The Problem
AI coding assistants - GitHub Copilot, VS Code extensions, Claude - perform inconsistently across enterprise codebases. They apply general programming knowledge to specialized projects, or leak project-specific conventions into shared libraries. In monorepos, the problem multiplies.
One development team documented their solution after hitting this issue repeatedly: hierarchical configuration files that separate global rules from project-specific context. The pattern is worth noting because it aligns with emerging hierarchical agent architectures being deployed in production.
How It Works
The implementation uses layered configuration files:
Global level: Core safety rules, general coding standards, organization-wide patterns. These apply everywhere.
Project level: Specific frameworks, testing requirements, architectural decisions. These override global defaults for development tasks but defer to global rules for security and compliance.
Detection: Automatic context switching based on current directory. No manual intervention required.
GitHub Copilot supports this via .github/copilot-instructions.md files at workspace root and subfolder levels. VS Code AI extensions can read similar markdown files. The key is explicit hierarchy rules for conflict resolution.
Why This Matters
This mirrors production hierarchical agent patterns documented in recent AI research. High-level agents handle planning and delegation, low-level agents execute project-specific tasks, all coordinated through structured context.
The enterprise application: CTOs managing microservices architectures or large monorepos need consistent AI assistance that understands both organizational standards and per-service nuances. Manual context-switching doesn't scale.
Implementation Notes
Standard file structure across projects:
- Identity/role definitions
- Team member information
- Workflow specifications
- Project memory/notes
Safety-first: Global security and privacy rules always take precedence. Project configs can't override compliance requirements.
The approach scales - adding new projects means copying the config structure, not reconfiguring the AI assistant.
The Pattern
This isn't just about coding assistants. Hierarchical context management applies to any AI tool operating across organizational boundaries: documentation systems, testing frameworks, deployment agents.
Worth watching: How vendors formalize this into official features. Currently, most teams are rolling their own solutions based on whatever config files their tools happen to read. Standardization would help.
The trade-off: Additional configuration overhead. For teams with fewer than five distinct projects, flat context files probably suffice. For enterprise monorepos or multi-service architectures, the investment pays off in consistency.