Scaling Laws for Agentic AI: When Does Swarm Intelligence Peak?

ABSTRACT

While scaling laws for single-model LLMs are well established, the relationship between the number of collaborating agents and overall system performance remains poorly understood. This paper investigates the scaling properties of Agentic AI swarms across complex reasoning and coding benchmarks. The authors discover a non-linear 'Swarm Intelligence Peak', demonstrating that while performance improves predictably up to a threshold of specialized agents, adding further nodes introduces severe communication overhead, consensus degradation, and logic decay. The study provides a mathematical formula for optimizing swarm size based on task complexity.