Assess AI delegation suitability
You have two tasks in your sprint: (a) write a Python script that backfills a denormalized `display_name` column across 40M user rows by reading from a `profiles` table, and (b) generate the unit tests for a pure date-formatting utility you already wrote. A teammate suggests handing both to an AI agent end-to-end to save time. For each task, decide whether it's actually suitable for full AI delegation or not, and explain the reasoning that distinguishes them.
ai_delegation_scope(mutates_production: bool, reversible: bool, rows_affected: int, verify_minutes: int) → str[false,true,0,5]out"delegate_end_to_end"[true,false,40000000,120]out"ai_drafts_human_executes"[true,true,500,20]out"delegate_with_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.