Towards a Science of Scaling Agent Systems

The first quantitative scaling principles for multi-agent AI systems, derived from 260 configurations across six benchmarks.

Three findings matter: independent agent swarms can amplify baseline errors up to 17 times; tool-heavy tasks suffer disproportionately from multi-agent overhead; and a capability saturation effect means adding agents only helps when single-agent accuracy is below roughly 45%.

The paper also distinguishes centralized from decentralized topologies — centralized orchestration contains errors better but at higher coordination cost.

For anyone designing agentic architectures, this is the paper that replaces intuition with measurement, turning the single-vs-multi question into an engineering tradeoff with quantifiable thresholds.