Google and the cloud-native community have consistently strengthened Kubernetes to support modern applications. At KubeCon EU 2025 earlier this year, we announced a series of enhancements to Kubernetes to better support AI inference. Today, at KubeCon NA 2025, we’re focused on making Kubernetes the most open and scalable platform for AI agents, with the introduction of Agent Sandbox.
Consider the challenge that AI agents represent. AI agents help applications go from answering simple queries to performing complex, multi-step tasks to achieve the users objective. Provided a request like “visualize last quarters sales data”, the agent has to use one tool …
Google and the cloud-native community have consistently strengthened Kubernetes to support modern applications. At KubeCon EU 2025 earlier this year, we announced a series of enhancements to Kubernetes to better support AI inference. Today, at KubeCon NA 2025, we’re focused on making Kubernetes the most open and scalable platform for AI agents, with the introduction of Agent Sandbox.
Consider the challenge that AI agents represent. AI agents help applications go from answering simple queries to performing complex, multi-step tasks to achieve the users objective. Provided a request like “visualize last quarters sales data”, the agent has to use one tool to query the data and another to process that data into a graph and return to the user. Where traditional software is predictable, AI agents can make their own decisions about when and how to use tools at their disposal to achieve a user’s objective, including generating code, using computer terminals and even browsers.
Without strong security and operational guardrails, orchestrating powerful, non-deterministic agents can introduce significant risks. Providing kernel-level isolation for agents that execute code and commands is non-negotiable. AI and agent-based workloads also have additional infrastructure needs compared to traditional applications. Most notably, they need to orchestrate thousands of sandboxes as ephemeral environments, rapidly creating and deleting them as needed while ensuring they have limited network access.
With its maturity, security, and scalability, we believe Kubernetes provides the most suitable foundation for running AI agents. Yet it still needs to evolve to meet the needs of agent code execution and computer use scenarios. Agent Sandbox is a powerful first step in that direction.
Strong isolation at scale
Agentic code execution and computer use require an isolated sandbox to be provisioned for each task. Further, users expect infrastructure to keep pace even as thousands of sandboxes are scheduled in parallel.
At its core, Agent Sandbox is a new Kubernetes primitive built with the Kubernetes community that’s designed specifically for agent code execution and computer use, delivering the performance and scale needed for the next generation of agentic AI workloads. Foundationally built on gVisor with additional support for Kata Containers for runtime isolation, Agent Sandbox provides a secure boundary to reduce the risk of vulnerabilities that could lead to data loss, exfiltration or damage to production systems. We’re continuing our commitment to open source, building Agent Sandbox as a Cloud Native Computing Foundation (CNCF) project in the Kubernetes community.
Enhanced performance on GKE
At the same time, you need to optimize performance as you scale your agents to deliver the best agent user-experience at the lowest cost. When you use Agent Sandbox on Google Kubernetes Engine (GKE), you can leverage managed gVisor in GKE Sandbox and the container-optimized compute platform to horizontally scale your sandboxes faster. Agent Sandbox also enables low-latency sandbox execution by enabling administrators to configure pre-warmed pools of sandboxes. With this feature, Agent Sandbox delivers sub-second latency for fully isolated agent workloads, up to a 90% improvement over cold starts.
The same isolation property that makes a sandbox safe, makes it more susceptible to compute underutilization. Reinitializing each sandbox environment with a script can be brittle and slow, and idle sandboxes often waste valuable compute cycles. In a perfect world, you could take a snapshot of running sandbox environments to start them from a specific state.
Pod Snapshots is a new, GKE-exclusive feature that enables full checkpoint and restore of running pods. Pod Snapshots drastically reduces startup latency of agent and AI workloads. When combined with Agent Sandbox, Pod Snapshots lets teams provision sandbox environments from snapshots, so they can start up in seconds. GKE Pod Snapshots supports snapshot and restore of both CPU- and GPU-based workloads, bringing pod start times from minutes down to seconds. With Pod Snapshots, any idle sandbox can be snapshotted and suspended, saving significant compute cycles with little to no disruption for end-users.
Built for AI engineers
Teams building today’s agentic AI or reinforcement learning (RL) systems should not have to be infrastructure experts. We built Agent Sandbox with AI engineers in mind, designing an API and Python SDK that lets them manage the lifecycle of their sandboxes, without worrying about the underlying infrastructure.
This separation of concern enables both an AI developer-friendly experience and the operational control and extensibility that Kubernetes administrators and operators expect.
Get started today
Agentic AI represents a profound shift for software development and infrastructure teams. Agent Sandbox and GKE can help deliver the isolation and performance your agents need. Agent Sandbox is available in open source and can be deployed on GKE today. GKE Pod Snapshots is available in limited preview and will be available to all GKE customers later this year. To get started, check out the Agent Sandbox documentation and quick start. We are excited to see what you build!
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