Multi-agent AI Systems Architecture

Google DeepMind’s empirical analysis across 180 experiments demonstrates multi-agent systems deliver up to 80% performance uplift on parallelizable tasks like financial analysis via centralized coordination, but degrade sequential reasoning by 39-70% due to coordination overhead—pushing enterprises toward task-specific architectural choices for true scalability.[1][3]