# Scaling real-time AI agents with session-aware load balancing

DevFeed: [Scaling real-time AI agents with session-aware load balancing](<https://devfeed.tech/articles/scaling-real-time-ai-agents-with-session-aware-load-balancing-4217.md>)

Original publisher: [Read original article](<https://developers.googleblog.com/scaling-real-time-ai-agents-with-session-aware-load-balancing/>)

Author: Simerus Mahesh

Published: 2026-09-12T11:04:33.891311Z

Content type: article

Language: en

Sources: [Google Developers Blog](<https://devfeed.tech/sources/google-developers-blog.md>)

Topics: [real-time](<https://devfeed.tech/topics/real-time.md>), [Artificial Intelligence](<https://devfeed.tech/topics/ai.md>), [Streaming](<https://devfeed.tech/topics/streaming.md>), [Server](<https://devfeed.tech/topics/server.md>), [gRPC](<https://devfeed.tech/topics/grpc.md>), [WebSocket](<https://devfeed.tech/topics/websocket.md>), [cpu](<https://devfeed.tech/topics/cpu.md>), [Concurrent Programming](<https://devfeed.tech/topics/concurrent-programming.md>)

Tags: [ai-agents](<https://devfeed.tech/tags/ai-agents.md>), [concurrent](<https://devfeed.tech/tags/concurrent.md>), [cpu](<https://devfeed.tech/tags/cpu.md>), [real-time](<https://devfeed.tech/tags/real-time.md>), [routing](<https://devfeed.tech/tags/routing.md>), [server](<https://devfeed.tech/tags/server.md>), [streaming](<https://devfeed.tech/tags/streaming.md>)

## AI overview

This article explains why real-time AI agents require session-aware load balancing. Long-lived, stateful bidirectional streams make request rates and CPU utilization insufficient measures of backend capacity. The proposed approach tracks active sessions at the application level and combines session counts with CPU metrics to distribute traffic and avoid bottlenecks.

## Source excerpt

Real-time AI agents break traditional request-response load balancing paradigms because they rely on long-lived, stateful bidirectional streams that obscure true server capacity. To solve this, developers must implement application-level session tracking directly within the runtime to accurately measure the committed concurrent workload of active conversations. By feeding these precise session counts alongside standard CPU utilization metrics into a hybrid routing algorithm, infrastructure can effectively distribute stateful AI traffic and prevent individual backend bottlenecks.