Google Just Proved More Agents Can Make Things WORSE -- Here's What Actually Does Work

Multi-agent AI systems are alluring, but current industry practices risk failure due to flawed comparisons to human teams. A Google-MIT study revealed that adding more agents can degrade system performance because coordination overhead grows faster than capability, leading to serial dependencies. To scale effectively, simplicity is key, requiring a philosophical commitment to eliminate these dependencies. Successful architectures adopt a two-tier hierarchy, keeping workers ignorant of the big picture and avoiding shared states. Isolated workers with small toolsets and prompts that act as API contracts are crucial. Prioritizing orchestration over agent intelligence, with systems designed to manage numerous simple workers, is essential for converting compute into capability.