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Posted on Jun 8 • Originally published at Medium on May 4

Deploying OpenClaw: AI Agent Automation Stack on Microsoft Azure — A Complete Step-by-Step Guide

#cloud #agents #openclaw #aiagentsinaction

AI agents are rapidly evolving from simple assistants into fully autonomous systems capable of orchestrating complex workflows, integrating with tools, and making real-time decisions. As this shift accelerates, developers and organizations need robust infrastructure to deploy, manage, and scale these agents efficiently.

That’s where OpenClaw comes in.

OpenClaw is a powerful AI agent automation stack designed to simplify how you build, deploy, and operate intelligent agents. When combined with the scalability and reliability of Microsoft Azure, it becomes an enterprise-grade solution capable of handling everything from experimentation to production workloads.

In this guide, you’ll learn how to deploy OpenClaw on Azure step by step — from provisioning a virtual machine to accessing the web interface and configuring your first AI agent. Whether you’re a developer exploring agent-based systems or a team looking to operationalize AI workflows, this walkthrough will help you get up and running quickly.

Step-by-Step Guide

Open OpenClaw: AI Agent Automation Stack VM listing on Azure Marketplace.

Click on Get It Now

It will take you to the Product details page. Click on Create.

Select a Resource group for your virtual machine
Select a Region where you want to launch the VM(such as East US)

Note: If you see “This image is not compatible with selected security type. To keep trusted launch virtual machines, select a compatible image. Otherwise, change your security type back to Standard” error message below the Image name, as shown in the screenshot below, then please change the Security type to Standard.

Optionally change the number of cores and amount of memory.

Minimum VM Specs: 16GB RAM /4vCPU

Please note that the VM can also be deployed using an NVIDIA GPU instance to fine-tune models faster. Please check the Publisher recommendations instance type for GPU (Standard_NC4as_T4_v3–4 vcpus, 28 GiB memory) or check the available NVIDIA GPU instances on the Azure documentation page.

Select the Authentication type as Password and enter Username as ubuntu and the Password of your choice.

Optionally change the OS disk size and its type. By default, the VM comes with 64GB of disk.

Optionally change the network and subnetwork names. Be sure that whichever network you specify has ports 22 (for SSH), 3389 (for RDP), 80 (for HTTP), and 443 (for HTTPS) exposed.

The VM comes with the preconfigured NSG rules. You can check them by clicking on the " Create New option available under the security group option.

Optionally, go to the Management, Advanced, and Tags tabs for any advanced settings you want for the VM.
Click on Review + create and then click on Create when you are done. The virtual machine will begin deploying.

A summary page displays when the virtual machine is successfully created. Click on the " Go to resource link to go to the resource page. It will open an overview page of the virtual machine.

If you want to update your password, then open up the left navigation pane, select Run command, select RunShellScript, and enter the following command to change the password of the VM.

sudo echo ubuntu:yourpassword | chpasswd

Now that the password for the Ubuntu user is set, you can SSH to the VM. To do so, first note the public IP address of the VM from the VM details page, as highlighted below

Open Putty, paste the IP address, and click on Open.

You can also connect to the VM’s desktop environment from any local Windows machine using RDP protocol or a local Linux machine using Remmina.
To connect using RDP via a Windows Machine, first note the public IP address of the VM from the VM details page, as highlighted below.

Then, from your local Windows machine, go to the “Start” menu, in the search box type and select “Remote Desktop Connection”. In the “Remote Desktop Connection” wizard, copy the public IP address and click Connect

This will connect you to the VM’s desktop environment. Provide the username (e.g., “ubuntu”) and the password set in step 4 to authenticate. Click OK

Now you are connected to the out-of-the-box “OpenClaw: AI Agent Automation Stack” VM’s desktop environment via Windows Machine.

To connect using RDP via a Linux machine, first note the external IP of the VM from the VM details page, then from your local Linux machine, goto menu, in the search box type and select “Remmina”.

Note: If you don’t have Remmina installed on your Linux machine, first install Remmina as per your Linux distribution.

In the “Remmina Remote Desktop Client” wizard, select the RDP option from the dropdown and paste the external IP, and click Enter.

This will connect you to the VM’s desktop environment. Provide “ubuntu” as the username and the password set in the above reset password step to authenticate. Click OK

Now you are connected to the out-of-the-box “OpenClaw: AI Agent Automation Stack” VM’s desktop environment via a Linux machine.

The VM will generate a random password to log in to the OpenClaw Web Interface. To get the password, connect via the SSH terminal as shown in the steps above and run the command below.

sudo cat /home/ubuntu/setup/.gateway_pass

To access the Open Claw Web Interface, copy the VM's public IP address and paste it into your local browser as https://public_ip_of_vm. Make sure to use https and not http.

The browser will display an SSL certificate warning message. Expand the warning message, accept the certificate warning, and continue.

It will open a login page. Provide the password we got at step 14 above and click connect.

After the connection is successful, it will ask you to pair the device. For that, go back to the SSH terminal and run the following command to get the request ID.

openclaw devices list

To approve the request, replace the request ID obtained in the previous step in the command below.

openclaw devices approve <requestId>

e.g openclaw devices approve 31b187f9–4545–44fc-bf49-dd1553753b25

Note: Sometimes the request may expire if there is a delay in approving it. If the request gets rejected, simply rerun the “openclaw devices list” command to obtain a new request ID, and then run the approve command using that ID. Also, each browser profile generates a unique device ID, so switching browsers or clearing browser data will require re-pairing. And lastly, if your VM's IP address is dynamic, which changes on VM reboot, then you also need to repair your device.

Now you are logged in to the OpenClaw Web Interface. You can set up your Agent, configure various channels, and start the automation.

By default, the LLM model set is “gpt-oss:20b”. You can pull other ollama models and switch them to primary models by running the commands below.

ollama pull <modelname>

e.g ollama pull llama3.1:8b

openclaw models set ollama/llama3.1:8b

Once the model is switched, go back to the Web Interface, refresh the page, and select the new model from the model dropdown. Once the model is loaded successfully, you can run your queries.

For more details, please visit the Official Documentation page

Conclusion

Deploying OpenClaw on Microsoft Azure unlocks a powerful combination of flexibility, scalability, and control for building AI-driven automation systems. With just a few setup steps, you gain access to a fully configured environment where you can run agent workflows, integrate LLMs, and experiment with multi-agent architectures.

From provisioning the VM and configuring secure access to connecting via SSH/RDP and setting up models, the process is straightforward once you understand each component. The ability to switch models, leverage GPU instances, and manage agents through a web interface makes OpenClaw especially valuable for both prototyping and production use cases.

As AI agents continue to redefine how software interacts with the world, tools like OpenClaw provide the foundation to build smarter, more autonomous systems. Now that your environment is ready, the next step is to start experimenting — create agents, connect integrations, and push the boundaries of what automated intelligence can do.