Written by Cosmos Darwin, Azure Edge PM, and Michael MacKenzie, VP of Digital Operations
AI is transforming how we work, but so far it's mostly lived on your screen: agents and models assisting with information work. How can that intelligence take on physical work, too? Jobs that happen out in the world, like transporting goods, inspecting equipment, manufacturing products, and serving retail customers. This is already possible today, but developing autonomous robots remains highly complex and specialized. The real breakthrough will come when using AI in physical work is as simple and ubiquitous as it is on a screen. To get there, we need to go beyond software agents and embed intelligence directly into physical systems.
Today at Microsoft Build 2026, we're announcing several new capabilities to help organizations everywhere get started. We're extending AI-ready Azure-managed infrastructure to smaller form factor hardware, bringing Foundry Local to it for running local AI agents and models, and adding support for Azure Kubernetes Service and Azure IoT Operations.
Demo: a simple robot that thinks for itself
Applied in combination, these capabilities can be surprisingly powerful. For Microsoft Build this week, we wanted to show you just how easy this can be. We put together a basic agentic robot using nothing but open-source AI models, commercial off-the-shelf sensors and robot hardware, and the new Azure previews we're announcing today. It's a playful example, but it illustrates what’s possible – check it out:
Lightweight deployments on smaller form factor hardware (preview)
First, we're extending Azure-based provisioning and management to smaller hardware form factors, using a lightweight, performance-oriented architecture built for AI workloads. Unlike hyperconverged and disaggregated deployments, this doesn’t rely on virtualization, and instead runs Linux (initially Azure Linux) directly on bare metal to host containers. You can choose whichever runtime tools you prefer, like Docker, open source k3s, or fully managed Azure Kubernetes Service.
Each deployment is provisioned and managed from the cloud using a new type of resource called Provisioned Machine that looks and behaves a lot like an Azure VM – for example, you can see it in the Azure portal and govern access with Microsoft Entra ID. Over the coming months, we’ll be rolling out more features like update management, metrics, security configuration, and natively configurable child resources for network interfaces and disks.
Screenshot of the new Provisioned Machine resource type in Azure portal.
Provisioned Machines support lifecycle operations centrally from the Azure portal and APIs. Effectively, you can treat physical machines like cloud resources, removing the need for separate on-site IT tools. This makes it much more practical to scale across many distributed locations. For an organization like Chevron, whose operations span field sites around the world, that’s significant:
"Chevron has a growing fleet of industrial edge devices that collect data in the field and increasingly perform local AI processing. Technologies like Azure Local on smaller form factors can help us manage these systems centrally and in a more automated way – reducing complexity compared to the customized OS environments and tools we use today."
— Ed Moore, OT Strategist and Distinguished Engineer, Chevron
Run agents and models locally with Foundry Local (preview)
To embed intelligence into physical systems, Foundry Local is now available as a lightweight container image for Linux infrastructure.
Foundry Local provides a consistent way to deploy and run agents and models, including an inference server that runs alongside your app container and exposes an OpenAI-compatible REST endpoint. It also offers a trusted source for the latest open-source models with an extensive online catalog. Although it integrates closely with Microsoft Foundry, at run time everything stays local: there's no round-trip to the cloud. Data stays on the machine, responses start instantly with zero network latency, and inferences continue even without connectivity. There are no per-token costs, either.
Optimized for edge and industrial form factors, the new Foundry Local preview automatically detects and uses available accelerators like GPUs (and soon NPUs), lining up the full stack for you, from kernel drivers to user-mode libraries. For example, in our demo above, Foundry Local taps an Nvidia RTX 2000E GPU to deliver snappy inferences in real time.
Diagram of the lightweight Linux architecture with container-based Azure services.
More popular Azure services
In addition to Foundry Local, these popular Azure services are validated too:
Azure Kubernetes Service (AKS), the fully-managed enterprise-grade Kubernetes service, now runs directly on bare metal with small form factor deployments – no virtualization layer required. It's the same AKS already available in the cloud and on servers. Once deployed, the cluster looks and works exactly like AKS anywhere else – with Azure-based RBAC, networking, upgrades, monitoring, and even integrations like AKS Fleet Manager – so the controls and tooling you rely on in the cloud extend all the way to the industrial edge. Learn more and join the AKS preview
Azure IoT Operations provides a unified data and control plane for physical assets at the edge. It includes a variety of connectors and an industrial-grade MQTT broker where local agents and logic can run – even with intermittent connectivity – to shape operational data into AI-ready forms, act on it autonomously, and connect into broader cloud analytics and AI systems. It provides a no-code graphical interface to configure data flows and contextualize data before sending it to destinations like Microsoft Fabric for Real-Time Intelligence, and allows you to send messages back to the physical machines it’s connected to. It's already generally available, and as seen in our demo above, it now works on small form factor deployments too. Learn more about Azure IoT Operations
Choose the hardware that fits your requirements
We're delighted to partner with leading makers of edge and industrial computers so you can deploy Azure-managed infrastructure on smaller form factor hardware that’s available to buy today – straight from your preferred vendor or distributor, with no special customization required.
We’re partnering with leading makers of AI-ready edge and industrial computers.
The most compact and affordable options are the ASUS NUC 14 Pro and 15 Pro. At barely 4 inches square and under 2 pounds, they pack the latest Intel® Core™ Ultra processors into a remarkably trim package, well suited to space-constrained scenarios like retail. Learn more about NUC 15 Pro
“With ASUS NUC 14 Pro and 15 Pro, organizations have a powerful yet compact platform for innovation at the edge. When paired with Azure Local, these devices make it easy to deploy, manage, and scale AI workloads at the edge – unlocking real-time intelligence for retail stores and manufacturing environments while maintaining seamless integration with the cloud.”
– (ASUS) KuoWei Chao, General Manager of ASUS NUC Business Unit
For more flexibility, the industrial-grade Lenovo ThinkEdge SE100 offers expandable storage and networking, plus an optional Nvidia RTX A1000 (8GB) or 2000E (16GB) GPU to accelerate demanding edge AI inferencing. Learn more about ThinkEdge SE100
For the toughest operational and regulatory constraints, the OnLogic Helix 521 offers a fan-less design with no moving parts. Designed, assembled, and supported entirely in the USA, it takes the uncertainty out of meeting stringent supply-chain requirements. Learn more about the Hx521
Get started today
We're excited to bring AI-ready infrastructure to where physical work happens, and we genuinely had a lot of fun making the agentic robot demo above. Now it's your turn. Small form factor deployments are available in public preview today, starting in the East US region. There is no charge during the preview. Once your hardware is ready, the Azure-based provisioning experience gets most previewers up and running in about an hour. Instructions to get started are on Microsoft Learn, and if you’d like to engage directly with our team, get in touch here. (If you need to evaluate before committing to hardware, you can spin it up on a virtual machine, though it’s not quite the same as real hardware.)
Whether you're bringing intelligence to a fleet of machines, standing up inference next to your data, or building something we haven't even imagined yet, we can't wait to see what you create!
- Cosmos & Mike
on behalf of our global team in Redmond, Mountain View, Pittsburgh, and Bengaluru
Microsoft