iterative-retrieval

To improve context retrieval for multi-agent workflows, you can use an iterative retrieval loop that dispatches broad queries, evaluates the relevance of retrieved files, and refines search criteria over up to 3 cycles. This progressively gathers necessary information for subagents.

The best way to gather relevant files for complex bug fixes is to dispatch an initial broad query, evaluate candidate files for relevance, and iteratively refine search patterns and keywords. This ensures subagents receive accurate context for the task.

Yes, you can integrate iterative context refinement directly into subagent prompts. This allows agents to automatically update search patterns, keywords, and exclusions based on relevance evaluations, ensuring they have deep context understanding.

Iterative context retrieval works by dispatching broad queries to gather candidate files, evaluating their relevance, and refining search criteria based on the evaluation. This dispatch and evaluate process repeats up to 3 cycles to ensure accurate context.

The main limitation of using iterative cycles for context retrieval is that the refinement process repeats up to a maximum of 3 cycles. This bounds the search depth, which may not be sufficient for extremely complex tasks requiring extensive file gathering.