iterative-retrieval

Iterative codebase context refinement uses a looping pattern to selectively retrieve and evaluate code files across up to three cycles, progressively honing in on critical segments to solve the contextual gap problem in complex codebases.

Iterative code retrieval is a multi-phase workflow that uses dispatch, evaluation, refinement, and iteration phases to progressively identify relevant files, systematically narrowing down code context to reduce noise and save troubleshooting time.

Progressive context refinement is needed when troubleshooting complex bugs across large codebases where initial data is insufficient, requiring agents to iteratively search and evaluate files to gather adequate context for accurate multi-agent processing.

To iteratively retrieve relevant files, describe the task to the AI and let it refine the search over up to three cycles of dispatch, evaluation, and query refinement until sufficient code context is gathered for the workflow.

Yes, iterative retrieval operates without external dependencies, using a self-contained multi-phase workflow of dispatch, evaluation, refinement, and iteration to dynamically refine code context within multi-agent systems.

Iterative code context refinement is limited to up to three search cycles, meaning if the relevant files are not identified within that iteration cap, the agent may not gather sufficient context for complex codebase troubleshooting.