Getting AI Coding Assistants Right on Large Repos
Hybrid indexing, agentic loops, and model routing—not bigger context windows—are the real keys to making AI coding assistants reliable on large codebases. The [Kilo Blog post](https://blog.kilo.ai/p/ai-coding-assistants-for-large-codebases) argues that context window size is a red herring. Most tools fetch the wrong files, ignore dependency graphs, and reset state on every request. It proposes combining AST/code graphs with vector search to give assistants structural and semantic understanding. It recommends agentic loops so models can plan, act, observe, and self-correct, plus routing work to the right model for each task. The post also offers evaluation guidance and purchase questions for leaders choosing tools. Use it to shape proofs of concept and your platform roadmap.