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. To isolate the true driver of performance, researchers at Stanford University compared single-agent systems against multi-agent architectures on complex multi-hop reasoning tasks under equal “thinking token” budgets. Their experiments show that in most cases, single-agent systems match or outperform multi-agent systems when compute is equal. Multi-agent systems gain a competitive edge when a single agent’s context becomes too long or corrupted. In practice, this means that a single-agent model with an adequate thinking budget can deliver more efficient, reliable, and cost-effective multi-hop reasoning. Engineering teams should reserve multi-agent systems …