Agent evaluation
The practice of measuring whether an AI agent reliably completes its tasks correctly, safely, and efficiently across realistic multi-step scenarios.
In more detail
Evaluating an agent is harder than scoring a single model output. An agent's success depends on a whole trajectory of steps, tool calls, and decisions, and the same goal can be reached by different valid paths. Evaluation has to consider not just the final result but whether the steps were correct, safe, and efficient along the way.
Practical agent evaluation combines several methods: test cases with known-good outcomes, checks on individual steps, monitoring of real runs, and human review of samples. Because agents act on real systems, evaluation also has to cover safety, such as whether the agent respected its scope and escalated when it should have.
Where this shows up at Ceven
Ceven's audit trail is a foundation for evaluating agent behavior: because every step and action is recorded, a run can be reviewed after the fact to check what the agent did and why. Combined with human-approval gates that surface consequential steps for review, this makes an agent's behavior observable rather than a black box.