AI Agents Remove the Informal Human Safety Net That Caught Bad Requirements

A software development commentary argues that when developers received vague or flawed sprint requirements, their natural confusion prompted them to walk over to a colleague's desk mid-implementation and ask clarifying questions — an undocumented but effective error-catching mechanism. This informal habit routinely surfaced planning failures early in a sprint, before flawed assumptions could reach production. AI coding agents lack this behavior: they either proceed on their best interpretation of an ambiguous spec or halt entirely, skipping the organic peer consultation that humans relied on. While modern AI tools do ask clarifying questions, the author contends these are directed back at the same person who wrote the prompt, rather than reaching colleagues who may hold missing context. The result is a genuinely new failure mode where bad requirements that once got caught mid-sprint now flow unchecked all the way to production.