Enterprise teams building multi-agent AI systems may be paying a compute premium for gains that don’t hold up under equal-budget conditions. New Stanford University research finds that single-agent systems match or outperform multi-agent architectures on complex reasoning tasks when both are given the same thinking token budget.

However, multi-agent systems come with the added baggage of computational overhead. Because they typically use longer reasoning traces and multiple interactions, it is often unclear whether their reported gains stem from architectural advantages or simply from consuming more resources….

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