Towards a science of scaling agent systems: When and why agent systems work
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.
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