How GKE Networking Scales to Support High Densities of Concurrent AI Agents
Google, Wednesday, October 28th, 2026
Google explains how GKE Dataplane V2 handles network identity scaling to support very large numbers of concurrent AI agent sandboxes.
Agentic and reinforcement learning workloads can require up to a million concurrent code-execution sandboxes with thousands of pods starting and stopping each minute.
Networking efficiency then determines whether GPUs and TPUs sit idle waiting on sandboxes.
The post describes how GKE Dataplane V2, built on eBPF, maps pod labels to numeric identities, and how highly unique per-agent labels (such as agent-id or claim-uid) strain that model.
It covers how Dataplane V2 addresses identity scaling limits to reach sub-second provisioning, and positions Agent Sandbox and Agent Substrate on GKE as the platforms for these dense environments.