Lesson 1182 of 2116
Evaluating Multi-Step Agent Quality
Multi-step agent quality requires trajectory-level evaluation. Step accuracy isn't enough.
Lesson map
What this lesson covers
Learning path
The main moves in order
- 1The premise
- 2multi-step
- 3trajectory eval
- 4quality
Concept cluster
Terms to connect while reading
Section 1
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
End-of-lesson quiz
Check what stuck
15 questions · Score saves to your progress.
Tutor
Curious about “Evaluating Multi-Step Agent Quality”?
Ask anything about this lesson. I’ll answer using just what you’re reading — short, friendly, grounded.
Progress saved locally in this browser. Sign in to sync across devices.
Related lessons
Keep going
Creators · 10 min
Agentic AI: Build Evals That Catch Loop and Tool-Misuse Failures
Standard answer-quality evals miss agent-specific bugs; design evals that score loops, wasted tools, and abandoned subgoals.
Creators · 11 min
AI Agent Evaluation Harnesses: Beyond Pass/Fail
How to build eval suites that catch agent regressions across capability, safety, and cost.
Creators · 48 min
Computer Use API: Letting AI Click Through GUIs
Computer Use lets Claude see your screen and use it — mouse, keyboard, apps. The capability is real, the gotchas are real. A hands-on look at what works in 2026.