Lesson 809 of 1596
Evaluating Multi-Step Agent Quality
Multi-step agent quality requires trajectory-level evaluation. Step accuracy isn't enough.
Creators · Agentic AI · ~7 min read
The premise
Multi-step agent quality emerges across trajectories; step accuracy misses the actual outcome.
What AI does well here
- Evaluate task completion at trajectory level
- Score trajectory quality (was the path reasonable)
- Compare to human-judgment ground truth
- Track quality as system updates
What AI cannot do
- Substitute step accuracy for trajectory quality
- Eliminate human judgment in evaluation
- Predict trajectory quality from training 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 multi-step in plain language, then underline anything that sounds uncertain or too broad.
- 2Give it one detail from "Evaluating Multi-Step Agent Quality" and ask for two possible next steps plus one reason each step might be wrong.
- 3Check trajectory eval 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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