iterative-retrieval

Refining subagent context is achieved through an iterative retrieval loop that progressively filters codebase information. It dispatches subagents, evaluates file relevance scores, and updates search criteria to dynamically include necessary context for multi-agent workflows.

Managing context overload during codebase exploration requires iteratively retrieving and filtering information. By scoring file relevance and excluding low-signal results across multiple cycles, the system minimizes context size while ensuring critical data remains accessible for debugging.

Iterative retrieval improves dynamic task decomposition by running a 4-phase loop: DISPATCH, EVALUATE, REFINE, and LOOP. It evaluates subagent results, refines search patterns, applies exclusion rules, and loops up to three cycles to maximize context relevance.

Yes, iterative retrieval works with both codebases and structured datasets to minimize context overload. It applies the same relevance scoring and dynamic exclusion rules to ensure critical context is accessible for data analysis tasks.

The primary limitation of this iterative retrieval loop is the maximum cycle constraint, allowing up to three refinement loops. If the required codebase context is not discovered within three cycles, the system may not retrieve sufficient high-relevance information.