Modern large language models are becoming dramatically better at reasoning about code. Context windows are expanding from a few thousand tokens to millions. Yet, developers still struggle to get consistent, accurate answers from AI coding assistants when working on large, real-world repositories.
If you've ever watched an agent like Claude Desktop or Cursor try to debug a complex issue in a new repository, you've likely witnessed the "Grep Loop of Despair":
The agent runs grep -r "AuthService" .
It gets 500 results.
It runs cat on three random files.
