iterative-retrieval

Iterative retrieval resolves subagent context challenges in multi-agent workflows by progressively refining codebase context through a four-phase retrieval process. This ensures subagents receive necessary context dynamically as tasks are performed.

The iterative retrieval pattern is a context progression mechanism that progressively refines codebase context across four phases. It improves subagent performance by dynamically retrieving relevant files instead of relying on upfront context prediction.

Dispatch a query for files relevant to a specific context, such as 'authentication', to refine retrieval asynchronously. The process requires Python with asyncio and aiohttp to support the four-phase progressive context refinement workflow.

Yes, the iterative retrieval pattern requires Python and support for asynchronous retrieval and evaluation, specifically utilizing the aiohttp and asyncio dependencies to handle progressive context refinement for subagents.

Subagents fail when codebase context cannot be predicted upfront. The iterative retrieval pattern solves this by progressively refining context through a four-phase process, ensuring subagents receive necessary information for task completion.

Use progressive context refinement when implementing subagents that need codebase context, building multi-agent workflows, or facing context-related agent failures where required context cannot be predicted upfront.