AI-generated network changes under urgency
An engineer used an AI agent to generate a Python script that pushes BGP route-policy changes across a carrier's edge routers to mitigate a routing leak. The agent's diff looks correct and includes a confident explanation of each route-map. Network-wide misconfiguration here can black-hole traffic for millions of subscribers. The on-call wants to run it now to stop the leak. How do you handle the tension between urgency and the risk of AI-generated network changes?
approve_bgp_push(pop_names: list[str], subscriber_counts: list[int], human_signoff: bool, lab_validated: bool, rollback_timer_seconds: int, incident_active: bool) → str[["ams1","fra1","lon1"],[1200000,300000,800000],true,true,300,true]out"canary:fra1"[["ams1"],[1200000],false,true,300,true]out"block:no_human_signoff"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.