Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors

End-of-lesson check

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

1. What is the core idea behind "Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors"?

   1. Agent behaviors emerge from multi-step interactions; unit tests on individual tools miss the failures that matter. Real evaluation requires task-completion harnesses with tracing and human review.
   2. AI could research three nearby museums to pick from.
   3. Self-impose budgets without enforcement in code
   4. Pet decisions: AI summarizes the responsibilities everyone signed up for.

2. Which term best describes a foundational idea in "Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors"?

   1. task completion
   2. agent evaluation
   3. tracing
   4. trajectory analysis

3. A learner studying Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors would need to understand which concept?

   1. agent evaluation
   2. tracing
   3. task completion
   4. trajectory analysis

4. Which of these is directly relevant to Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors?

   1. agent evaluation
   2. task completion
   3. trajectory analysis
   4. tracing

5. Which of the following is a key point about Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors?

   1. Build task-completion harnesses with realistic input distribution and ground-truth outcomes
   2. Implement tracing that captures the full trajectory (prompts, tool calls, tool outputs, model decisi…
   3. Use trajectory analysis to identify common failure modes (loops, premature stopping, wrong tool sele…
   4. Maintain regression test suites that catch behavior degradation across model updates

6. Which of these does NOT belong in a discussion of Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors?

   1. Build task-completion harnesses with realistic input distribution and ground-truth outcomes
   2. AI could research three nearby museums to pick from.
   3. Implement tracing that captures the full trajectory (prompts, tool calls, tool outputs, model decisi…
   4. Use trajectory analysis to identify common failure modes (loops, premature stopping, wrong tool sele…

7. Which statement is accurate regarding Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors?

   1. Catch novel failure modes the test set doesn't cover
   2. Replace production monitoring (eval-set performance ≠ production performance)
   3. Substitute for human review on high-stakes trajectories
   4. AI could research three nearby museums to pick from.

8. What is the key insight about "Agent eval harness design" in the context of Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors?

   1. AI could research three nearby museums to pick from.
   2. Self-impose budgets without enforcement in code
   3. Pet decisions: AI summarizes the responsibilities everyone signed up for.
   4. Design an evaluation harness for [agent]. Cover: (1) task set composition (realistic input distribution + adversarial ca…

9. What is the key insight about "Evals don't predict production" in the context of Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors?

   1. Evaluation set performance gives confidence — it doesn't predict production.
   2. AI could research three nearby museums to pick from.
   3. Self-impose budgets without enforcement in code
   4. Pet decisions: AI summarizes the responsibilities everyone signed up for.

10. Which statement accurately describes an aspect of Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors?

    1. AI could research three nearby museums to pick from.
    2. Agent quality emerges across trajectories, not within individual steps; evaluation must span trajectories.
    3. Self-impose budgets without enforcement in code
    4. Pet decisions: AI summarizes the responsibilities everyone signed up for.

11. Which best describes the scope of "Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors"?

    1. It is unrelated to agentic workflows
    2. It applies only to the opposite beginner tier
    3. It focuses on Agent behaviors emerge from multi-step interactions; unit tests on individual tools miss the failure
    4. It was deprecated in 2024 and no longer relevant

12. Which section heading best belongs in a lesson about Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors?

    1. AI could research three nearby museums to pick from.
    2. Self-impose budgets without enforcement in code
    3. Pet decisions: AI summarizes the responsibilities everyone signed up for.
    4. What AI does well here

13. Which section heading best belongs in a lesson about Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors?

    1. What AI cannot do
    2. AI could research three nearby museums to pick from.
    3. Self-impose budgets without enforcement in code
    4. Pet decisions: AI summarizes the responsibilities everyone signed up for.

14. Which of the following is a concept covered in Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors?

    1. task completion
    2. agent evaluation
    3. tracing
    4. trajectory analysis

15. Which of the following is a concept covered in Agent Evaluation Harnesses: Beyond Unit Tests for Multi-Step Behaviors?

    1. agent evaluation
    2. tracing
    3. task completion
    4. trajectory analysis