What are the best strategies for managing “exploding” context in large enterprise codebases for AI agents?
Q: What are the best strategies for managing “exploding” context in large enterprise codebases for AI agents?
In enterprise environments, codebases often span millions of lines across numerous dependencies, creating an overwhelming volume of information for AI agents. The key challenge is avoiding context overload, which can degrade performance or lead to errors. The best approach is to avoid loading the entire codebase into the agent’s context window. This strategy is impractical and risks losing critical details during any necessary compression. Instead, focus on delivering targeted, high-level abstractions that guide the agent efficiently.
Core Strategies
Develop Curated Summaries (e.g.,
LLMs.txtFiles): Generate concise documentation for key libraries, modules, and components rather than raw code. These summaries serve as an API reference or developer handbook tailored for AI use, including:The library’s overall purpose, usage conventions, and high-level architecture.
Key function and method signatures, including parameters, return types, and examples.
Details on external dependencies and integration points.
This keeps the context lean while providing enough structure for the agent to reason about interactions.
Construct an Agent-Friendly Index: Treat these summaries as a navigable index of the codebase. It equips the agent with a roadmap to identify relevant components without exhaustive searches or random exploration. For instance, the agent can reference the index to select appropriate modules for a task, then request specifics only as needed.
Automate Documentation Maintenance: Manual updates are unsustainable in dynamic codebases. Implement automation through scripts, build pipelines, or CI/CD tools like GitHub Actions to regenerate summaries on code changes. This guarantees the agent always accesses current information, reducing drift between documentation and reality.
By prioritizing abstraction over completeness, these strategies enable AI agents to handle enterprise-scale complexity without performance bottlenecks. Start small by summarizing high-impact modules, then scale automation as the system evolves.