AI Agent Interview Questions
Questions on products built with AI agents: what an agent should be allowed to do, how to judge its success, when a person must approve its actions, and how to recover when it gets something wrong. Asked of AI engineers and AI product managers working on agents. 87 questions across 4 subtopics, each with a model answer.
AI Agent Product Management
How products built on AI agents differ: scoping what an agent may do, defining success beyond task completion, and evaluating the path an agent took rather than only its final answer. Asked of AI PMs and AI engineers building agents.
- What product decisions are unique to an agent versus a single-turn AI feature?
- How do you scope what an agent is allowed to do?
- Describe the permission model you would design for an agent acting in a user's account.
- What does success look like for an agent, and why is task completion insufficient?
- How do you evaluate an agent's trajectory rather than its final answer?
- Explain the product implications of an agent that takes 40 steps instead of 4.
- Design the interruption and takeover experience for a running agent.
- What should an agent do when it is uncertain mid-task?
Human in the Loop Design
When a person should review the AI's work, how to decide which cases go to a human, why review fatigue breaks the design, and how to build approval flows for agents.
- When should a human be required to approve an AI action rather than merely able to?
- Design the review interface for a human checking 200 AI-generated outputs an hour.
- Explain how review fatigue undermines a human-in-the-loop design.
- What is the difference between human-in-the-loop and human-on-the-loop?
- How do you decide which cases get routed to a human?
- Describe a confidence-based routing policy and its failure mode.
- How do you measure whether the human in the loop is adding value?
- Design an approval flow for an agent that sends external emails.
Designing for AI Failure
What the product should do when the model returns nothing useful, how to fall back to a non-AI version instead of breaking, and how to help a user recover after an agent takes a wrong action.
- What should happen in the UI when the model returns nothing usable?
- Design the fallback experience for an AI feature when the provider is down.
- Explain the difference between failing loudly and failing silently, and which you prefer.
- How do you design a feature that degrades to a non-AI version rather than breaking?
- Describe three failure modes to design for before launch.
- What error message would you write for a model timeout, and what would you avoid saying?
- How should the product behave when the model produces output that fails a safety filter?
- Design the recovery path for a user whose agent took a wrong action.
AI Feasibility and Technical Spikes
How to find out quickly whether an AI idea can work: scoping a spike, why curated data lies, and how feasibility checks differ for agents. These questions test practical judgement before money is spent.
- What is a technical spike and how do you scope one for an AI feature?
- Design a two-week spike to test whether an AI feature is viable.
- What question should a feasibility spike answer, and what should it explicitly not try to answer?
- How do you prevent a spike from silently becoming the production implementation?
- Describe the smallest test that would tell you whether retrieval quality is the blocker.
- What does a good spike report contain?
- How many examples do you need before a feasibility judgement is trustworthy?
- Your spike produces 70 percent accuracy. How do you decide whether that is promising or fatal?