iterative-retrieval

Context retrieval for subagents is refined by dispatching broad queries, evaluating relevance, and iteratively narrowing search criteria up to three times. This progressive pattern gathers sufficient codebase context without prior knowledge of target files.

Token optimization in multi-agent workflows is achieved by iteratively evaluating retrieved content and refining search criteria. This prevents sending excessive or irrelevant data to agents, ensuring only precise context is loaded.

Progressive context discovery for RAG-like pipelines works by looping up to three times: dispatching an initial broad query, evaluating the retrieved content, and refining search criteria to gather only the relevant snippets needed for complex agent tasks.

Iterative context refinement is needed when debugging complex features with vague bug reports. It allows agents to progressively discover specific files and functions involved by evaluating and refining initial broad search results.

Yes, the iterative retrieval pattern handles unpredictable context needs in agent orchestration by looping up to three times. It evaluates retrieved content relevance and refines search criteria dynamically until sufficient context is gathered.

The iterative context refinement approach is limited to a maximum of three loops for narrowing down relevant files. If the initial query is too vague, three refinement cycles might be insufficient to gather all necessary subagent context.