Agent evals for tool-using AI systems
Quick answer
Section titled “Quick answer”Tool-using agents should not be evaluated like ordinary chatbots. A good eval must check:
- whether the agent chose the right tool,
- whether it called the tool with the right arguments,
- whether it escalated or paused when it should,
- and whether the final outcome was correct.
If you only score the final answer, you will miss the most expensive failures.
Why this is different from ordinary prompt evals
Section titled “Why this is different from ordinary prompt evals”A tool-using agent can fail in several distinct ways:
- wrong plan,
- wrong tool,
- wrong arguments,
- wrong order,
- missing approval,
- or wrong final answer.
That means one top-line “accuracy” score hides too much.
What the eval should measure
Section titled “What the eval should measure”A healthy eval set usually measures:
- plan correctness,
- tool selection correctness,
- tool-argument correctness,
- approval or escalation correctness,
- final outcome quality.
These layers expose whether the agent is reliable for the right reasons.
The failure that matters most
Section titled “The failure that matters most”The worst production failures are often not bad text. They are:
- an unnecessary write action,
- a missed approval gate,
- a wrong external search path,
- or a bad internal mutation triggered with high confidence.
That is why eval design must reflect operational risk classes, not only benchmark style.
The practical eval design rule
Section titled “The practical eval design rule”For every tool-using workflow, define:
- the valid tool set,
- the forbidden tool paths,
- the approval-required boundaries,
- and the acceptable final states.
Then test those directly.
Implementation checklist
Section titled “Implementation checklist”Your agent evals are probably healthy when:
- tool choice is measured explicitly;
- approval behavior is scored;
- final answer quality is only one layer of the rubric;
- regression tests include real failure modes from production;
- and the team can identify whether a failure came from planning, tool use, or execution.