What are agent evaluations?
Agent evaluations test whether a model-driven system completes representative tasks within acceptable behavioral boundaries. They can examine the outcome, tool choices, permissions, and intermediate evidence. A useful evaluation set includes realistic success cases, failure cases, and situations where the correct action is to stop or ask.
What does the source say?
Anthropic’s evaluation guide discusses assessing agents across multi-turn tasks and tool interactions. Evaluating the final sentence alone can miss mistakes in the actions that produced it.
How can you apply this?
Build a small set of anonymized tasks from a real workflow. For each, define an acceptable result, one prohibited action, and the evidence needed to judge the run. Include an ambiguous request and a missing-permission case. Re-run the set when you change a prompt, tool, model, or context source, and compare failures individually. Keep the examples available to future reviewers so a model upgrade cannot quietly redefine what the team considers acceptable.
What should you watch out for?
An average score can hide a severe boundary violation. Keep release-blocking checks separate from quality improvements that can tolerate gradual progress.