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Independent subtasks are processed in parallel and either merged (sectioning) or the best result is selected via an aggregator (voting). Typical conditions for use: The input is naturally segmentable and tasks are independent to reduce latency (sectioning); Robustness is more important than single execution costs (voting). The central trade-off: Lower latenc…
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The agentic parallelization pattern splits a task into independent subtasks that multiple agents handle concurrently before combining the results.
Anthropic documents the pattern as a workflow for independent subtasks: agents work concurrently to reduce waiting time and then combine their results.
Imagine a team distributing several research assignments at once and reconciling the results in a shared report.
Independent agents work simultaneously on separate subproblems.
Parallelization shortens runtimes for tasks that can be separated.
A coordinator defines subtasks, collects results, and handles conflicts.
The pattern fits research, evaluation, and processing when subtasks have few dependencies.
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