Quality & operations
AI Agent Evaluation
Evaluating agents is harder than evaluating a single response — here is how to approach it.
Evaluating a single AI response is already hard. Evaluating an agent is harder still, because you need to judge not just the final output, but the sequence of decisions and tool calls that got there — and whether a different, more efficient path existed.
Without deliberate agent evaluation, teams often discover an agent is unreliable only after it has already taken costly or embarrassing actions in production.
Key takeaways
Why outcome alone is not enough
An agent can reach the correct final answer through an inefficient, expensive, or risky path — calling the wrong tool several times before stumbling onto the right one, for example. Judging only the final output misses this, and that inefficiency often becomes a real cost or reliability problem at scale.
Good agent evaluation looks at both whether the task succeeded and whether the path taken was reasonable, efficient, and safe.
Building a realistic evaluation set
Collect or write a set of realistic tasks that span common cases, tricky edge cases, and cases designed to test failure handling — what happens when a tool returns an error, or the requested information genuinely does not exist.
Score each run on task success, number of steps taken, whether the correct tools were used, and whether the agent recognized and handled failure cases appropriately rather than pushing forward with bad information.
Making evaluation an ongoing practice
Run your evaluation set every time you change the agent’s prompts, tools, or underlying model, not just before initial launch. Agent behavior is sensitive to small changes in ways that are easy to miss without a repeatable evaluation process.
Track trends over time — a gradually declining success rate is a much more useful signal than a single pass/fail check at launch.
Put this into practice