iterative-retrieval

Context gaps in multi-agent workflows are resolved through staged, iterative retrieval that progressively discovers codebase patterns and terminology across DISPATCH, EVALUATE, REFINE, and LOOP phases. This automated loop builds actionable context for complex tasks.

Refining code search queries for sub-agents is best achieved by starting with a broad file query, scoring relevance, and dynamically refining search criteria with selective exclusion. Up to three cycles surface high-relevance files to guide sub-agents effectively.

Iterative retrieval for codebase context is needed when patterns and terminology in the codebase are unclear for sub-agents. It applies progressive discovery to resolve ambiguity, enabling reliable outcomes through staged context refinement cycles.

Relevance scoring in iterative context refinement evaluates retrieved file candidates during the EVALUATE phase. It assesses search results against dynamic criteria, allowing the LOOP phase to refine queries and selectively exclude low-relevance files over up to three cycles.

Automated loops for multi-agent context management handle large codebases by applying up to three iterative retrieval cycles. The staged process dispatches broad queries, evaluates relevance, and refines criteria to surface high-relevance files effectively.

Limitations of iterative retrieval in multi-agent workflows include a strict maximum of three cycles to surface high-relevance files. If codebase context remains unclear after these refinement loops, sub-agents may still lack sufficient actionable context for complex tasks.