Spawning parallel subagents with independent tools and planning is more effective for complex long-context tasks than having a single agent handle everything—this architectural pattern improves coding performance from 71.75% to 81.36% on long documents.
This paper introduces Recursive Agent Harness (RAH), a pattern where AI agents spawn subagents to handle complex tasks in parallel rather than processing everything in a single call. Like how recursive language models break down reasoning into multiple model calls, RAH breaks down work into multiple agent instances, each with their own tools and planning capabilities.