July 2026 Summaries

The integration of AI into platforms has prompted many companies to reconsider their approach to maintaining realtime infrastructure, as AI conversations demand a shift from signaling to delivery models, which require transmitting responses token-by-token without interruptions. Fin, formerly Intercom, opted to transition from its in-house realtime system, Nexus, to a managed service, achieving higher reliability and reduced operational costs. This decision reflects a broader trend where businesses evaluate the opportunity cost of maintaining self-built systems versus focusing on core product innovations. The complexities and expenses of sustaining realtime infrastructure—estimated at $100K to $200K annually—are compounded when AI-specific functionalities are added, making managed solutions like Ably AI Transport an attractive alternative for ensuring uninterrupted delivery and freeing engineering resources for product development.

AI Transport, developed by Ably, is a durable session layer designed to enhance agent-to-human communication by addressing the limitations of HTTP's request-response model in scenarios where AI agents operate for extended periods. Unlike traditional HTTP, which struggles with connection reliability, bidirectional communication, and state synchronization, AI Transport uses a pub/sub model to decouple agents from clients, allowing for session continuity even when connections drop or devices switch. By inserting a durable session between the agent and user, AI Transport manages connection recovery, enables real-time bidirectional communication, and maintains state continuity independently of backend activities. This approach ensures that both agents and clients can disconnect and reconnect without losing session data, facilitating multi-agent coordination and eliminating the need for custom buffers. AI Transport is designed to integrate seamlessly into existing architectures with minimal code changes, making it a promising addition to the AI agent stack alongside durable memory and execution. While it focuses on human-agent interactions, backend agent-to-agent coordination remains outside its scope, marking a clear boundary for its application.