Workflow for parallelization

This workflow involves breaking down a task into independent subtasks that can be handled concurrently by multiple LLM calls or agents. Outputs are then programmatically aggregated and synthesized into a result.

The Parallelization workflow is used when a task can be divided into independent, nonsequential subtasks that can be processed simultaneously, significantly improving efficiency, throughput, and scalability. It is especially powerful in data-heavy, batch-oriented, or multiperspective problem spaces where the agent must analyze or generate content across multiple inputs.

Parallelization is particularly effective when:

• Subtasks do not depend on each other's intermediate results, allowing them to run in parallel without coordination.

• A task involves repeating the same reasoning process across many items (for example, summarizing multiple documents or evaluating a list of options).

• Multiple hypotheses or perspectives are explored in parallel to promote diversity, creativity, or robustness.

• You need to reduce latency for high-volume or high-frequency requests through concurrent LLM execution.

• This workflow is commonly used in document processing agents, survey or comparison engines, batch summarizers, multi-agent brainstormers, and scalable classification or labeling tasks, especially where rapid, parallel reasoning is a performance advantage.

Capabilities

• Parallel execution of LLM tasks (by using AWS Lambda, AWS Fargate, or an AWS Step Functions map state)

• Requires result alignment, validation, or deduplication at the synthesis stage

• Well-suited for stateless agent loops

Common use cases

• Analyzing multiple documents or perspectives in parallel

• Generating diverse drafts, summaries, or plans

• Accelerating throughput across batch jobs