Why orchestrators become a bottleneck in multi-agent AI

Multi-agent AI systems, where multiple specialized agents collaborate to complete complex tasks, require more than traditional orchestration frameworks to function effectively. Users expect these systems to break down tasks and coordinate seamlessly, providing visible progress and allowing for real-time intervention. However, conventional orchestrators often act as bottlenecks, limiting the fluidity and responsiveness of these systems. To overcome this challenge, a robust AI transport layer is essential, supporting persistent, multiplexed communication and state synchronization across agents. This infrastructure allows for direct, concurrent updates from multiple agents, enhancing transparency and reliability without relying solely on a single orchestrator. The ability to handle mid-task changes and ensure that system activity is visible to users builds trust and ensures that agents can coordinate effectively, ultimately transforming isolated model calls into a cohesive and collaborative user experience.