In this video, I break down a Google + MIT study that should honestly worry you if you’re building AI agents. They benchmarked ~180 agent configurations across OpenAI, Google Gemini, and Anthropic Claude, and the headline is brutal: “more agents” can be a catastrophic choice and drop performance up to 70%. At the same time, with the right setup, the exact same model and task can jump performance up to ~80%. So the real question isn’t “multi-agent or not?”—it’s which architecture matches your task. I walk through the five architectures they tested: single agent, independent agents (parallel + voting/aggregation), centralized (master orchestrator delegating to sub-agents), decentralized (agents coordinate peer-to-peer), and hybrid (orchestrator + peer coordination). The key takeaways are the cost of complexity (coordination kills token efficiency), the “45% rule” (if your single agent is already ~45% accurate, multi-agent often gives diminishing returns), and error amplification (independent agents can amplify errors massively without a validator/orchestrator). To make it practical, I end with a checklist: start by asking if your task is parallelizable or sequential, check single-agent accuracy, decide based on safety-critical needs, and account for tool-heaviness and token economics. The bottom line: stop blindly adding agents—design the agentic architecture like an MLOps system, with metrics, constraints, and real tradeoffs.