Evaluating AI Agents Quiz

Answers and explanations appear as you take the quiz.

1. Why is checking a single final answer usually not enough for agents?
   • Agents never produce answers
   • Agents act over many steps and cause side effects, so the path and final state matter
   • Answers are always correct
   • Reference answers cannot be written for agent tasks
2. How does SWE-bench judge whether an agent solved an issue?
   • By running the repository's tests after the agent's patch
   • By counting lines changed
   • By asking the agent if it succeeded
   • By measuring response speed
3. What does a pass^k style metric measure?
   • Speed across k tools
   • Success on at least one of k attempts
   • Whether the agent succeeds on every one of k repeated attempts
   • The number of tokens per step
4. Which dimension would flag an agent that succeeds but takes fifty steps for a five-step job?
   • Task success
   • Safety
   • Tool naming
   • Trajectory quality
5. What is a known weakness of model-based graders?
   • They cannot read text
   • They can be biased, for example toward long or confident answers, so they need checking against human labels
   • They are always slower than humans
   • They only work on images
6. Why reset the environment to a known state before every run?
   • So results are reproducible and runs cannot affect each other
   • To save disk space
   • Because agents require it to start
   • To make tasks harder
7. Why can public leaderboard scores mislead a team?
   • They are always faked
   • Leaderboards never publish numbers
   • Benchmarks can leak into training data, saturate or fail to match your workload
   • They only measure cost
8. How should a safety eval treat a run where the agent finished the task but obeyed a planted instruction to leak data?
   • As a full success
   • As a partial success worth half credit
   • It should be ignored
   • As a failure