A raw language model cannot give you a Published Language, because it cannot enter an agreement. It can answer in natural language structured by nobody, different on every call, and that is precisely what makes it an upstream Big Ball of Mud on your Context Map. This first deep dive in the Context Maps in the Age of AI series works through what it takes to put a boundary in front of that model: why every MCP server is an Open-Host Service commitment, and why the schema you expose is only the beginning of the Published Language you actually need.

Context Maps were never about IT systems alone. They were about sociotechnical systems: the teams, the politics, the influence between people who build models. AI introduces a new kind of actor to that picture, one that does not negotiate and drifts silently. This post opens a series that walks the whole Context Map catalog and asks, pattern by pattern, how they still hold up when a new actor is present.