Recipe Overview

When different parts of a task can be done simultaneously, parallelization speeds up processing. A parallel agent splits the task into independent threads and calls multiple LLMs in parallel. Anthropic explains that this is effective for subtasks that can run concurrently or to gather diverse answers. For example, the agent might generate multiple answers in parallel and then combine or vote on them. This solves latency and can improve quality through redundancy. The pattern works well for tasks like generating multiple creative options, fact-checking across sources, or processing large datasets where independence allows simultaneous work.