Evaluation

What is evaluation?

Evaluation is the systematic measurement of an AI agent's outputs, decisions, actions, and task outcomes against defined criteria. It answers whether the agent performed well enough for a particular use case.

Agent evaluation can inspect both the final result and the path taken to reach it.

What can be evaluated?

Useful dimensions include:

• task success and completeness;
• factual accuracy and grounding;
• correct tool selection and arguments;
• safety and policy compliance;
• efficiency, latency, and cost;
• recovery from errors;
• quality of handoffs or plans;
• user satisfaction.

For example, a support agent may produce a polite answer but still fail because it used the wrong policy or claimed that a refund was issued when the tool result showed only a draft.

Evaluation methods

Evaluation can use deterministic checks, expert review, user feedback, test environments, model-based graders, or combinations. Some criteria are objective, such as whether a database record changed. Others require a rubric, such as whether an explanation is clear and complete.

Representative datasets should include normal cases, edge cases, tool failures, ambiguous requests, and adversarial inputs.

Evaluation versus observability

Observability provides the execution evidence needed to understand what happened. Evaluation applies criteria to judge whether that behavior was acceptable.

A trace may reveal the selected tool and its result. An evaluator determines whether the tool choice and final response were correct. Observability supports evaluation but does not define success.

Evaluation versus reflection

Evaluation measures or judges behavior. Reflection is an agent process that uses feedback to revise work or strategy. An evaluator can score an output without allowing the agent to change it.

Self-evaluation can help, but independent checks are important because the same model may repeat its original mistake.

Common failures

Evaluating only final text hides unsafe or inefficient trajectories. Weak rubrics produce inconsistent scores. Synthetic test cases may not reflect real usage, while model graders can be biased or unreliable.

Builders should combine multiple signals, calibrate automated graders, preserve human-reviewed examples, and track regressions over time.

Why it matters

Evaluation turns vague claims of “agent quality” into measurable requirements. It supports model changes, prompt updates, tool design, and release decisions.

The goal is not one universal score. It is evidence that the agent meets the quality, safety, and cost needs of its actual task. See [Reflection in AI Agents](/reflection-in-ai-agents/) for how evaluation feedback can drive revision.