The Wealth of Agents: Scaling Agentic AI Across Developers
Combining specialist agents from many developers lifts GAIA accuracy from under 35% to 70%.
The paper studies multi-developer agentic systems (MDAs), in which separate developers collaboratively compose agents, versus single-developer agentic systems. On the GAIA assistant benchmark, specialists whose individual agents stay at or below 35% accuracy reach 70% when combined, 12 percentage points above the median general-purpose SDA built with several times each specialist's budget. Theoretical models suggest MDAs encourage specialization and more efficient use of aggregated developer resources, framing collaboration as a scaling axis for agentic AI.
- Multi-developer agentic systems combine specialists built by different developers.
- Specialists under 35% GAIA accuracy reach 70% when combined.
- That MDA beats the median general-purpose SDA by 12 points.
- Theory argues MDAs incentivize specialization and better resource use.
From the original article
1 min read at arxiv.orgToday'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.
By Austin W. Hanjie · arxiv.org
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