iterative-retrieval

To prevent context too large errors in multi-agent systems, implement iterative context retrieval. This pattern progressively searches and evaluates codebase files, ensuring subagents only receive the most relevant context instead of the entire codebase.

Iterative context retrieval in RAG pipelines is a pattern for progressively refining context retrieval. It learns from initial search results to dynamically adjust keywords and focus areas for subsequent searches, solving the subagent context problem.

Dynamically refine search queries for codebase exploration by applying an iterative retrieval pattern. This approach learns from initial search results to adjust keywords, patterns, and focus areas for subsequent targeted file discovery.

Progressive context refinement works effectively for debugging complex features. It intelligently identifies and retrieves only the relevant files related to the bug through dynamic query adjustment, preventing missing context errors.

The best way to score file relevance for subagent communication is using iterative retrieval. It performs relevance scoring based on task intent and codebase analysis to build the most relevant context for multi-agent workflows.

Use iterative retrieval instead of static context loading when initial context is insufficient for multi-agent systems. It is necessary for scenarios requiring dynamic codebase exploration where upfront context is unknown, preventing missing context issues.