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Towards a science of scaling agent systems: When and why agent systems work
January 28, 2026 Yubin Kim, Research Intern, and Xin Liu, Senior Research Scientist, Google Research Through a controlled evaluation of 180 agent configurations, we derive the first quantitative scaling principles for AI agent systems, revealing that multi-agent coordination dramatically improves performance on parallelizable tasks but degrades it on sequential ones; we also introduce a predictive model that identifies the optimal architecture for 87% of unseen tasks. AI agents — systems capable of reasoning, planning, and acting — are becoming a common paradigm for real-world AI applications.
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