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AI agents require relevant context to perform effectively. Providing this context manually in every prompt is cumbersome, and a one-size-fits-all global context is often too broad or too narrow. Different projects, users, and organizational policies may require different baseline information for the agent.
Solution
Implement a system of layered configuration files (e.g., named CLAUDE.md or a similar convention) that the agent automatically discovers and loads based on their location in the file system hierarchy. This allows for:
Enterprise/Organizational Context: A root-level file (/<enterprise_root>/CLAUDE.md) for policies or information shared across all projects in an organization.
User-Specific Global Context: A file in the user's home directory (~/.claude/CLAUDE.md) for personal preferences, common tools, or notes shared across all their projects.
Project-Specific Context: A file within the project's root directory (<project_root>/CLAUDE.md), typically version-controlled, for project-specific instructions, architectural overviews, or key file descriptions.
Project-Local Context: A local, non-version-controlled file (<project_root>/CLAUDE.local.md) for individual overrides, temporary notes, or secrets relevant to the project for that user.
The agent intelligently merges or prioritizes these context layers, providing a rich, tailored baseline of information without manual intervention in each query.
Example (configuration hierarchy)
flowchart TD
A[Enterprise Root<br/>/enterprise/CLAUDE.md] --> E[Merged Context]
B[User Global<br/>~/.claude/CLAUDE.md] --> E
C[Project Root<br/>project/CLAUDE.md] --> E
D[Project Local<br/>project/CLAUDE.local.md] --> E
E --> F[Agent Context Window]
style A fill:#e1f5fe
style B fill:#f3e5f5
style C fill:#e8f5e8
style D fill:#fff3e0
style F fill:#ffebee
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Evidence
Evidence Grade:high
Industry Adoption: Production-validated across Claude Code, Continue.dev, Cursor AI, and GitHub Copilot
Origin: Industry-practitioner pattern; limited formal academic literature
How to use it
Use this when model quality depends on selecting or retaining the right context.
Start with strict context budgets and explicit memory retention rules.
Measure relevance and retrieval hit-rate before increasing memory breadth.
Version-control project context (CLAUDE.md); exclude local overrides (CLAUDE.local.md) from VCS.
Trade-offs
Pros: Raises answer quality by keeping context relevant and reducing retrieval noise; enables enterprise-wide policy enforcement; supports automatic context loading without manual intervention.
Cons: Requires ongoing tuning of memory policies and indexing quality; context window limits may truncate layers; potential for configuration conflicts.
References
Based on the CLAUDE.md system described in "Mastering Claude Code: Boris Cherny's Guide & Cheatsheet," section IV.