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

Realtime AI agents behave differently from traditional APIs because they keep longlived, bidirectional sessions open instead of handling isolated requests. In a Google Developers Blog post dated Aug. 3, 2026, Site Reliability Engineer Simerus Mahesh explains why this changes infrastructure planning for voice and other interactive AI systems. The article argues that metrics such as QPS and CPU usage do not fully reflect the workload of streaming AI services. A backend may appear lightly loaded by request count, yet still be committed to many active sessions, including idle or longrunning conversations. To address that gap, the post recommends tracking active sessions inside the application runtime and combining that signal with utilization metrics in a hybrid balancing model. The post also outlines benchmarking and reliability considerations, including measuring session duration, cancellation behavior, startup latency, and counter accuracy under concurrency. The central takeaway is that scaling realtime AI requires balancing live conversations, not just network requests.