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Codebase context for AI agents starts before the prompt
AI agents work better on real repositories when the repo context, architecture, and documentation are mapped before implementation begins.
Most AI coding failures in real codebases are context failures. The model is asked to act before it understands the shape of the system.
That is why codebase context should be treated as infrastructure, not as a nice-to-have after the prompt starts.
What context actually means
Context is not just code search. It includes service boundaries, documentation, ownership, repo relationships, and the path from a user request to the modules that matter.
Where DevGraph fits
DevGraph helps teams turn scattered repo knowledge into a graph that humans and AI can navigate. That makes prompts more grounded and review easier because the architecture is visible before editing begins.