More Agents, Worse Results: Google Just Proved That Multi-Agent Scaling Is a Myth

180 experiments across 5 architectures reveal that adding agents degrades performance by up to 70% on sequential tasks. The 45% threshold rule every agent builder needs to know. There's a prevailing assumption in the AI agent ecosystem right now: if one agent is good, multiple agents must be better. More agents means more reasoning power. More specialization. More parallelism. Better results. Google DeepMind and MIT just tested that assumption rigorously — 180 configurations, 5 architectures, 3 model families, 4 benchmarks — and the results should make every agent builder reconsider their architecture. The headline finding: multi-agent systems improved performance by 81% on parallelizable tasks but degraded it by up to 70% on sequential ones. Adding agents didn't just fail to help — it actively made things worse. This isn't a theoretical argument. It's the most comprehensive empirical study of agent scaling published to date, and it comes with a practical decision framework that