Question
Your team's PR volume has tripled since adopting AI agents, but review quality is collapsing: reviewers are drowning, approving large AI-generated diffs with cursory looks, and a few quiet bugs have shipped. One faction wants to cap AI usage; another wants to ship faster and 'trust the tools.' As the senior engineer, you're asked to propose how the team should adapt its review and authoring norms so AI raises throughput without eroding the quality bar. What do you propose, and what's the core tension you have to manage?
Treat the AI’s output as a draft to verify, not an answer to trust. Name the specific flaw and the input that triggers it, say how you’d catch it — tests, edge cases, reading critically — and how you’d re-prompt or decompose to get it right.
Vibe coding: describe the solution in plain language (or narrate it) and the coach grades your approach. Generating runnable code from your description is coming next.