Agentic AI: Build Evals That Catch Loop and Tool-Misuse Failures

End-of-lesson check

15 questions · take it digitally for instant feedback at tendril.neural-forge.io/learn/quiz/end-agentic-evals-for-agent-loops-r8a1-creators

1. What is the primary focus of trajectory evaluation in agentic AI systems?

   1. Evaluating the complete sequence of tool calls and intermediate actions, not just the final output
   2. Checking whether the agent's code follows best practices
   3. Measuring how satisfied a user feels after the agent completes a task
   4. Counting the total number of tokens the agent generates

2. An AI agent produces the correct final answer but uses 40 unnecessary tool calls and fails to mention a key sub-goal to the user. What does this scenario demonstrate about agent evaluation?

   1. The agent is behaving optimally because it got the right answer
   2. User satisfaction surveys would catch this problem
   3. Standard answer-quality tests would miss these agent-specific failures
   4. This agent is actually better than one that uses fewer tools

3. Which of the following can AI evaluation tools directly measure in agent trajectories?

   1. Whether the user is satisfied with the agent's personality
   2. How creatively the agent approaches problems
   3. If the agent is showing appropriate emotional intelligence
   4. Tool-call efficiency and whether similar tools are called repeatedly

4. What is required to ensure an LLM-as-judge remains a reliable evaluator over time?

   1. Having it evaluate the same samples multiple times
   2. Regularly comparing its grades to human judgment and recalibrating when agreement drops
   3. Training it on increasingly larger datasets
   4. Giving it access to more sophisticated tools

5. What fundamental limitation does an LLM-as-judge have when evaluating agent trajectories?

   1. It can only detect problems that were explicitly defined in the evaluation rubric
   2. It cannot understand natural language explanations
   3. It fails when there are too many tool calls
   4. It always gives perfect scores to attractive agents

6. Why is it important for trajectory evaluation rubrics to be reusable across different agent runs?

   1. They are required by most AI safety regulations
   2. They prevent the agent from using too many tools
   3. They enable consistent scoring and fair comparison between different model versions
   4. They automatically fix any bugs found in the agent

7. A trajectory evaluation detects that an agent repeatedly calls the same tool with identical inputs. What problem has been identified?

   1. A successful optimization strategy
   2. An appropriate use of multiple problem-solving approaches
   3. A user request for redundant information
   4. A potential loop or dead-end retry pattern wasting resources

8. When comparing two different agent model versions using trajectory evaluation, what should evaluators primarily compare?

   1. How efficiently each version completes tasks and handles subgoals
   2. The number of engineers who built each version
   3. The programming language each version uses
   4. How much each version costs to run

9. What does it mean for an agent to "silently abandon" a subgoal?

   1. The agent runs out of available tools
   2. The agent asks the user for clarification
   3. The agent completes all subgoals and reports success
   4. The agent stops pursuing an intermediate objective without notifying the user

10. Why can't AI evaluation completely replace production monitoring for AI agents?

    1. AI evaluation doesn't work with real-time data
    2. AI evaluation is too expensive for production use
    3. Production monitoring is illegal in most countries
    4. Production monitoring captures real-world issues that evals cannot anticipate or measure

11. What constitutes a meaningful "signal" in LLM-as-judge evaluation?

    1. Disagreement between the LLM judge and human judgment on the same runs
    2. The LLM judge uses a detailed rubric
    3. Perfect agreement between the LLM judge and itself on repeated evaluations
    4. High scores across all evaluated agent runs

12. What is "tool efficiency" in the context of agent trajectory evaluation?

    1. Evaluating how expensive each tool is to run
    2. Checking if tools are called in alphabetical order
    3. Counting how many tools are available to the agent
    4. Measuring whether agents use the minimum necessary tools to complete tasks

13. An LLM-as-judge evaluates the same 20 agent runs quarterly and finds its grades increasingly diverge from human judgment. What should be done?

    1. Recalibrate the judge to restore alignment with human judgment
    2. Add more questions to the evaluation rubric
    3. Ignore the human judgment and trust the LLM
    4. Replace the LLM entirely with a rules-based system

14. What does "dead-end retry" mean in agent trajectory analysis?

    1. The agent stops working completely
    2. The agent returns to a previous successful state
    3. The agent repeatedly attempts the same failed approach without success
    4. The agent correctly identifies an unsolvable problem

15. Why must trajectory evaluation check for "wasted tools" in agent runs?

    1. Wasted tools are the only way to measure agent intelligence
    2. It's required by AI copyright law
    3. Redundant or unnecessary tool calls indicate inefficient agent behavior that wastes computational resources
    4. Wasted tools cause legal liability issues