Why AI agents need a transport layer: Solving the realtime sync problem

Building reliable AI agents for production involves addressing infrastructure challenges beyond just AI model development, particularly in maintaining real-time synchronization between agents and clients. This requires solving issues related to distributed systems such as connection management, client-specific state tracking, distributed agent routing, and ensuring continuity between historical and live data. Effective solutions involve implementing a robust transport layer that manages delivery, synchronization, and state management, allowing for seamless token streaming, message appends, and annotation handling. By employing these technical patterns, AI applications can handle network interruptions, device switches, and multi-user sessions more efficiently, reducing complexity and enhancing user experience. The focus shifts from model capabilities to overcoming the distributed system hurdles, ensuring that AI applications can function resiliently across various conditions.