In the news
Decomposition Buys Integrity, Not Yield
arXiv cs.AI · Published · 3 min read
In 30 seconds
- What happened
- Research shows task decomposition across agent trees preserves accuracy but reduces output yield, with costs that often outweigh benefits.
- Why it matters
- Engineers building multi-agent systems need to weigh integrity gains against throughput losses when deciding whether to split tasks hierarchically.
- Watch out
- The model predicts only 0.7 to 11.3 percent of production sessions warrant delegation, yet 7.8 percent currently use it, suggesting widespread over-delegation.
- agent
- multi-agent
The patterns behind this
- Hierarchical Task Network (HTN) Planning
- Deep Research Agent
- Agentic Context Engineering (Evolving Playbook)
Each one covers how the technique works, when it earns its cost, and where it breaks.
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