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A single AI agent repeating its own reasoning will make the same mistake over and over. Researchers call it "Degeneration of Thought" -- a confirmation bias loop where the model generates an action, evaluates it, reflects on it, and arrives at the same flawed conclusion every time. Multi-agent systems break this cycle. But here's what most teams get wrong: throwing more agents at a problem without the right architecture amplifies errors by 17.2x instead of solving them. In this analysis, we break down 6 peer-reviewed studies, 7 production frameworks, and 3 scaling laws that define when multi-agent AI works, when it backfires, and how to choose the right architecture for your workload. Why Single Agents Hit a Ceiling A single-agent system is an AI architecture where one LLM handles all reasoning, tool use, and self-evaluation within a single session. It works well for straightforward tasks, but three structural constraints limit its effectiveness on complex workflows. ...