iterative-retrieval

To prevent token overrun in multi-agent workflows, progressively refine retrieved files across multiple phases to provide subagents with a balanced context rather than passing large payloads upfront.

The best way to refine context for unpredictable agent orchestration is applying a deterministic loop that dispatches, evaluates, and refines retrieved content to iteratively converge on the right files.

Iterative retrieval balances breadth and relevance by cycling through dispatch, evaluate, refine, and loop phases to filter retrieved content, minimizing token usage while maximizing contextual usefulness for subagents.

Yes, you can use this multi-agent context refinement loop for RAG-like exploration, as it progressively matures context across multiple phases to extract relevant information without overwhelming the agent.

No, iterative context refinement does not require predefined file selections, as it is specifically designed for scenarios where subagents need codebase context that cannot be predicted upfront.

A limitation of progressive context refinement is the overhead of multiple evaluation cycles, meaning it is less suited for simple retrieval tasks where context requirements are already deterministic and fully predictable.