Lesson 1146 of 1596
Tool-Use Evaluation: Building Reliable Agent Benchmarks
Tool-use evals must capture argument correctness, sequencing, and recovery from tool errors — not just whether the model called the tool at all.
Creators · AI Foundations · ~24 min read
The premise
AI can design tool-use eval suites that score argument correctness and recovery, but engineering must integrate them into CI.
What AI does well here
- Generate tool-use eval scenarios across success, partial-success, and failure paths.
- Draft argument-correctness scoring rubrics.
What AI cannot do
- Decide what error-recovery quality is acceptable.
- Replace human review of edge-case behaviors.
Key terms in this lesson
Practice this safely
Use a small project example from your own work. The useful move is to compare the AI's draft against your goal, sources, and constraints before you trust it.
- 1Ask AI to explain function calling in plain language, then underline anything that sounds uncertain or too broad.
- 2Give it one detail from "Tool-Use Evaluation: Building Reliable Agent Benchmarks" and ask for two possible next steps plus one reason each step might be wrong.
- 3Check error recovery against a trusted source, teacher, adult, expert, or original document before you use it.
End-of-lesson quiz
Check what stuck
10 questions · Score saves to your progress.
Tutor
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