iterative-retrieval

Dynamic codebase context gathering for subagents uses an iterative retrieval pattern that progressively searches and evaluates files to build a relevant context set. This approach refines broad search terms into precise code snippets needed for complex agent tasks.

Progressive context refinement for multi-agent codebase exploration applies an iterative search mechanism that evaluates initial findings to narrow down relevant file paths. This RAG-like pipeline dynamically discovers the specific code sections required when context is not known upfront.

Iterative search supports feature implementation and bug fixing by allowing agents to start with broad queries and progressively narrow them down. This dynamic context gathering satisfies the need for precise codebase context when handling unpredictable subagent requirements.

Iterative context retrieval handles unpredictable search needs in multi-agent systems by repeatedly searching and evaluating codebase files based on prior findings. This progressive refinement builds a relevant context set without requiring predetermined file paths or code snippets.

An iterative retrieval pipeline is needed for codebase exploration when initial requirements are underspecified and subagents require specific code snippets that cannot be determined at the start. It solves the subagent context problem by dynamically refining search terms based on initial findings.