iterative-retrieval

Iterative retrieval progressively narrows search results across multiple cycles using a DISPATCH-EVALUATE-REFINE-LOOP pattern, filtering noisy data to surface the most relevant files and information for your task without overwhelming your agent with excess context.

Multi-agent workflows benefit from phase-guided retrieval that enforces structured cycles: dispatch agents to search, evaluate results against task requirements, refine the search space, then loop up to three times to progressively improve relevance and minimize irrelevant data.

Yes. Start with broad keyword and comment searches across your codebase, then iteratively filter results through evaluation cycles to pinpoint the most relevant files and context needed for debugging or feature work.

Context refinement applies the same DISPATCH-EVALUATE-REFINE-LOOP pattern to any structured data source: retrieve initial candidates, assess them against your requirements, prune the search scope, and repeat to extract high-relevance results with minimal noise.

Use iterative retrieval when initial context is limited or search results are too noisy to be immediately useful; it's designed for multi-agent workflows where progressive filtering reduces irrelevant information and focuses agents on task-specific context.

Yes. Iterative retrieval guides subagents through structured retrieval phases using prompt engineering to interpret results and refine context, enabling each cycle to build on previous evaluations to improve answer quality.