Are you paying an AI ‘swarm tax’? Why single agents often beat complex systems

Stanford researchers found that single-agent AI systems often matched or outperformed multi-agent setups on complex multi-hop reasoning tasks when given the same intermediate thinking-token budget.

The advantage of multiple agents disappeared in many cases, suggesting extra coordination does not automatically improve reasoning quality.

Multi-agent designs may mainly add compute overhead without better accuracy, especially in enterprise settings.

A single-agent approach appears to be the more compute-efficient default unless the model’s context becomes too long or starts to degrade.