Lesson 1569 of 1596
AI Agent Evaluation Harnesses: Beyond Pass/Fail
How to build eval suites that catch agent regressions across capability, safety, and cost.
Creators · Agentic AI · ~7 min read
The premise
AI agent eval requires measuring not just final answers but trajectories — tool sequences, token costs, latency, and recovery behavior — across canonical task suites.
What AI does well here
- Producing trace logs of every tool call and reasoning step
- Following test scenarios with deterministic seeds when configured
- Reporting structured success/failure indicators per subtask
- Replicating prior runs when given identical inputs
What AI cannot do
- Generate genuinely adversarial test cases against itself
- Self-evaluate without bias toward its own outputs
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 trajectory eval in plain language, then underline anything that sounds uncertain or too broad.
- 2Give it one detail from "AI Agent Evaluation Harnesses: Beyond Pass/Fail" and ask for two possible next steps plus one reason each step might be wrong.
- 3Check cost regression 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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