iterative-retrieval

Iterative context retrieval improves subagent relevance by running a four-phase loop that evaluates search results, refines queries, and expands or excludes candidates based on relevance scores.

Automating context search in multi-agent workflows is best handled by an iterative retrieval loop that dispatches queries, evaluates results, and refines criteria across up to three cycles for high-relevance output.

Iterative search refinement handles missing context by automatically identifying gaps during the evaluation phase and updating search criteria in subsequent loops to focus on missing areas across codebases.

Yes, iterative retrieval scales across codebases and documentation by using a four-phase loop that progressively expands or excludes search candidates based on relevance scores.

Subagents return irrelevant files because they are spawned with limited context and lack awareness of relevant patterns until an iterative retrieval loop evaluates and refines search criteria.

A fixed cycle count limits iterative context retrieval to three refinement loops, which may not fully resolve complex missing context gaps across highly fragmented codebases.