Design an evaluation loop for an agent that learns from reviewer feedback without silently drifting.

Instruction: Describe how you would use reviewer feedback to improve an agent while keeping behavior stable.

Context: Assesses whether the candidate can design a practical architecture and explain the main tradeoffs. Describe how you would use reviewer feedback to improve an agent while keeping behavior stable.

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I would make learning and evaluation distinct stages. Reviewer feedback can improve prompts, policies, or routing, but those changes should not silently flow into production without passing a benchmark and versioned review process.

So I would capture reviewer feedback as structured...

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Your preparation path

Work through these questions in order. Read the answer aloud, then explain it in your own words.

1. Start with the foundations

Build the vocabulary and explain the core decisions.

  1. When would you use a deterministic workflow instead of an agent? Free sample
  2. What state should persist between agent turns? Free sample
  3. How do you decide whether an agent should ask a human for approval? Free sample

2. Apply it to a real workflow

Practice diagnosis, validation, and everyday tradeoffs.

  1. How would you keep an agent from looping on the same failed action? Free sample
  2. When do multi-agent systems create more risk than value? Member answer
  3. An agent keeps choosing the wrong skill. How would you diagnose and fix its catalog? Member answer

3. Prepare for senior discussions

Explain failure boundaries, recovery, and production choices.

  1. Design an agent that can resume after partial failure without repeating side effects. Member answer
  2. After a model upgrade, how would you decide which agent-harness rules to remove? Member answer
  3. An agent's execution container dies halfway through a task. What survives, and how does it recover? Member answer

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