セッション対応ロードバランシングによるリアルタイムAIエージェントのスケーリングScaling real-time AI agents with session-aware load balancing

Real-time AI agents—systems that hold continuous, interactive conversations with users through streaming audio, video, or text—are becoming a common building block for developers working with models like Google's Gemini. As this piece from the Google Developers blog explains, deploying these agents at scale exposes a fundamental mismatch with the load-balancing techniques that have served web applications for decades. Understanding why matters for anyone planning to move a real-time agent from a prototype to production traffic.

The core problem is architectural. Traditional load balancing assumes a request-response pattern: a client sends a short request, a server processes it, returns a response, and the connection is effectively free again. Load balancers distribute these requests across a pool of servers using strategies such as round-robin or least-connections, and they can infer how busy a server is from the rate and volume of requests flowing through it. Real-time AI agents break this model because they rely on long-lived, stateful, bidirectional streams. A single user session may keep a connection open for minutes or hours, exchanging data continuously in both directions.

Because these streams are persistent and stateful, they obscure the true capacity of a server. From the load balancer's perspective, a connection either exists or it does not; it cannot easily tell whether a given stream represents an idle session or one consuming significant compute for continuous inference. Two servers might show the same number of open connections while carrying very different actual loads. As a result, conventional signals like connection count or request rate become unreliable for routing decisions, and naive distribution can leave some servers overloaded while others sit underused.

The blog's proposed remedy is to move session awareness up the stack. Rather than relying on the network layer to guess at server load, developers should implement application-level session tracking directly within the runtime. In this approach, the application itself keeps an accurate count of active sessions and the resources they consume, and exposes that information so routing and scaling decisions can be made on real capacity rather than on a proxy signal. Tracking sessions at the application level gives the system a truthful picture of how much headroom each instance has before accepting a new stream.

This pattern connects to several well-established concepts. Bidirectional streaming is typically implemented over protocols such as WebSockets or gRPC, which keep a channel open for ongoing exchange—exactly the kind of connection that real-time interfaces like the Gemini Live API use. Session affinity, sometimes called sticky sessions, is already familiar to engineers who need to keep a user pinned to the same backend, and it becomes essential when a stream carries in-memory conversational state that cannot be arbitrarily moved. The session-aware approach builds on these ideas by adding capacity-aware admission control: deciding not just where a session goes, but whether an instance should take it at all.

The operational implications extend to autoscaling and lifecycle management. In orchestration systems such as Kubernetes, horizontal autoscaling often keys off CPU utilization or request counts, which the article's reasoning suggests may not accurately reflect the load of streaming workloads. Scaling on active session counts is likely to be a more faithful trigger. Long-lived connections also complicate routine operations like deployments and shutdowns, since abruptly terminating an instance would drop active conversations. Techniques such as connection draining and graceful shutdown, which let existing sessions complete while new ones are routed elsewhere, become correspondingly more important.

None of this is unique to any single vendor, but it is increasingly relevant as more products embed conversational, low-latency agents. The broader industry shift toward multimodal, real-time interaction—voice assistants, live translation, interactive tutoring, and agentic tools that act while the user watches—means these scaling challenges will appear more often. The takeaway from the Google Developers post is practical rather than product-specific: when the workload is a persistent, stateful stream, capacity must be measured and managed where that state actually lives, inside the application runtime, rather than left to infrastructure that was designed for stateless requests.