Lesson 2089 of 2116
AI Agent Evaluation Harnesses: Beyond Pass/Fail
How to build eval suites that catch agent regressions across capability, safety, and cost.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2trajectory eval
- 3cost regression
- 4safety probes
Concept cluster
Terms to connect while reading
Section 1
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
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