recursive-decomposition

Recursive decomposition handles large-scale codebase analysis by programmatically partitioning inputs into smaller pieces and recursively processing them via sub-agents, ensuring comprehensive analysis without exceeding context window limits.

The best way to aggregate information across multiple large documents is recursive processing, which systematically searches, partitions, and synthesizes scattered data to overcome token limitations and generate comprehensive reports.

Recursive decomposition works by iteratively self-invoking sub-agents to process partitioned chunks of extensive datasets, retrieving and structuring data efficiently to enable complex reasoning across the entire input scope.

Yes, you can use recursive decomposition to find error handling patterns in your entire codebase; it intelligently identifies relevant files, partitions them, and analyzes each part recursively before synthesizing a complete report.

You should avoid recursive task decomposition for large context processing when tasks are simple enough to fit within standard context windows, as the overhead of iterative self-invocation and structured data handling is unnecessary for small-scale analysis.