iterative-retrieval

Solves the "context problem" in multi-agent workflows where subagents don't know what context they need until they start working. Subagents are spawned with limited context. They don't know: Standard approaches fail: A 4-phase loop that progressively refines context: Initial broad query to gather candidate files: Assess retrieved content for relevance: Scoring criteria: Update search criteria based on evaluation: Repeat with refined criteria (max 3 cycles): Use in agent prompts: - Spawning subagents that need codebase context they cannot predict upfront - Building multi-agent workflows where context is progressively refined - Encountering "context too large" or "missing context" failures in agent tasks - Designing RAG-like retrieval pipelines for code exploration - Optimizing token usage in agent orchestration - Which files contain relevant code - What patterns exist in the codebase - What terminology the project uses - Send everything : Exceeds context limits - Send nothing : Agent lacks critical information - Guess what's needed : Often wrong - High (0.8-1.0) : Directly implements target functionality - Medium (0.5-0.7) : Contains related patterns or types - Low (0.2-0.4) : Tangentially related - None (0-0.2) : Not relevant, exclude - Start broad, narrow progressively - Don't over-specify initial queries - Learn codebase terminology - First cycle often reveals naming...