Across Microsoft, our product teams rely on lab environments to develop, test, validate, and troubleshoot products before they reach customers. These labs are typically made up of virtual machines (VMs) that can be quickly deployed and scaled as needed.
At Microsoft Digital, the company’s IT organization, we now offer Managed Cloud Labs (formerly known as MyWorkspace), a self-service, Azure-based platform that enables our employees to create and manage the lab environments they need. With more than 150,000 VMs hosted in Managed Cloud Labs across the organization, keeping those environments healthy is essential to maintaining engineering productivity.
When a VM becomes unavailable, however, diagnosing the issue can require significant manual effort. Today, an engineer may begin troubleshooting by attempting to access the VM, reviewing logs, and running diagnostic commands to identify the root cause. While this process can sometimes resolve the problem, it requires the engineer to remain actively involved throughout the investigation, validating findings and testing potential fixes.
At the scale of Managed Cloud Labs, even routine VM issues can add up to a significant productivity cost. That’s why our team went looking for a way to automate troubleshooting. The answer was AI.
“The Troubleshooting Agent is the first integration of Azure Serial Console and AI that streamlines VM troubleshooting. It significantly reduces the toll on support engineers and speeds up problem resolution.”
Dominika Bobik, technical lead, Azure Serial Console
We and our partners on the product group side engineered a solution for Managed Cloud Labs VMs using Azure Serial Console and agentic AI. This approach—which includes an Info Agent and a Troubleshooting Agent—has been a great success, allowing engineers to identify what’s broken and repair the issue in minutes instead of hours.
“The Troubleshooting Agent is the first integration of Azure Serial Console and AI that streamlines VM troubleshooting,” says Dominika Bobik, a technical lead in the Azure Serial Console product group. “It significantly reduces the toll on support engineers and speeds up problem resolution.”
A new path for VM support issues
When we analyzed our support incidents, two recurring challenges emerged: Users needed faster answers to everyday how-to questions, and they needed a quicker path to resolution when remote access to their virtual machines via Remote Desktop Protocol (RDP) failed.
For how-to questions, the issue usually wasn’t the platform. Our employees were often blocked because they needed assistance with straightforward tasks: How to change a password, update a lab quota, or deploy a custom workspace.
“We had a lot of repetitive how-to questions, where the answers could be found in our user guide. An AI agent made more sense because as the product changes, we update the user guide—our one source of truth—and the Info Agent always provides the latest instructions.”
Thien-Y Le, senior product manager, Microsoft Digital
Connectivity issues are more disruptive. When our employees can’t use RDP to connect to a virtual machine, diagnosing the problem means checking across networks, OS configuration, and Azure infrastructure to determine which layer has failed. This typically results in a work stoppage, unless they can quicky recreate the lab.
In some cases, rebuilding a lab may take only a few minutes. However, many of our customers rely on highly customized virtual machines that have been fine-tuned over years to fit their environments. These labs are essential for investigations, troubleshooting, and development work, and rebuilding them can take much longer. Being unable to remotely access a provisioned lab can also become a critical blocker for active customer case work. These kinds of labs require immediate attention.
Those experiences shaped our Managed Cloud Labs Agent Experience solution, which pairs an Info Agent for user education and a Troubleshooting Agent for connectivity issues. The Info Agent answers how-to questions conversationally, using retrieval-augmented generation (RAG) to draw from internal documentation as well as the user guide.
“We had a lot of repetitive how-to questions, where the answers could be found in our user guide,” says Thien-Y Le, a senior product manager in Microsoft Digital. “An AI agent made more sense because as the product changes, we update the user guide—our one source of truth—and the Info Agent always provides the latest instructions.”
Automating the troubleshooting process
The Troubleshooting Agent changes the support model. When one of our engineers selects a virtual and reports an RDP problem, the agent investigates the VM, diagnoses the cause, and attempts a repair. Throughout the session, it gives the user a running summary of what it’s detecting and the actions it’s taking, making the automated work visible to the user but also “hands-free.”
When RDP or run commands are broken, our support engineers would typically turn to Azure Serial Console as an expedient path to the VM when all else fails. The Troubleshooting Agent automatically takes that same serial console path, but adding an AI agent to the process required retooling to automate the human action.
Our team built an orchestrated crew of AI agents behind that user experience. We used Microsoft Foundry to host our models and Microsoft Agent Framework to manage the overall troubleshooting workflow and orchestrate the different agents. To help with evaluations, we utilized the Azure AI Evaluation software development kit.
A session begins with programmatic checks, then moves on to a read-only diagnostic orchestrator agent that decides what information to gather and tools to use. It can query Azure resource health, call Managed Cloud Labs APIs, and route work to a specialized Windows RDP diagnostic agent that drives the serial console.
“We deliberately break test VMs in dozens of ways and expect the Troubleshooting Agent to repair them, then run evals to make sure the fixes are safe and consistent.”
Joshua Deans, senior software engineer, Microsoft Digital
The process reads early signals. For example, if Azure resource health points to a clear problem, the orchestrator stops there instead of running more complex diagnostics. When the system identifies an issue it can safely address, an auto-repair orchestrator restarts the VM, resets a password and network interface, or sends serial console commands to correct a configuration problem. It then validates whether the fix has succeeded before closing the loop.
When the agent can’t safely resolve something, because the fix carries risk or the issue falls outside its known scenarios, it provides the full diagnostic context to the user. This context can then be given to a human support engineer, who can pick up the thread from there instead of starting over.
Making that reliable across the many Windows operating systems that Managed Cloud Labs supports took a lot of testing and careful tuning under close collaboration with the Azure Serial Console product group.
“We deliberately break test VMs in dozens of ways and expect the Troubleshooting Agent to repair them, then run evals to make sure the fixes are safe and consistent,” says Joshua Deans, a senior software engineer in Microsoft Digital.
Decreasing wait times
Our Troubleshooting Agent targets the 20% of ticket volume that comes from the common issues our support team typically resolves without escalation. The Info Agent addresses another 30% to 40%. Together, the team aims to take on about half of all ticket volume.
“With the Troubleshooting Agent, the same class of issue that once took more than 90 minutes to resolve is identified in less than 10 minutes. The agent provides immediate self-remediation to reduce downtime and has become an essential part of my workflow.”
Hrishi Deshpande, senior tech support engineer, Microsoft Exchange
In the first month of using the Troubleshooting Agent, we were able to:
- Reduce the average time to mitigate a common connectivity ticket from 16 hours to 15 minutes.
- Save our employees more than 1,200 hours a month in wait time.
“With the Troubleshooting Agent, the same class of issue that once took more than 90 minutes to resolve is identified in less than 10 minutes,” says Hrishi Deshpande, a senior tech support engineer in Microsoft Exchange. “The agent provides immediate self-remediation to reduce downtime and has become an essential part of my workflow.”
What’s next on our journey
We intend to add tooling for firewall issues to the Troubleshooting Agent and keep expanding the orchestrator to cover more signals over time.
“Every transformative platform starts with a single capability that proves what’s possible. For us, that’s the VM Troubleshooting Agent, the foundation beneath it, and the future it unlocks.”
Vikram Dadwal, principal software engineering manager, Microsoft Digital
Our team sees the next chapter as helping inform broader Azure capabilities, so the same self-healing approach can reach anyone running Azure VMs.
“Every transformative platform starts with a single capability that proves what’s possible,” says Vikram Dadwal, a principal software engineering manager on the Managed Cloud Labs team in Microsoft Digital. “For us, that’s the VM Troubleshooting Agent, the foundation beneath it, and the future it unlocks.”
For Le, that future is about what AI is making possible for users: intelligent support that feels immediate and always within reach. She likens it to giving every user their own personal support engineer. That shift from reactive troubleshooting to proactive, self-healing support is the larger opportunity that Managed Cloud Labs is addressing.
“We envision a future where troubleshooting becomes invisible,” Le says “It’s a world where systems automatically identify and remediate issues, eliminating the need for customers to open support tickets or seek assistance.”
While Managed Cloud Labs is an internal Microsoft platform, the principles behind it—self-service provisioning, governance guardrails, automation, AI-driven optimization, and cost management—can be implemented using Microsoft Azure services. Click through to learn more about how Azure helps organizations build and manage cloud environments at scale and how to get started building your own static web apps.
Key takeaways
As you consider where AI automation can help your own support model, keep these ideas in mind:
- Start with repetitive work. Look for high-volume, low-complexity issues where support teams already follow consistent diagnostic and repair patterns.
- Keep trusted knowledge current. AI agents are only as useful as the documentation and source-of-truth content they rely on.
- Make automation visible. Give users a clear view of what the system is checking and validating, so that self-service support builds confidence instead of creating uncertainty.
- Design for safe, smooth handoff. When an agent can’t resolve an issue safely, make sure it passes useful diagnostic context to the user so that support engineers can pick up from there, rather than starting over.
Try it out
- Learn how to build a static web application using the Azure portal.
- Find out more about Microsoft Foundry and Microsoft Agent Framework.
