iterative-retrieval

To prevent subagent context overflow in multi-agent workflows, use a progressive retrieval loop that dispatches, evaluates, and refines queries iteratively. This approach applies relevance scoring to filter files, ensuring subagents receive targeted context without exceeding token limits.

The best way to retrieve targeted context for codebase exploration is using an iterative retrieval pattern. It automatically discovers project-specific naming conventions during retrieval cycles and ranks retrieved files on a 0-1 relevance scale to prioritize high-value code context.

Iterative query refinement for RAG pipeline design works by progressively narrowing relevant context through a maximum of 3 retrieval cycles. It evaluates and refines search queries based on relevance scores to optimize token usage and improve retrieval accuracy.

Yes, you can use iterative retrieval to gather context for subagent tasks like fixing authentication bugs. It first retrieves broad auth-related files, then refines the search in subsequent cycles to find specific session and JWT utility files without exceeding token limits.

A key limitation of progressive context retrieval for agent orchestration is the maximum cap of 3 retrieval cycles. This constrains the depth of iterative query refinement, meaning highly fragmented codebases may still lack complete context if the pattern cannot be found within three iterations.