doing-a-simple-two-stage-fanout

Analyzing large data sets that exceed a single context capacity requires distributing the corpus to parallel Worker subagents, reviewing with Critic subagents, and synthesizing with a Summarizer for a final report.

Processing large text corpora in parallel is best handled by a two-stage fanout workflow that distributes data segments to Workers, has Critics review the outputs, and uses a Summarizer to synthesize the final analysis.

Yes, you need specific computation resources to run parallel data processing workflows because the system orchestrates multiple Worker, Critic, and Summarizer subagents simultaneously to handle large volumes of data.

Failure recovery in parallel text analysis workflows works through built-in task tracking mechanisms that monitor the progress of distributed Worker and Critic subagents, ensuring reliable execution and resuming incomplete segments.

Yes, you can use a two-stage fanout workflow for code analysis because the parallel processing architecture supports large text, code, or data sets by distributing segments to subagents for comprehensive review.

Limitations of using parallel subagents for large scale data processing include the requirement for sufficient computation resources to handle concurrent processes and dependencies on task tracking for failure recovery.