How Three Root Files Can Make AI Coding Agents Production-Ready

AI coding tools like Lovable and Cursor are powerful but share a common weakness: they lack persistent context about a project's conventions, architecture, and library choices. Sandbox agents generate fast prototypes but lose all project-specific decisions the moment a session ends, while file-system agents read real codebases but often invent conflicting conventions without proper guidance. The core problem is that language models operate within a sliding context window, forcing them to guess at naming patterns, component APIs, state management, and architectural rules when no reference exists. A practical fix involves placing a small set of structured files — such as CLAUDE.md, .cursorrules, and AGENTS.md — at the repository root, where agents can automatically read them at the start of every session. Written once and maintained alongside the codebase, these files give AI agents the project-specific map they need to bridge the gap between prototype and production.