Building a Multi-Agent ATDD Pipeline with LangGraph and Hexagonal Architecture

Building a Multi-Agent ATDD Pipeline with LangGraph and Hexagonal Architecture Write the spec, mark the story as ready, walk away. The agents do the rest. The problem with solo AI development Building a product solo is brutal. You are the PO, the architect, the developer, and the QA — all at the same time. When AI coding agents entered the picture, I didn't see a magic button. I saw a new kind of team member that needed the same thing any team member needs: clear responsibilities, short tasks, and a verifiable definition of done . The first thing I tried was the obvious approach: long prompts, one agent, do everything. It failed the way it always fails. The model drifted, lost context, and confidently built the wrong thing. Then I applied something I already knew from software architecture: Divide and conquer. If a long prompt fails, what about a very short one with a very specific context? What if instead of one agent doing everything, you had multiple agents — each with a single role