Agentic AI: building an eval harness before scaling the agent

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-agentic-eval-harness-r7a1-creators

1. What is the core idea behind "Agentic AI: building an eval harness before scaling the agent"?

   1. A frozen set of input scenarios with graded outcomes is the only way to know if your agent got better or worse with each change.
   2. Agent: 'I'm not 100% sure this is right — please double-check.'
   3. How to remember the rules for using AI agents safely and well.
   4. Logs help you spot mistakes AI made.

2. Which term best describes a foundational idea in "Agentic AI: building an eval harness before scaling the agent"?

   1. regression testing
   2. agent evals
   3. graded scenarios
   4. Agent: 'I'm not 100% sure this is right — please double-check.'

3. A learner studying Agentic AI: building an eval harness before scaling the agent would need to understand which concept?

   1. agent evals
   2. graded scenarios
   3. regression testing
   4. Agent: 'I'm not 100% sure this is right — please double-check.'

4. Which of these is directly relevant to Agentic AI: building an eval harness before scaling the agent?

   1. agent evals
   2. regression testing
   3. Agent: 'I'm not 100% sure this is right — please double-check.'
   4. graded scenarios

5. Which of the following is a key point about Agentic AI: building an eval harness before scaling the agent?

   1. Run against a fixed scenario list when one is provided
   2. Output structured results that map to scenario IDs
   3. Be deterministic enough to score with temperature 0
   4. Agent: 'I'm not 100% sure this is right — please double-check.'

6. What is one important takeaway from studying Agentic AI: building an eval harness before scaling the agent?

   1. Decide what 'pass' means for an open-ended task
   2. Generate the scenarios that matter to your business
   3. Replace human judgment on subjective outputs
   4. Agent: 'I'm not 100% sure this is right — please double-check.'

7. What is the key insight about "Try this minimum viable harness" in the context of Agentic AI: building an eval harness before scaling the agent?

   1. Agent: 'I'm not 100% sure this is right — please double-check.'
   2. How to remember the rules for using AI agents safely and well.
   3. Build a JSON file of 30 scenarios: {id, input, expected_observable_outcome, graded_by: regex|llm-judge|human}.
   4. Logs help you spot mistakes AI made.

8. What is the key insight about "Watch out: eval overfitting" in the context of Agentic AI: building an eval harness before scaling the agent?

   1. Agent: 'I'm not 100% sure this is right — please double-check.'
   2. How to remember the rules for using AI agents safely and well.
   3. Logs help you spot mistakes AI made.
   4. If you tune prompts against the same 30 scenarios forever, you optimize for them and regress on the long tail.

9. Which statement accurately describes an aspect of Agentic AI: building an eval harness before scaling the agent?

   1. Without an eval harness, every prompt change is a vibe-based decision. With one, you can measure whether a model swap or prompt edit actuall…
   2. Agent: 'I'm not 100% sure this is right — please double-check.'
   3. How to remember the rules for using AI agents safely and well.
   4. Logs help you spot mistakes AI made.

10. Which best describes the scope of "Agentic AI: building an eval harness before scaling the agent"?

    1. It is unrelated to agentic workflows
    2. It focuses on A frozen set of input scenarios with graded outcomes is the only way to know if your agent got bette
    3. It applies only to the opposite beginner tier
    4. It was deprecated in 2024 and no longer relevant

11. Which section heading best belongs in a lesson about Agentic AI: building an eval harness before scaling the agent?

    1. Agent: 'I'm not 100% sure this is right — please double-check.'
    2. How to remember the rules for using AI agents safely and well.
    3. What AI does well here
    4. Logs help you spot mistakes AI made.

12. Which section heading best belongs in a lesson about Agentic AI: building an eval harness before scaling the agent?

    1. Agent: 'I'm not 100% sure this is right — please double-check.'
    2. How to remember the rules for using AI agents safely and well.
    3. Logs help you spot mistakes AI made.
    4. What AI cannot do

13. Which of the following is a concept covered in Agentic AI: building an eval harness before scaling the agent?

    1. agent evals
    2. regression testing
    3. graded scenarios
    4. Agent: 'I'm not 100% sure this is right — please double-check.'

14. Which of the following is a concept covered in Agentic AI: building an eval harness before scaling the agent?

    1. agent evals
    2. regression testing
    3. graded scenarios
    4. Agent: 'I'm not 100% sure this is right — please double-check.'

15. Which of the following is a concept covered in Agentic AI: building an eval harness before scaling the agent?

    1. agent evals
    2. regression testing
    3. graded scenarios
    4. Agent: 'I'm not 100% sure this is right — please double-check.'