iterative-retrieval

Context retrieval is optimized by iteratively refining search queries based on file relevance scores. This prevents agents from loading the entire repository, minimizing token consumption while ensuring precise codebase navigation.

Subagents fail due to missing codebase knowledge and excessive token usage. This issue is solved by progressively refining search patterns through a 4-phase evaluation loop to identify specific relevant files.

The best way to limit token usage is applying a structured 3-cycle evaluation and refinement loop. This dynamically updates search keywords based on initial retrieval results to narrow down relevant files.

Progressive context refinement works through a 4-phase Dispatch, Evaluate, Refine, and Loop mechanism. It dynamically updates search patterns and keywords based on initial retrieval results to isolate relevant files.

Yes, iterative context discovery works for complex authentication bugs. It helps agents identify specific files containing relevant JWT and session logic without loading the entire repository into the agent context.

Iterative search refinement may not suit simple tasks where file locations are already known. It is designed for complex software engineering tasks requiring precise codebase navigation and limited token consumption.