iterative-retrieval

Multi-agent context failures are prevented by using iterative retrieval to progressively narrow broad candidate files into high-relevance selections. This approach evaluates relevance scores and identifies context gaps over up to three cycles to ensure optimal context size.

Finding relevant files with unfamiliar naming requires iterative retrieval with terminology and pattern learning. The mechanism updates search keywords based on actual codebase usage during each retrieval cycle, progressively improving search accuracy for bug fixing or feature implementation.

Token optimization in RAG pipelines is achieved by applying relevance scoring with early stopping. The iterative retrieval process halts once sufficient high-relevance context is found, preventing oversized context windows from consuming excessive tokens during agent orchestration.

Yes, iterative retrieval is designed for multi-agent code exploration scenarios. It applies a multi-cycle loop with candidate dispatch and query refinement, explicitly identifying missing context to guide the next retrieval pass until relevant files are acquired.

Progressive context refinement is limited to a maximum of three retrieval cycles. If the high-relevance context threshold is not met within these cycles, the mechanism stops, potentially requiring manual query refinement for complex codebase search tasks with deeply nested dependencies.