The Wealth of Agents: Scaling Agentic AI Across Developers

Today's agentic systems are typically designed, built, and deployed by a single developer with full control over all participating agents. While this simplifies development, it forces developers to rebuild existing capabilities. We study _multi-developer agentic systems_ (MDAs), which coordinate agents from independent developers with different capabilities and resources. Such systems offer a new scaling axis: developers can build on existing agents and contribute specialized capabilities beyond what they 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 agents whose individual accuracy does not exceed 35% reaches 70% when their capabilities are combined in an MDA. This is 12 percentage points above the median general-purpose SDAs built with several times the development budget. Our theoretical models show how MDAs can incentivize specialization and encourage more efficient use of developer resources. Put together, our results make a preliminary case for MDAs as a new scaling axis and way for developers with diverse capabilities and resources to advance agentic AI collaboratively.