Lesson 719 of 1596
Agent Quality Evaluation: Beyond Single-Step Accuracy
Single-step accuracy doesn't measure agent quality. Trajectory quality, task-completion rate, and human-judgment matching do.
Creators · Agentic AI · ~7 min read
The premise
Agent quality requires trajectory-level evaluation; step-by-step accuracy misses the actual outcome.
What AI does well here
- Evaluate task-completion rate (did the agent finish what was asked)
- Evaluate trajectory quality (was the path reasonable)
- Compare to human-judgment ground truth on representative tasks
- Track quality over time as system updates
What AI cannot do
- Substitute step accuracy for trajectory quality
- Eliminate the human-judgment component of evaluation
- Predict trajectory quality from training data alone
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 agent evaluation in plain language, then underline anything that sounds uncertain or too broad.
- 2Give it one detail from "Agent Quality Evaluation: Beyond Single-Step Accuracy" and ask for two possible next steps plus one reason each step might be wrong.
- 3Check trajectory quality 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.
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