If you've spent any real time building with AI coding agents, you've hit this moment: the agent confidently produces hundreds of lines of code that solve a problem you didn't ask it to solve. It didn't misunderstand your prompt — it filled in the gaps you left with its own assumptions. And it never told you. This is the most common complaint in every AI development community right now. On Reddit, in Discord servers, across dev Twitter — the pattern is identical. Someone asks an agent to build a feature, gets back something that looks right, and discovers hours later that it made silent decisions about authentication, state management, or data structure that contradict the rest of the codebase. Agents don't go sideways because they're bad at code. They go sideways because they're working from vague context. A long PRD gives them too much irrelevant information. A one-shot prompt gives them too little structure. In both cases, the agent is forced to guess — and LLMs are exceptionally good at guessing confidently. One Reddit commenter in r/AI_Agents put it perfectly: "All of the stuff that will make your agent sparkle in the end is all the boring stuff that nobody wants to do. It begins and ends with good, clear, documentation." Another developer in r/microsaas observed that the real challenge isn't agent memory — it's that there's no structured product context for agents to draw from in the first place. The pattern that changed my output quality wasn't a better model or a longer context window. It was forcing a structured breakdown before any code gets written: What do we know for certain about this feature? What are we assuming? What questions are still open? When you make agents surface their assumptions explicitly, two things happen. First, you catch misalignment before it becomes code. Second, the agent gets exactly the context it needs — not a 40-page PRD, not a one-liner, but a focused package of facts, assumptions, and constraints scoped to the specific thing being built. I discovered this the hard way, building an application across 200+ separate AI chat sessions. Every feature was a new conversation. Every agent had no idea what the last one did. I was the only thread connecting everything — in my head, across browser tabs, in a Notion doc nobody else could read. So I built Dossier to productize that workflow. It models your product as a hierarchical map — user workflows, actions, functionalities — and generates context-rich prompts feature by feature. Each piece carries structured context: what's known, what's assumed, what needs answering. Agents work from that, not from a brain dump. The result: agents stay scoped, assumptions get challenged before coding starts, and you can actually see the product taking shape instead of staring at a terminal hoping for the best. If agent quality is the wall you keep hitting, the answer isn't a better model. It's better context. Dossier is free and open source.