doing-a-simple-two-stage-fanout

To analyze massive text, code, or data corpuses that exceed single agent context limits, use a two-stage fan-out pipeline that divides the corpus into parallel Worker segments, followed by Critic review and Summarizer synthesis.

Quality assurance during parallel processing is achieved by employing Critic agents to review Worker outputs, identifying gaps, inconsistencies, and cross-segment patterns before final synthesis.

Yes, distributed computing orchestrates multiple Worker agents to analyze subsets of files in parallel. Critics then ensure no vulnerabilities were missed across the project, consolidating all findings into a final report.

Agent orchestration handles failure recovery in large scale analysis by incorporating task tracking and recovery mechanisms throughout the Worker, Critic, and Summarizer pipeline stages.

The two-stage fanout approach for data processing requires segmenting the corpus, meaning it is limited to scenarios where dividing the dataset does not break critical cross-segment context dependencies entirely.