dev-iterative-retrieval

To retrieve codebase context without overwhelming limits, use an iterative retrieval process that refines search queries based on relevance scoring over a maximum of three cycles. This narrows down results to prioritize high-impact files while maintaining strict context boundaries.

Iterative context retrieval in multi-agent systems is a 4-phase loop mechanism—Dispatch, Evaluate, Refine, and Loop—that progressively narrows down search results. It ensures sub-agents discover correct architectural patterns by filtering noise and extracting high-relevance files based on actual codebase terminology.

To refine search queries when initial results are ambiguous, apply a structured evaluation loop that scores relevance and adjusts search terminology. This method iteratively filters out noise by matching actual codebase terms, peforming up to three refinement cycles to extract the correct dependencies.

Yes, multi-agent iterative retrieval works for complex bug fixes in large codebases. By implementing a relevance scoring system and progressively refining search queries, it ensures sub-agents discover the correct architectural patterns and dependencies needed to resolve the issue.

The main limitation of iterative context retrieval is its strict cycle cap; the refinement loop runs a maximum of three times. If the required codebase terminology or architectural patterns are not discovered within these cycles, the extracted context may remain insufficient for the task.