Building an AI Layer for Large Codebases

The AI Layer: Harness Over Model

Success with coding agents in large, complex codebases depends less on the underlying model and more on the 'AI Layer'—a structured harness of context and tools. Rather than relying on the agent to 'figure out' a massive repository, you must curate the environment to provide relevant, scoped information. This layer consists of global rules, specialized skills, and external tool integrations that act as an extension of the developer's own navigation capabilities.

Context Curation via Lean, Layered Rules

Avoid the common pitfall of creating massive, monolithic CLAUDE.md files. These overwhelm the model and degrade performance. Instead, keep global rules lean, focusing on high-level architecture, tech stack, and core conventions. Use a layered approach: place a root CLAUDE.md for universal rules and subdirectory-specific CLAUDE.md files for local conventions. This ensures the agent only loads the context relevant to the specific slice of the codebase it is currently editing, adhering to the principle of progressive disclosure.

Self-Improving Feedback Loops

Transform your harness from static to dynamic using start and stop hooks. A 'start hook' can dynamically inject team-specific context or documentation (e.g., from Confluence) based on the current task. A 'stop hook' acts as a continuous improvement mechanism: it runs in a headless session after the agent finishes, reviews the changes made, and proposes updates to your CLAUDE.md files. This prevents your documentation and rules from going stale as the codebase evolves.

Scoped Skills and LSP Integration

Skills should be treated as reusable workflows rather than just prompts. By scoping skills to specific directory paths, you ensure that specialized domain knowledge (like API route generation) is only surfaced when the agent is working in the relevant area. Furthermore, integrating an MCP (Model Context Protocol) server allows the agent to utilize Language Server Protocol (LSP) capabilities, providing it with the same symbol-search and definition-navigation power that a human developer has in their IDE, significantly outperforming basic grep-based search.

Subagents for Exploration

For tasks where the starting point is unknown, use subagents to perform discovery. By delegating the initial exploration of a large codebase to a subagent, you can map out dependencies and identify the correct focus area before the primary agent begins implementation. This keeps the main session focused and prevents the agent from wandering through irrelevant files.