AI agent evaluation: complete definition in AI for SMEs

AI agent evaluation is the method of measuring whether an AI agent — which chains several tasks through several tools — correctly achieves its objectives. The LLM is evaluated by testing responses; the agent, meanwhile, is evaluated on its ability to complete a full journey: getting the right result, at the right time, without errors, with the right tools. It is a more complex, multi-step evaluation.

What it changes for an SME

Agent evaluation is the guardrail between the demo prototype and the production agent:

• a prospecting agent: do you evaluate email quality, target compliance, volume handled, or all three?
• a support agent: do you measure ticket resolution, customer satisfaction, or processing time?
• a document management agent: do you check correct classification, completeness, or absence of critical errors?

Best practice

Define the business success criteria before building the agent, then build a test set reproducing real cases with edge cases. Measure continuously, not once. In fractional AI leadership, we document each evaluation as a report: success rate, critical errors, cost per task and human oversight budget.