Logging Agent Runs So You Can Debug Them Later

End-of-lesson check

10 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-agentic-observability-r12a1-creators

1. What is the main idea of "Logging Agent Runs So You Can Debug Them Later"?

   1. Capture decisions, tool inputs, and outputs in a replayable log.
   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 "Logging Agent Runs So You Can Debug Them Later"?

   1. trace
   2. logging
   3. replay
   4. unrelated shortcut

3. Which use of AI fits this topic best?

   1. Tell you which step was 'wrong' without your judgment.
   2. Let the AI decide what matters without your review
   3. Emit a structured event per tool call (input, output, latency).
   4. Use the answer before checking whether it fits the situation

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

   1. Emit a structured event per tool call (input, output, latency).
   2. Explain the topic in plain language
   3. Organize a draft for human review
   4. Tell you which step was 'wrong' without your judgment.

5. What should a careful learner remember about "Trace event schema"?

   1. Each step logs: {run_id, step_n, tool, input, output, status, latency_ms, model_version}. Store in append-only storage.
   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 logging 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 logging.

9. Which action would help you apply "Logging Agent Runs So You Can Debug Them Later" responsibly?

   1. Log information that was never captured at runtime.
   2. Use the tool to avoid thinking through the tradeoff
   3. Keep going even if the output conflicts with a trusted source
   4. Reconstruct a session from the event log alone.

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

    1. Log information that was never captured at runtime.
    2. Emit a structured event per tool call (input, output, latency).
    3. Ask for a plain-language explanation of trace
    4. Compare the answer with a trusted source