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Explore groundbreaking research from Google DeepMind and MIT that challenges the conventional wisdom that "more agents equals better performance" in AI systems. Discover how this comprehensive study of 180 agent architectures reveals the mathematical flaws in current multi-agent approaches and introduces the critical "Tool-Coordination Trade-off" concept. Learn why independent agent swarms can amplify errors by up to 17.2 times and understand the new scaling laws that govern agent system performance. Examine the rigorous mathematical framework presented in the research, including the advanced functional forms and critical coefficients that define optimal agent orchestration. Gain insights into how these findings fundamentally reshape our understanding of multi-agent systems and their practical applications in AI development.
