, and monitor whether quality holds up over time. Without evals, you are relying on gut feel. With them, you have measurable proof of accuracy that you can show stakeholders and use to decide whether an agent is ready for real work.
Traditional software testing checks whether code produces an exact, predictable output. Evals deal with AI systems whose answers can vary, so they measure quality and accuracy across many examples rather than a single pass or fail. An eval might report that an agent is correct 96 percent of the time, which is a more useful measure for AI than a simple yes or no.
An eval is a dataset of test cases, each paired with the correct expected result. For instance, a set of one hundred customer emails labeled with the correct category, used to test whether a classification agent sorts them properly. The agent's output is compared against these known answers to produce a score.
They let you measure accuracy on real examples before an agent touches actual business data. If an invoice agent scores poorly on a test set, you fix it before it ever miscodes a real payment. Evals turn risky guesswork into a controlled, evidence-based decision.
Ideally both before launch and on an ongoing basis. AI behavior can shift when models are updated or when the kind of work changes, so periodic evals confirm that accuracy is holding steady rather than quietly degrading.
A failing score is a signal to improve before deployment. You can refine the agent's instructions, adjust its process, or add human checkpoints for the cases it struggles with.
Zamp addresses this by combining evals with structured processes and a Knowledge Base where you define instructions and rules in plain language, so weak spots found during evaluation can be corrected directly. For cases an agent is not confident about, it flags a "Needs Attention" status for human review instead of guessing, and activity logs let you trace exactly what happened on any case that an eval surfaces as a problem.
Yes, and you generally should. The most meaningful evals use your real documents and scenarios, because they reflect the actual work the agent will do rather than generic examples.