Today, we are announcing the availability of Agent Substrate on Google Kubernetes Engine (GKE). Agent Substrate is an open-source, secure-by-default agent execution runtime engineered to run millions of sandboxes with 10x higher density than standard container runtimes . Purpose-built for the era of autonomous agents, Substrate delivers sub-500ms resume operations at over 500 suspend/resume activations per second with a native zero-trust kernel and network isolation. Agent Substrate is available as an open-source solution that runs on any Kubernetes infrastructure and is optimized for GKE.
Leading AI teams are already building on it: Nous Research , the team behind the Hermes Agent , is actively building on top of Agent Substrate. Hermes is currently ranked the #1 AI agent globally by OpenRouter usage across productivity, coding, CLI, and personal agents. Developers already run Antigravity, Claude Code, Codex, OpenClaw, Hermes, and other harnesses locally but that’s fundamentally than running hundreds of thousands of concurrent, long-lived agents that generate code, interact with tools, and drive automated execution — challenges that existing architectures often struggle to meet.
Scaling an agent platform from a local prototype to running agents at scale fundamentally changes your infrastructure constraints, which can include: Opaque trust boundaries: Models can generate and run arbitrary code on the fly. Without kernel-level isolation and dynamic network controls, running untrusted code that no human has ever looked at risks host escape, credential theft and data exfiltration. Tool access friction: Agents need full computer environments to invoke command-line tools, headless browsers, and filesystem workspaces. Running these safely needs to be fast and easy.
Massive bursts: Agent harnesses, benchmarks, and reinforcement learning rollouts can generate thousands of sandboxes per minute. General-purpose schedulers struggle under this churn, and repeatedly decompressing container images can cause severe disk contention. Idle compute: Autonomous agents spend the vast majority of their time dormant while waiting on model inference, tool responses, or human feedback. Reserving dedicated CPU and RAM for idle containers wastes valuable resources . When platform teams hit these challenges, they face an unacceptable trade-off: sacrifice control and isolation, or deal