Accountability for agent code
Your org wants to formalize who is accountable when AI-authored code fails in production. Today, blame is fuzzy: an engineer ran the agent, skimmed the diff, and merged — and when it broke, the post-incident discussion devolved into 'the AI wrote it.' As the staff engineer, define the ownership and merge model for AI contributions across the team. Where does accountability sit, how does that change what 'review' must mean for agent code, and what perverse incentive do you have to design against?
merge_gate(risk_areas: list[str], diff_lines: int, reviewer_can_explain: bool, automated_gates_green: bool) → str[["ui"],50,false,true]out"merge:light_review"[["security"],50,false,true]out"block:needs_comprehension"[["money"],50,true,true]out"merge:deep_review"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 & agentic: describe the solution in plain language (or narrate it) and the coach grades your approach.