Agent Quality Evaluation: Beyond Single-Step Accuracy

End-of-lesson check

10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-agentic-agent-quality-evaluation-creators

1. What is the main idea of "Agent Quality Evaluation: Beyond Single-Step Accuracy"?

   1. Single-step accuracy doesn't measure agent quality. Trajectory quality, task-completion rate, and human-judgment matching do.
   2. Use AI as the final authority for the whole decision
   3. Avoid checking the answer once it sounds polished
   4. Focus only on speed instead of judgment

2. Which concept is most central to "Agent Quality Evaluation: Beyond Single-Step Accuracy"?

   1. trajectory quality
   2. agent evaluation
   3. task completion
   4. unrelated shortcut

3. Which use of AI fits this topic best?

   1. Substitute step accuracy for trajectory quality
   2. Let the AI decide what matters without your review
   3. Evaluate task-completion rate (did the agent finish what was asked)
   4. Use the answer before checking whether it fits the situation

4. Which limitation should you watch for in this topic?

   1. Evaluate task-completion rate (did the agent finish what was asked)
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Substitute step accuracy for trajectory quality

5. What should a careful learner remember about "Agent quality evaluation design"?

   1. Use "Agent quality evaluation design" as a reminder to verify the AI output before anyone relies on it.
   2. Skip the context so the tool can guess faster
   3. Treat the output as private even after sharing it online
   4. Use the answer without checking the source

6. You want to use AI after this lesson. What is the safest next step?

   1. Act immediately because the AI answer is written clearly
   2. Use AI for drafting and comparison, but verify before publishing or relying on it.
   3. Hide uncertainty so the final answer looks cleaner
   4. Use private or sensitive details before checking permission

7. How should AI output about agent evaluation be treated?

   1. As proof that no other source is needed
   2. As a replacement for context, consent, or expert review
   3. As a draft or helper output that still needs human judgment and verification
   4. As something that becomes correct when it sounds confident

8. Name one way to verify an AI answer about agent evaluation.

9. Which action would help you apply "Agent Quality Evaluation: Beyond Single-Step Accuracy" responsibly?

   1. Eliminate the human-judgment component of evaluation
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Evaluate trajectory quality (was the path reasonable)

10. Which choice is a bad use of AI for this lesson?

    1. Eliminate the human-judgment component of evaluation
    2. Evaluate task-completion rate (did the agent finish what was asked)
    3. Ask for a plain-language explanation of trajectory quality
    4. Compare the answer with a trusted source