The hype around scaling work across multiple AI agents often overshoots reality. In practice, you're better off focusing on executing 2 tasks simultaneously rather than trying to parallelize across an entire swarm. Running too many agents concurrently tends to create bottlenecks and diminishing returns. When it comes to optimizing on-chain agent performance and throughput, the sweet spot for most use cases sits right at that dual-execution model—it's where you get real efficiency gains without the coordination overhead that kills your actual productivity.