After a model upgrade, how would you decide which agent-harness rules to remove?

Instruction: Design a controlled ablation of planning, reset, and review rules. Explain task-level acceptance criteria, interaction effects, and enforcement boundaries.

Context: Evaluate agent-harness simplification after a model upgrade using comparable task runs, failure analysis, and a reversible rollout.

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I'd write down the failure each rule is supposed to prevent, then test the upgraded model with the current harness as a baseline. That separates improvements from the model upgrade from improvements caused by removing a rule...

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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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