iterative-retrieval

Subagent context problems occur when agents lack the specific information needed to execute tasks in multi-agent workflows. They are solved by iteratively refining context from broad queries, progressively narrowing the information scope to deliver precise task context.

To design RAG-like retrieval pipelines for codebase exploration, apply an iterative retrieval approach. This method progressively refines context based on initial broad queries, enabling robust evaluation logic to extract relevant code segments effectively.

Progressive context refinement in multi-agent systems is a four-phase process. It starts with broad queries and iteratively evaluates and narrows the context, ensuring subagents receive progressively refined information for unpredictable tasks.

No, iterative retrieval requires no specific dependencies to manage subagent context. It is implemented as standalone scripts, relying on robust internal logic for context evaluation and refinement rather than external libraries.

You should use an iterative approach for context management when subagent workflows require unpredictable context or during codebase exploration. It is designed for scenarios where a single query cannot retrieve sufficiently refined task context.