Product design question
Codex needs a graduated authority model so low-risk actions like local read-only work with minimal friction, while actions involving production systems, secrets, or irreversible changes require stronger controls. How would you define the permission tiers, approval triggers, and exception paths, and what principles would you use to keep developers in-flow rather than driving them to bypass the product?
- OpenAI
- Product design
- Hard
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What this question tests
Tests designing a tiered permission system for an AI coding agent that scales friction with risk, while keeping developers in flow rather than pushing them to bypass controls.
How to approach it
- Define risk tiers based on action type and reversibility: local read-only and non-destructive edits as low risk, writes to shared or production systems as medium, and anything touching secrets or irreversible changes (deletions, deploys) as high risk.
- Set default behavior per tier: low risk runs with no friction, medium risk requires a lightweight in-flow confirmation, high risk requires explicit approval with full context on what will happen.
- Design approval triggers based on the actual action requested, not just the tool being used, since the same tool can be low or high risk depending on the target.
- Build exception paths: a pre-approved allowlist for specific, trusted repeated actions, so developers aren't re-approving the same safe operation constantly.
- Design the friction itself to stay in-flow: fast, inline approval prompts with clear context, not a context-switch to a separate approval system that breaks concentration.
- Monitor for bypass behavior, for example developers running actions outside the tool to avoid approval friction, as a signal the tiers or triggers are miscalibrated.
What a strong answer includes
- Ties risk tiers to the specific action and its reversibility, not just the tool, since the same tool can span multiple risk levels.
- Builds an allowlist exception path for trusted repeated actions, explicitly designed to prevent approval fatigue from becoming a bypass incentive.
- Keeps high-friction approval in-flow, fast and inline, recognizing that a clunky approval step is what actually drives bypass behavior.
- Proposes monitoring for bypass behavior itself as a feedback signal to recalibrate the tiers over time.
Common mistakes
- Tying risk tiers to the tool alone instead of the specific action and its context, over- or under-gating in practice.
- Making approval friction so heavy that developers find workarounds outside the controlled tool entirely.
- Skipping an allowlist mechanism, causing repeated unnecessary approvals for genuinely safe, routine actions.
Likely follow-up questions
- How would you detect that developers are bypassing the controls?
- What would you do if a trusted allowlist action turns out to be riskier than expected?
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Learn the skill behind it
Chapters of the AI PM course that teach what this question tests.
- Chapter 4: Discovery and strategy for AI products
- Chapter 7: AI UX and human oversight: design for a system that is wrong sometimes
- Chapter 14: Get the job: the AI PM interview loop