Product design question

Ghostwriter can draft flows, prompts, and tool usage, while Explorer can surface failure patterns and suggest fixes. How would you decide which actions should be AI-assisted, which should require explicit user approval, and which should stay fully manual? Explain the framework you would use, including reversibility, confidence, auditability, and trust tradeoffs.

Practice this question out loud. An AI interviewer asks it, follows up like a real interviewer would, and scores your answer. Type or speak.

What this question tests

Ability to design a graduated autonomy framework for AI assisted building tools based on risk and reversibility, not a blanket automation decision.

Define the framework's axes explicitly: reversibility of the action, model confidence in the suggestion, need for an audit trail, and how much user trust the action requires before it feels safe to automate.
Score draft actions, for example drafting a new flow or prompt, as high reversibility since nothing goes live yet, which argues for AI assisted generation with the user reviewing before anything is published.
Score fix suggestions similarly, surfacing a failure pattern is safe to fully automate, but applying a fix to a live customer facing agent is lower reversibility and should require explicit user approval.
Reserve fully manual control for actions with high consequence and low model confidence, for example changes to policy sensitive behavior or anything affecting compliance.
Require an audit log for every AI suggested and AI applied change regardless of tier, since trust depends on being able to trace what changed and why.
Build in a feedback loop where approval or rejection patterns from users retrain confidence thresholds over time, potentially promoting a category from approval required to more automated as trust is earned.

Applies the four named factors, reversibility, confidence, auditability, trust, consistently across both drafting and fix suggesting tools rather than treating them as separate problems.
Distinguishes suggesting a fix, safe to automate, from applying it live, needs approval, a concrete and defensible split.
Proposes auditability as a constant requirement across all tiers, not just the manual one.
Describes a feedback loop that could shift a capability toward more automation as trust is earned, showing the framework evolves.

Treating AI generated suggestions as uniformly safe to automate just because they came from the model.
No mention of an audit trail, which is critical for trust in an enterprise support context.

What would move a currently approval required action into the fully automated tier?
How would you handle a customer who wants more automation than your framework recommends for their risk tolerance?