The Wealth of Agents: Scaling Agentic AI Across Developers· 代理的财富：跨开发者扩展代理AI
Today's agentic systems are typically designed, built, and deployed by single developers with full control over all participating agents. While this simplifies development, it limits the potential scale of agentic systems. We study multi-developer agentic systems (MDAs) which enable developers to collaboratively build agentic systems to push frontier capability beyond what any individual developer could achieve alone. We construct empirical and theoretical models of developers to compare MDAs with single-developer agentic systems (SDAs). On GAIA, a general assistant benchmark, a population of specialized developers whose individual agents do not exceed 35% accuracy reaches 70% when their agents are combined in an MDA. This MDA performs 12 percentage points above the median general-purpose
研究多开发者协作构建代理AI系统以超越单个开发者能力的局限。
- 核心方法
- 构建实验和理论模型来比较多开发者代理AI系统（MDAs）与单开发者系统（SDAs），并在GAIA基准上测试MDAs的表现。
- 适合谁读
- 研究者、工程师
- 要解决的问题
- 当前代理AI系统由单个开发者完全控制，限制了系统的潜在规模和发展。
- 关键实验
- 在GAIA基准上，多开发者系统中的一组专业化开发者，其个体代理的准确率不超过35%，但组合后系统准确率达到了70%。
- 主要贡献
- 展示了通过多开发者协作，即使个体代理能力有限，整体系统性能也能显著提升，超过单开发者的限制。
- 意义与局限
- 证明了多开发者协作构建代理AI系统的可行性和优势，但未详细探讨实际应用中的潜在挑战和局限。