According to @rseroter, Google Research reports the first quantitative scaling principles for AI agent systems after a controlled evaluation of 180 agent configurations, finding multi agent setups excel on parallelizable tasks while sequential chains deliver limited gains due to coordination overhead (source: Google Research blog). The study concludes that adding more agents or larger models is not universally better; performance depends on task decomposability, communication cost, and role design, guiding when to prefer agent teams versus a single agent prompt (source: Google Research blog). For practitioners, the results inform architecture choices such as parallel task sharding, minimizing handoffs, and careful tool routing to reduce latency and cost while maintaining output quality in production LLM workflows (source: Google Research blog).