Evaluation & AI Reliability · core
Tool-use Evaluations
Tool selection, argument correctness, sequencing, recovery, side-effect safety, and end-state verification.
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`.
How to study Tool-use Evaluations
Begin by restating the mental model in your own words, then connect it to a concrete system you have built or operated. Name the mechanism, the constraint it addresses, and the trade-off it introduces. Use τ-bench: A Benchmark for Tool-Agent-User Interaction to check details, but close the source before writing your explanation. Retrieval is the learning step; rereading is only preparation.
Next, compare Tool-use Evaluations with Coding-agent Benchmarks, Prompt & Version Logging. Ask what changes in correctness, latency, resource use, operability, and failure recovery. Complete Design exercise: Tool-use Evaluations and preserve the command, input, output, and one failed attempt as evidence. Finish by explaining the idea without jargon to someone who has not studied the track.
Proof of understanding
- Explain the mechanism from first principles and identify the state it reads or changes.
- Give one situation where the concept is the right choice and one where it is not.
- Predict a realistic failure mode before running the drill, then compare the prediction with evidence.
- Connect the result to a roadmap or build artifact instead of treating the concept as isolated trivia.
Learn from primary sources
Practice and explain it back
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.
Open the interactive drill →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.
- Implements or precisely models the core mechanisms from all three milestones
- Includes at least one injected failure or adversarial case and demonstrates recovery
- Reports quality, latency, resource, reliability, or usability measurements relevant to the domain
- Ships a concise architecture note explaining decisions, trade-offs, and remaining risks