# Tool-use Evaluations

> Tool selection, argument correctness, sequencing, recovery, side-effect safety, and end-state verification.

Canonical: https://learn.significanthobbies.com/curriculum/concepts/tool-use-evaluations

Tool selection, argument correctness, sequencing, recovery, side-effect safety, and end-state verification.

- Difficulty: core
- Tracks: Evaluation & AI Reliability

## Mental model

Tool-use evals grade the entire trajectory: correct tool, valid arguments, efficient sequence, safe handling of errors, and verified final state. Scope: this card owns grading the TRAJECTORY — tool choice, argument correctness, sequencing, error recovery, and verified end state. Grading the final text is `llm-evals`; the SWE-bench-style environment-plus-verifier setup is `coding-agent-benchmarks`.



## Primary sources

- [τ-bench: A Benchmark for Tool-Agent-User Interaction](https://arxiv.org/abs/2406.12045) (paper)

## Practice

### Design exercise: Tool-use Evaluations

Tool selection, argument correctness, sequencing, recovery, side-effect safety, and end-state verification. Implement designOutline() returning non-empty values for: toolChoice, trajectory, endState. Each value must name a concrete mechanism or decision.

**Expected evidence:** A design outline with toolChoice, trajectory, endState plus an explicit failure mode or trade-off.

## Review prompts

- Why does grading only the final answer under-measure a tool-using agent?

## Build evidence

- **Synthesize: Evaluation & AI Reliability** — Build an evidence-backed evaluation and observability system for models, tools, and agents. Produce one working system, benchmark, or evidence-backed design that integrates the path.

## Prerequisites

- [Coding-agent Benchmarks](https://learn.significanthobbies.com/curriculum/concepts/coding-agent-benchmarks)

## Related concepts

- [Coding-agent Benchmarks](https://learn.significanthobbies.com/curriculum/concepts/coding-agent-benchmarks)
- [Prompt & Version Logging](https://learn.significanthobbies.com/curriculum/concepts/prompt-versioning)

## Learning paths

- [12-Week Evaluation & AI Reliability](https://learn.significanthobbies.com/curriculum/roadmaps/ai-reliability-12w)