Towards a science of scaling agent systems: When and why agent systems work
Summary
Google Research presents a large-scale evaluation of 180 agent configurations to derive quantitative scaling principles for AI agent systems. The work shows that more agents improve parallelizable tasks but can degrade sequential tasks, and introduces a predictive model that identifies near-optimal architectures for most unseen tasks.