iterative-retrieval

To improve multi-agent context retrieval, use an iterative process that progressively narrows context via a 4-phase loop. This guides subagents to retrieve relevant codebase information without overwhelming their inputs.

The best way to manage token limits during codebase exploration is applying relevance scoring and dynamic query refinement. This minimizes data transfer by iteratively evaluating and selecting only high-relevance context.

Implement an iterative retrieval pipeline by initiating a three-cycle process starting with broad file searches, evaluating relevance, then iteratively refining criteria to assemble a high-quality codebase context.

The 4-phase loop works by dispatching broad searches, evaluating relevance, refining dynamic queries to identify explicit gaps, and looping up to three cycles to progressively narrow codebase context.

Yes, you can use iterative retrieval for debugging and feature implementation across distributed agent systems. The progressive context refinement applies to any multi-agent workflow requiring token-efficient codebase context.