iterative-retrieval

You refine subagent context by implementing an iterative retrieval loop that progressively fetches and filters codebase context. This pattern handles context window limits by evaluating relevance and refining context across bounded cycles to improve task accuracy.

The iterative retrieval pattern is a 4-phase loop consisting of DISPATCH, EVALUATE, REFINE, and LOOP. It progressively gathers codebase context for subagents, using relevance scoring to filter information and ensure token-efficient orchestration.

You handle missing context failures by applying an iterative retrieval pipeline that evaluates and refines context across multiple cycles. If the initial context is insufficient, the loop dispatches further retrievals to progressively gather the missing codebase information.

You need a progressive retrieval pipeline when working with multi-agent workflows where context windows are limited. It is specifically designed for scenarios where context must be revealed gradually to solve complex code exploration tasks accurately.

Yes, iterative retrieval is useful for designing RAG-like retrieval pipelines for code exploration. It provides a structured 4-phase loop to fetch, evaluate, and refine codebase context, ensuring subagents receive accurate, token-efficient information.

The primary limitation of this 4-phase retrieval loop is that it requires bounded cycles to prevent infinite loops. While it effectively manages context window limits by refining codebase context, the iterative nature may increase retrieval latency before subagent dispatch.