Tool-Use Evals for LLM Agents

Score actions as state transitions

Tool-use evals measure the full action sequence: whether an agent selects an appropriate tool, supplies valid arguments, interprets the result, recovers from failure, and avoids prohibited side effects. This page owns one search job: measure tool selection, arguments, and recovery behavior. It does not promise a universal product ranking, an undisclosed benchmark, or hands-on results that are not present in the evidence ledger.

Give developers a source-led, reproducible answer for how to measure tool selection, arguments, and recovery behavior, with explicit version and stop conditions. In practice, that means separating documented behavior from inference, naming the consequence of being wrong, and defining the evidence that would change the decision.

For the tool use evaluation llm decision, the surrounding Agent Evals: Trajectories, Tools, and Outcomes guide defines the nearest architectural boundary and prevents this page from absorbing a broader search job.

Specify tools, policy, and final state

A test fixture should define available tool schemas, initial state, expected state transition, allowed alternative paths, injected failures, prohibited calls, and an auditable event trace. The artifact should be portable enough for another engineer to inspect without relying on a private chat transcript or the memory of the person who ran it.

For tool use evaluation llm, the source ledger uses current first-party material from OpenAI and OpenAI to define documented concepts and interfaces. Those sources do not prove performance on this site's hypothetical setup, so every comparative or operational conclusion remains tied to the recorded artifact and a local verification step.

After the tool use evaluation llm evidence is recorded, use Coding Agent Evals: Repository Tasks That Reproduce; it covers the adjacent implementation handoff without duplicating the protocol here.

Exercise success and recovery paths

The order matters for tool use evaluation llm. Starting with tooling or a score before the evidence boundary is defined makes later results hard to interpret. Keep each step small enough that its input, authority, output, and failure state can be reviewed independently.

1. Define the goal and state transition independently from one preferred tool-call sequence.
2. Expose only the tools and credentials the scenario permits, with deterministic mock responses where possible.
3. Run successful, denied, malformed, delayed, and partial-result paths while recording every call and argument.
4. Score outcome, policy compliance, unnecessary actions, and recovery separately before aggregating anything.

Record the exact tool use evaluation llm configuration and environment beside the artifact, but do not invent a version number in evergreen copy. At execution time, pin the tested release, preserve command output or trace evidence, and stop when the next action requires new authority or an unverifiable assumption.

FIG. 01 / Process map

Tool-use state transition

Do not grade only the final sentence

For tool use evaluation llm, the architecture flags three recurring risks for this family: metrics are chosen before error analysis, an LLM judge is not calibrated, and the evaluation dataset leaks or drifts. They are not abstract caveats; each can make a polished result unusable for the decision this page owns.

• Exact-sequence scoring can penalize a safe alternative path even when the final state is correct.
• Happy-path mocks hide authentication, timeout, partial-write, and idempotency failures found in production.
• A final-answer grader can miss data exposure or an unnecessary write that occurred earlier in the trace.

Treat a tool use evaluation llm failure label as the start of investigation, not as an explanation. Preserve the case, identify which evidence or control was missing, and rerun one changed condition at a time. That discipline separates a tool limitation from a bad task definition, weak context, an unsafe permission, or a broken test harness.

Verify selection, arguments, and recovery

Verification for “measure tool selection, arguments, and recovery behavior” needs a stopping rule that another engineer can apply. The checks below favor direct artifacts and observable state over confidence, verbosity, or vendor reputation. A failed check keeps the conclusion provisional even when the generated output appears convincing.

Run the tool use evaluation llm gate against both an expected success and at least one denied, malformed, or recovery path. Store disagreements and residual risk beside the result. If the evidence cannot distinguish a system failure from an evaluation failure, improve the instrument before using its score to approve a release.

When the tool use evaluation llm gate exposes a neighboring problem, continue with LLM Evals: Build Evidence Before Metrics and carry this page's evidence record into that step.

FIG. 02 / Decision aid

Tool-call acceptance gate

Gate only bounded scenarios

Promote a scenario to a release gate when its state, policy, and recovery expectations are explicit. Keep open-ended exploratory tasks outside a binary pass rate until acceptable paths can be bounded. This rule applies to the documented search job, not to every use of tool use evaluation llm. A different repository, data boundary, model, tool set, or consequence requires a new dated check.

End the tool use evaluation llm record with the owner, next review trigger, and one of four outcomes: proceed within the tested boundary, reduce scope, gather missing evidence, or reject the approach. This preserves a useful negative result and prevents scheduled editorial copy from implying an experiment that was never run.