Most AI-assisted coding projects fail long before the model writes bad code. The failure usually starts with context. Developers hand an autonomous coding agent a massive repository, a vague objective, and a 2,000-line CLAUDE.md filled with contradictory instructions, outdated architecture notes, and motivational prose disguised as engineering guidance. Then they wonder why the agent creates brittle abstractions, ignores conventions, or rewrites unrelated modules. The problem is not the model. The problem is operational ambiguity. As coding agents become increasingly capable of multi-step reasoning, repository navigation, and tool orchestration, the role of CLAUDE.md is shifting from “prompt helper” to something much more important: an execution specification for autonomous software systems. This article proposes a practical, production-oriented structure for CLAUDE.md files based on emerging patterns from agentic coding workflows, long-context evaluation research, and real-world repository orchestration.…