Five-Stage Workflow to Keep AI Coding Agents Under Human Control

As AI coding agents become common in software teams, many organizations find themselves with more machine-written pull requests than humans can meaningfully review. The core problem, according to a workflow guide published on DEV Community, is that team intent and product decisions are never recorded in a form agents or reviewers can check against. The proposed five-stage process shifts human effort to two critical points: precisely defining what should be built upfront via a structured spec, and verifying the final output matches that spec during review. Each agent is assigned a single, narrowly scoped task with explicit file boundaries, acceptance criteria, and required test evidence, preventing agents from expanding scope or conflicting with one another. The framework is designed to be tool-agnostic, relying on version-controlled files rather than any specific AI assistant's memory or chat history.