GeezerVison

Wednesday, November 11, 2026

by Kurtis Van Gent, Google Cloud, and Daryl Ducharme, Google Open Source Programs Office

Building production AI agents requires more than a capable reasoning model. Agents need a reliable way to connect to external systems and data, and they need procedural domain knowledge so they know how to use those tools safely without exhausting their context window.

Over the past year, open source communities have rallied around two complementary open standards to solve these challenges: the Model Context Protocol (MCP) for connecting agents to live tools, and Agent Skills for loading procedural instructions and scripts on demand. Following Anthropic's donation of the Agent Skills specification to the Linux Foundation's Agentic AI Foundation (AAIF) alongside MCP, engineers from Google, Anthropic, OpenAI, and the wider ecosystem are now co-maintaining the standard under vendor-neutral governance.

To explore how open standards prevent ecosystem fragmentation, and why the best solutions don't always have to be homegrown, we sat down with Kurtis Van Gent, co-creator of the open source MCP Toolbox for Databases and a core maintainer on both MCP and Agent Skills.

From the N-by-M database problem to open source collaboration

Daryl Ducharme: How did your work on AI ecosystems for Google Cloud Databases and co-creating the open source MCP Toolbox for Databases lead to your role as a core maintainer on both the Model Context Protocol (MCP) and Agent Skills?

Kurtis Van Gent: When I started my current role around three years ago, it was early in the generative AI cycle. My mission was to make it easy for developers to use cloud databases for their AI applications. As a strong proponent of developer experience, I loved that charter. As model reasoning improved, context and data were clearly the keys to making effective use of it, and there's no better place to store data than a database.

At the time, most database conversations focused on retrieval-augmented generation (RAG) using vector search. However, our team believed that agents and tools held the most interesting potential for large language models (LLMs) going forward. We immediately hit two scaling challenges: Google Cloud supports more than 15 different databases across its portfolio, and new open source agent frameworks (starting with LangChain and LlamaIndex) were emerging every week.

That created a classic N-by-M integration problem between many clients and many data hosts. To solve it, we started GenAI Toolbox for Databases (now MCP Toolbox for Databases) as an open source framework for building and serving tools. We built our own server, clients, and protocol to standardize how agents look up and invoke database tools. Back then, agent terminology wasn't common vernacular yet, so every talk I gave had to start with three to five slides explaining what agents and tools actually were.

When Anthropic announced MCP, it was exciting validation that the broader ecosystem was arriving at the same architectural conclusions we were seeing across our database integrations. Rather than insisting on a homegrown protocol or spinning off a competing specification, we reached out to the MCP co-creators, Justin Spahr-Summers and David Soria Parra, and began meeting regularly. Because I believe strongly in open source communities and shared standards, I advocated for adopting MCP directly. We evolved our project into MCP Toolbox for Databases so it worked out of the box with any MCP client, and I focused on contributing capabilities from our original protocol back into the upstream MCP specification.

Leading the transition to a stateless MCP specification

Kurtis Van Gent: One of my primary upstream focuses was transport scalability. I began leading the MCP Transports Working Group with the goal of evolving the specification toward a stateless, horizontally scalable architecture. At first, it wasn't a crowded room. Our early working group meetings had around five participants, three of whom were from Google.

As MCP adoption accelerated across the industry, however, it became clear that early stateful assumptions around persistent sessions and initialization were holding MCP back at production scale. That effort culminated in a two-day Transports Summit where Google and fellow working group members hosted the MCP core maintainers at our San Francisco offices to work through the scaling bottlenecks and align on a path forward. Shortly afterward, I was invited to become an MCP core maintainer, both in recognition of our work identifying those transport challenges and to keep the Transports Working Group closely aligned with the core specification. The result of that collaboration was the July 28, 2026 MCP specification, which transitioned MCP to a stateless model.

That experience as an MCP core maintainer naturally positioned me to help co-maintain Agent Skills. Having already built working relationships with contributors across Google, Anthropic, and OpenAI, stepping in to help steward Agent Skills inside the AAIF felt like a natural continuation of the same collaborative model.

Why vendor-neutral governance in the AAIF matters for developers

Daryl Ducharme: From your perspective as a core maintainer representing Google, why does bringing Agent Skills into the Linux Foundation's Agentic AI Foundation (AAIF) alongside MCP matter for developers and open source communities?

Kurtis Van Gent: We've seen open source technologies like Kubernetes achieve widespread adoption and sustained cross-company contributions, but organizations are understandably hesitant to build critical workflows on a specification controlled by a single vendor. Just as the Cloud Native Computing Foundation (CNCF) provided a vendor-neutral home for container orchestration, the AAIF provides a neutral forum where organizations and independent maintainers can collaborate on shared agent standards.

Bringing Agent Skills into the AAIF signals how foundational this format has become across the AI ecosystem. Following in the footsteps of MCP, the organizations and open source communities adopting Agent Skills now have a shared seat at the table to shape how the specification evolves. For developers, this means you can author a skill once and trust that it will remain portable across different agents, IDEs, and harnesses without vendor lock-in.

Solving tool bloat with progressive disclosure: how MCP and Agent Skills fit together

Daryl Ducharme: You have spoken about the tension between "tool bloat" and context saturation when building production agents. How should developers think about the division of labor between MCP (connecting agents to live tools and data) and Agent Skills (loading procedural domain knowledge on demand)?

Kurtis Van Gent: One of the most impactful things MCP accomplished was commoditizing tools: it became straightforward for anyone to wrap a set of actions in an MCP server and share them. The unintended consequence was tool and context bloat. As agents grew more capable and connected to dozens of MCP servers, deciding which tool schemas and operational instructions to load into the context window became a major bottleneck.

Agent Skills introduced a remarkably simple, effective pattern for solving that problem through progressive disclosure. By loading only a lightweight YAML frontmatter name and description at startup, and pulling the full SKILL.md instructions, reference files, or scripts into context only when a task requires them, Agent Skills commoditized context the same way MCP commoditized tools.

While some observers initially viewed MCP and Agent Skills as competing approaches, they have always been complementary: Agent Skills package domain expertise into modular chunks that teach an agent how to approach a problem, while MCP provides the live execution interface to carry it out. Today, the broader ecosystem has converged on combining them, including efforts around Agent Plugins and proposals for serving Agent Skills over MCP so platform maintainers can distribute domain instructions and live tools together.

Putting MCP and Agent Skills to work in database and developer workflows

Daryl Ducharme: What is a concrete example of how developers are using Agent Skills and MCP together today—whether in Antigravity, database workflows, or across the broader ecosystem?

Kurtis Van Gent: Both MCP and Agent Skills have become ubiquitous across everyday developer workflows. In my own daily work, I write custom skills whenever I notice an agent struggling to get a repeatable workflow right on the first try. For example, I write skills for how I like to structure GitHub repositories or format commit messages so I never have to re-explain those conventions in a prompt.

At the platform level, our Google Cloud Databases team pairs MCP and Agent Skills together inside our Agent Plugins. When a developer using Antigravity connects to a PostgreSQL database on Cloud SQL or AlloyDB, they can install the corresponding Cloud SQL or AlloyDB plugin. From MCP, the agent gets a list of safe actions it can take against the database. From the bundled Agent Skills, it gets step-by-step instructions on how to use those tools for specific tasks, like provisioning an instance or configuring Identity and Access Management (IAM) database authentication securely—and because both specifications are open standards, the same SKILL.md and MCP pattern works across any open source database or agent harness.

That combination is both faster and safer than pointing an agent at raw documentation written for human readers. In our agent workflow benchmarks, we see developers complete complex database tasks much more quickly and reliably when MCP tools are paired with targeted Agent Skills.

What comes next for the Agent Skills specification

Daryl Ducharme: Working alongside maintainers from Anthropic, OpenAI, and the wider community inside the AAIF, what technical or governance areas are you most focused on next?

Kurtis Van Gent: Two major areas I'm focused on evolving next are packaging and discovery. Today, the Agent Skills specification defines how an individual skill is structured, but it doesn't yet standardize how collections of skills are bundled, how harnesses discover them, or how to resolve naming conflicts across skill sets. Closing those gaps will help address conformance differences across harnesses.

Versioning is another priority. As the Agent Skills specification matures under the AAIF, we need a clear, portable way for skill authors and agent harnesses to declare which version of the specification they support. I'm looking forward to working with the other maintainers and open source communities to standardize packaging, discovery, and versioning in upcoming releases.

How to get involved

Daryl Ducharme: For open source developers and maintainers who want to author portable skills or contribute to the specification and tooling at github.com/agentskills, where should they start?

Kurtis Van Gent: If you want to start authoring portable skills today, the documentation at agentskills.io walks through creating your first SKILL.md package in just a few minutes, and you can explore Google's open source catalog of developer skills at github.com/google/skills (along with our engineering retrospective on how we ship Google Agent Skills like products).

If you want to help shape the specification itself, visit the GitHub organization at github.com/agentskills to review open pull requests, file issues, and join the community Discord server where maintainers and contributors discuss upcoming specification proposals.

Tuesday, November 10, 2026

by Stephanie Taylor, Google Summer of Code Program Lead

Last weekend, we hosted our annual Google Summer of Code Mentor Summit in Munich, Germany, welcoming 185 attendees representing 133 open source projects across 36 countries. The past year has presented unique challenges for open source communities, particularly with the surge of AI-generated pull requests placing additional strain on maintainers.

The 183 open source organizations participating in GSoC 2026 were at the forefront of these shifts. Each organization establishes its own AI guidelines and governance, and many continue to refine their policies as the technology evolves. Throughout the year, we have actively engaged with our communities to better understand these challenges and support them in identifying effective solutions.

Unsurprisingly, the Mentor Summit’s unconference format (where attendees create the schedule) featured numerous sessions on managing AI within both GSoC and the broader open source ecosystem. While many mentors arrived weary after a demanding season, the event fostered a strong sense of shared purpose. Discussions moved beyond common obstacles to highlight notable successes, with several organizations reporting their most impactful GSoC season yet, driven by dedicated contributors who are now transitioning into active maintainer roles.

Organizations shared a variety of strategies to address AI-assisted contributions, specifically targeting automated pull requests and supporting contributors in understanding underlying system architectures alongside AI-generated code.

Resilience emerged as a central theme of this year's summit. Open source communities have navigated dynamic shifts over the past two decades. The continued vitality and importance of open source in 2026 reflects a forward-looking mindset: one grounded in experimentation, adaptation, and continuous improvement.

For our GSoC team, the Mentor Summit underscored the remarkable collaboration across open source. Observing mentors from diverse projects exchange practical tools, workflows, and insights highlights the value of gathering community leaders to strengthen the broader ecosystem.

A huge thanks to all of the GSoC Mentors and Organization Administrators who joined us in Munich, as well as the more than 1,800 mentors worldwide who guided our 2026 contributors. Google Summer of Code wrapped up its 22nd year this week thanks to the dedication and leadership of this global community.

Thursday, November 5, 2026

by Mofi Rahman, Developer Relations Engineer, and Brandon Royal, Product Manager, Google Cloud

When Google introduced Kubernetes twelve years ago, the Pod became the default unit of compute for the cloud. The design assumed a straightforward lifecycle: a program boots, binds to a port, serves traffic, and runs until you stop it.

AI agents work fundamentally differently.

An AI agent that’s running on a machine executes untrusted code, commands, and tool calls, and must be isolated from its host and its network resources. At the same time, these agents, whether they’re an interactive coding assistant, an SRE troubleshooter, or a multi-turn evaluation loop, do not run continuously. They execute a step, call a language model, and wait. Most of the time, the agent does nothing at all while it waits for a model to stream tokens or a human to review a pull request. In the same way that Kubernetes was created to address the needs of containerized applications, we introduced Agent Substrate as a new orchestrator for the emerging agentic applications. Secure by default, highly efficient and built for multi-million agent scale.

Agent Substrate was designed from the ground up for AI agent security and density and brings the best parts of Kubernetes together with a new control and data plane for fully-isolated, ultra-low-latency sandboxed compute and storage. The growing Agent Substrate community has been rapidly building core capabilities and stabilizing APIs, culminating in the latest 1.0 release.

This week at KubeCon North America in Salt Lake City, Google Cloud is donating Agent Substrate and gVisor to the Cloud Native Computing Foundation (CNCF) in Sandbox status, alongside OpenRL.

This post explains why traditional approaches struggle with stateful agents, how Agent Substrate solves the problem, how it connects to enterprise governance on Google Kubernetes Engine (GKE), and where to see it in action at KubeCon NA 2026.

Challenges of running AI agents at scale

AI agents introduce two infrastructure bottlenecks that standard Kubernetes was never built to handle: idle resource waste and control plane overload.

1. The idle resource problem

During a typical interactive session, an agent can spend 90% to 95% of its time waiting for model inference, external APIs, or human reviews.

During that wait, the agent consumes zero CPU. However, you cannot simply kill the container. The agent holds critical state in memory and on disk: active language runtimes, local Git repositories, uncommitted file edits, and tool sessions.

With standard Kubernetes, platform engineers face two poor options:

1. Keep a dedicated Pod running all day: The agent responds quickly because it is already warm, but you pay for idle compute around the clock. At ten thousand active users, cluster costs become unsustainable.
2. Scale to zero with serverless containers: You save compute costs while the agent sits idle, but resuming from cold storage forces the container to clone repositories, reinstall packages, and rebuild memory. Wakeup latency climbs past 20 seconds, breaking the interactive experience.

Platform teams need agents that wake up in under 500 milliseconds with full memory and disk state intact—without paying for running Pods while the agent sits idle.

2. Control plane overload

The opposite issue occurs when training or evaluating agents. Reinforcement learning loops and benchmark suites need to run untrusted agent code across thousands of tasks at the exact same time.

If every sandbox requires a full Kubernetes Pod, the control plane quickly grinds to a halt. Scheduling, creating, and destroying thousands of short-lived Pods every minute overwhelms etcd, saturates kube-apiserver, and exhausts subnet IP addresses. Kubernetes was built to manage stable application topologies, not to act as a microsecond sandbox allocator.

How Agent Substrate solves the problem

Agent Substrate adds an actor-based control and data plane built on top of Kubernetes. It solves the idle resource problem by decoupling Actors from Workers:

• Actors hold the state: An Actor represents an individual agent session, complete with its own filesystem, environment variables, memory state, and network rules.
• Workers provide the compute: A Worker is a pre-warmed Kubernetes Pod running in a shared pool (WorkerPool), equipped with container runtimes and sandbox boundaries.

The lifecycle of an agent turn

1. Request parking: An incoming call hits the atenet-router L7 proxy. If the target Actor is suspended, the proxy holds the request in memory and alerts the Substrate controller.
2. Fast placement: The controller assigns the Actor to an available slot in the warm Worker pool.
3. Sub-second restore: The Actor’s memory and disk state restore from tiered storage, the process wakes up, and the proxy delivers the request. The agent resumes in under 500 milliseconds right where it paused.
4. Suspension on idle: When the turn finishes, Substrate snapshots the Actor’s state to tiered storage and frees the Worker slot for other workloads.

Because Substrate manages Actor state directly, thousands of agents can activate, suspend, and resume every second without creating new Pod objects or stressing etcd.

Safe execution with gVisor and microVMs

Agents frequently execute model-generated, untrusted code. Standard Linux containers share the host kernel, which presents an unacceptable security risk on multi-tenant nodes.

Agent Substrate pairs with gVisor (runsc), Google's open source application kernel written in Go. gVisor intercepts system calls in user space, isolating untrusted code from the host kernel. Because gVisor virtualizes its own memory and process tables, Substrate can freeze a running environment, write memory pages to storage, and restore them on another node in under 500 milliseconds.

For workloads requiring hardware virtualization or custom kernel modules, Substrate also supports Cloud Hypervisor microVMs under the same unified API.

Why Google donated Agent Substrate to the CNCF

Over the past two years, engineering teams across the industry have run into the same agent scaling boundaries. Without a shared open standard, every organization ends up building custom schedulers on top of VMs or working around Kubernetes.

Donating Agent Substrate and gVisor to the CNCF gives the cloud-native community a neutral, vendor-independent foundation that works across any conformant Kubernetes cluster.

Together with community projects, they form an open stack for agentic infrastructure:

• Layer 1: Secure Isolation (gVisor): Sandboxes untrusted code execution with user-space kernel isolation and fast memory checkpointing.
• Layer 2: Agent Runtime (Agent Substrate): Multiplexes stateful actors onto warm worker pools with sub-second wakeups.
• Layer 3: Training Orchestration (OpenRL): Coordinates Kubernetes-native reinforcement learning loops and evaluation sandboxes.

Where to find us at KubeCon NA 2026

If you are heading to Salt Lake City for KubeCon + CloudNativeCon North America from November 9 to 12, visit our sessions, workshops, and booth demos:

Get started

Agent Substrate is open source and ready for testing today:

• Documentation: Read the guides and architecture specs at ate.dev.
• Code: Explore the runtime on GitHub at github.com/agent-substrate/substrate.
• Community: Join #substrate-dev #substrate-user on the CNCF Slack.

See you in Salt Lake City!

by Oleg Shaldybin and Monica Song, Google TPU Software

Today we are excited to announce the launch of XLA Serving Engine (XSE), a general-purpose model export and inference runtime for the OpenXLA ecosystem, open-sourced under the Apache 2.0 license.

We created XLA Serving Engine to enable a “Train in Python, Serve in C++” workflow. Most ML engineers develop models using Python (with frameworks like JAX and PyTorch); however, some production serving environments do not allow Python due to operational and deployment complexity. XLA Serving Engine offers a solution by providing an API for engineers to export their ML models to an “XLA SavedModel” containing the StableHLO representation of their model’s function, which can then be loaded and executed by the XLA Serving Runtime.

This approach allows users to serve models (typically non-LLMs) directly in a C++ runtime, which enables high throughput at any scale and optimizes for low latency by removing the overhead of the Python interpreter. In our studies, we found that for short requests, XSE delivers 2–5x the throughput of the best Python server we could build, and 8–36x that of a straightforward one, while using 30–70% less CPU time per prediction and a quarter of the memory.

Last year, we began deploying core components of XLA Serving Engine as part of Google’s internal load-bearing, production serving infrastructure. Today, XSE serves over 6 million queries per second and powers some of Google's highest-volume online inference pipelines, including YouTube Shorts, Google Ads, Search, and parts of Gemini Omni, with plans to onboard more customers internally.

Having successfully scaled XSE to support the high-throughput workloads of our internal teams, we are now ready to bring this core piece of infrastructure to the wider ecosystem of hardware partners and Cloud TPU users. We designed XLA Serving Engine to be self-contained and modular, so users can deploy it as a standalone inference engine or plug it into their more complex serving solutions.

Core production-ready features

• Python-Free Serving: XSE enables a “Train in Python, Serve in C++” workflow via the intermediary compiled XLA SavedModel serialization format. In our studies, we found that XSE delivers 2–5x the throughput of a pure Python server.
• Low-latency execution: The XLA Serving Runtime wraps PJRT directly and optimizes the model using the XLA compiler. This eliminates Python runtime overhead and the global interpreter lock. XSE is C++ native, which means one process is capable of using all eight cores.

• Orbax Checkpointing Integration: XLA SavedModel stores JAX model computations as StableHLO and uses Orbax checkpoint to store the weights.
• Flexible Scale: Supports both single-host and multi-host serving by leveraging PJRT.
• Performance Parity: XSE preserves the performance of the model’s forward function (including kernel optimizations). Hand off your model to XLA Serving Engine with confidence that performance will be identical to its training environment.
• Docker Packages: We provide a suite of Docker images that are tested on CPU, GPU, and TPU and expose simple gRPC APIs for calling your model.

Customer testimonials and partner use cases

Our Private Preview partners have been actively leveraging XLA Serving Engine to bring their deep learning stacks to production. Here is a selection of private preview customers (alphabetized) to showcase the diversity of uses:

Arm: Performance Parity on Arm Neoverse platforms

Our work with Arm has proven the production capabilities of JAX and XLA for training and serving on Arm-based CPUs:

“As AI inference scales, software optimization is critical to delivering performance and efficiency across diverse workloads. Through our collaboration with Google and contributions to OpenXLA, we’ve optimized the XLA CPU backend and serving runtime for vision, embedding and recommendation workloads on Arm Neoverse-based platforms, including Google Cloud Axion.”

— Ian Bratt, Vice President of ML Technology and Fellow, Arm

Intel: Native JAX on Xeon server CPUs

We have also been partnering with Intel to showcase the power of Intel’s optimized compute:

“Our partnership with Google showcases the XLA Serving Engine (XSE) on Intel® Xeon® processors, delivering native, high-performance JAX inference accelerated by Intel® AMX. With XSE, enterprises get out-of-the-box performance across Vision, Recommendation, and LLM workloads — and the flexibility to serve them efficiently wherever their business runs, from cloud to edge to on-prem.”

— Bill Pearson, Vice President of Data Center and AI software, Intel

Moloco: Scaling advertising and recommenders

“To support our demand-side platform (DSP) processing over 650 billion daily inferences and peaking at 10.5 million QPS, we needed an inference runtime that could match our massive scale. XLA Serving Engine is the high-performance, C++-based JAX serving solution we’ve been waiting for that keeps us framework agnostic and complements our Cloud TPU Trillium investments.”

— Kunal Kukreja, Staff Machine Learning Engineer, Moloco

What’s next

• Torch Support: We plan to integrate with TorchTPU, allowing PyTorch users to export to an XLA SavedModel to serve their models on TPUs in C++.
• Ahead-of-Time (AOT) Compilation: We are working on rolling out AOT compilation to our internal users before bringing this feature to external users. AOT compilation will reduce cold-start time by offering users the ability to compile their models offline (on CPU) for their target serving hardware.
• C++ Data Preprocessing: We are offering a C++ based preprocessor for users who want to combine their data processing with their model’s graph in C++.

Limitations

XSE is a lightweight, one-shot inference engine. For large autoregressive language models with long context lengths, we recommend popular projects such as vLLM or SGLang, which benefit from LLM-specific memory management techniques.

Get started today

Ready to streamline your JAX production pipeline?

• Check out the XLA Serving Engine quick-start instructions at github.com/openxla/xla-serving. You can find the list of available Docker images here.
• File an issue or contribute pull requests at github.com/openxla/xla-serving.

Acknowledgements

Thank you to everyone who contributed to XLA Serving Engine, including but not limited to the following individuals:

Ashish Agarwal, MD Faijul Amin, Carlos Araya, Renato Arantes, Isha Arkatkar, Sagun B, Ashok Bhat, Xevi Miró Bruix, Chris Chan, Ray Chen, Chen Cheng, Hyun Min Choi, Andrea Covre, Gauri Deshpande, Tiffany Dong, Sayce Falk, Happy Gao, Vaibhav Gurunathan, Michael Hudgins, Kritanjali Jain, He Jiang, Leon Kennedy, Young Seok (Tony) Kim, Penporn Koanantakool, Kunal Kukreja, Younghee Kwon, Shawn Lee, Shawn Lu, Siddhartha Menon, Marek Michałowski, Sachin Muradi, César Naranjo, Pat Notz, Adam O’Brien, Olivia Qin, AG Ramesh, Shubha Rao, Mayank Raunak, Crefeda Rodrigues, Gassan Salama, Oleg Shaldybin, Brian Seung, Juanli Shen, Alex Shires, Jeyeon Si, Prairie Sims, Monica Song, Anne Stern, Quoc Truong, Armando Ugalde Velasco, Jess Wiser, Siqiao (James) Wu, Qinglan Xiang, Yunjie Xu, Jeffrey Yoo, Hyungsuk Yoon, and Wangyuan Zhang.

Wednesday, November 4, 2026

by Siyuan Zhang & Antonio Ojea, Google Cloud

If you're an ML engineer or researcher, the GPUs and TPUs you need increasingly live on Kubernetes, but Kubernetes should be an invisible layer. While your platform team sets up the GKE cluster and manages accelerator capacity and reservations, your job is to train and ship models—not to manage Kubernetes infrastructure.

Today we're introducing GKE Workspaces, a self-hosted AI/ML development platform for Google Kubernetes Engine, built on the open source Kubeflow Workspaces project. Deployed on your team's GKE cluster, it provides a dedicated web dashboard where you can pick an IDE and a machine, and start coding on cloud accelerators in minutes.

Why Kubeflow Workspaces

Kubeflow Workspaces gives every researcher a personal, persistent development environment on shared cluster hardware.

• Self-service, in a few clicks. Sign in to your team's Workspaces web UI, choose JupyterLab or in-browser VS Code, pick an environment, and select a hardware size such as "Small CPU", "GPU", or "TPU". Researchers don't need Google Cloud Console access, YAML, or command-line setup.
• Your work persists. Each workspace mounts its own home directory on a dedicated, encrypted Persistent Disk in your Google Cloud project—isolated to that workspace, so your files, notebooks, and installed packages stay safe across restarts, pauses, and machine changes.
• Guardrails set by your platform team. Admins curate the approved images and hardware options, and decide which machines, reservations, and capacity types (such as on-demand or Spot) back them. Researchers only see choices that work.
• Open source and portable. Workspaces is part of the Kubeflow community, empowering you with the freedom and flexibility of a fully open source notebook ecosystem.
• Upstream integration and open licensing. GKE Workspaces builds directly on Kubeflow Workspaces with key enhancements for GKE environments—including standalone deployment without Istio dependencies, integrated authentication token flows, and stateful GPU/CPU pause and resume capabilities. Both GKE Workspaces and upstream Kubeflow Workspaces are released under the Apache 2.0 open source license, and we are actively working with the Kubeflow community to upstream these features.

What's unique about GKE Workspaces

GKE Workspaces brings Kubeflow Workspaces together with capabilities that are only available on Google Cloud.

1. Pause and resume—without losing your state

You've spent an hour loading a large model into GPU memory and building up results across notebook cells. Now it's time to go home. Normally you have two bad options: shut down and lose everything, or leave it running and pay for an idle GPU overnight.

With GKE Workspaces, you just pause by clicking a button from the Workspaces Dashboard. Built on Pod Snapshots, GKE takes a snapshot of your running notebook—including variables in memory and models loaded on the GPU—saves it to Cloud Storage, and releases the hardware so you stop paying. When you resume, you pick up exactly where you left off. Workspaces can also be paused automatically after a period of inactivity.

2. An easy on-ramp to Cloud TPUs

Moving from a quick experiment to large-scale training on TPUs used to mean switching tools at every step. GKE Workspaces keeps you in the same notebook the whole way, with both JAX and PyTorch:

1. Prototype on a single TPU chip, just like you would on a GPU.
2. Scale to multiple chips on one machine by choosing a bigger hardware option.
3. Scale out to multi-host TPU slices by sending the same training function from your notebook to a larger slice. Logs stream back to your notebook, and the TPUs are released as soon as the job finishes.

3. Use your local coding agents with cloud accelerators

Prefer using your own AI coding agents and editors locally? You can easily bridge them to cloud compute. In the Workspaces web UI, generate a short-lived, revocable link to run your notebooks and agent-generated code on a remote GPU or TPU. Traffic is secured over HTTPS via Google Identity-Aware Proxy and the cluster's access proxy, checking permissions on every request—so no SSH keys, local tunnels, or cluster credentials ever sit on your laptop.

4. A launchpad for distributed work

Your workspace doesn't have to be big to drive big jobs. From a small, inexpensive CPU notebook you can process data with Spark, launch multi-host training, serve your model, and scale Ray clusters—all from Python. Built-in Cloud Storage access, with no keys to manage, makes it easy to pass data between each step.

5. A portal to safe elastic sandboxes for coding subagents

Running a fleet of coding agents directly on your local machine can drain your battery, expose sensitive files or credentials to agent-executed code, and halt progress as soon as you close your laptop. From a standard GKE Workspace, you can safely task your favorite coding agent to hand off parallel work—such as resolving failing tests across a repository—to background subagents. Crucially, while your primary workspace operates as a full-featured environment, these subagents are isolated within lightweight, in-cluster sandboxes using providers like Agent Substrate, requiring zero local setup. Each subagent executes independently in its own sandbox on shared cluster nodes, continues running even when you log off, snapshots to Cloud Storage at zero compute cost while idle, and outputs a clean patch for your review and merge.

6. Lightweight and secure by default

GKE Workspaces runs on its own on GKE Standard or Autopilot—you don't need to install the full Kubeflow platform. Users sign in with their Google accounts, traffic is encrypted with Google-managed certificates, and each team's work is isolated from the others.

Get started

GKE Workspaces lets ML teams focus on models, not infrastructure: one-click development environments, stateful pause and resume, a smooth path to Cloud TPUs, and the power of GKE behind every notebook.

Ask your platform team to deploy it using the GKE deployment guide, and learn more about the upstream project at Kubeflow Workspaces. We welcome feedback and contributions for both Apache 2.0-licensed projects.