Our Microsoft Digital engineers saw a better way forward: a single intelligent workspace, powered by an AI agent that makes identifying problems, troubleshooting them, and sharing context much quicker and simpler.

What began as a side project, inspired by a hackathon, quickly grew into a cross-team initiative.

“People were passionate about this project,” says Payal Awasthi, a senior software engineer in Microsoft Digital, the company’s IT organization. “Whenever people had some free capacity, they were coming in and contributing to it.”

The tool, called Smart DRI Agent, now supports over 40 teams at Microsoft, delivering more than 7,800 AI-assisted interactions and nearly 6,000 proactive incident notifications. This has saved the company an estimated 600 engineering hours per month, with bigger gains projected in the future.

“It saves a lot of time on context switching,” says Bhupesh Guptha Muthiyalu, a principal engineering manager in Microsoft HR. “The DRIs have ready-made information in hand, and based on that they’re typically able to resolve the incident quickly.”

Bringing all incident info into one place

In Microsoft Digital, our engineers serve as designated responsible individuals, or DRIs, who oversee production scenarios on a rotating basis. Their responsibilities include investigating incidents, monitoring service health, reviewing security work, and responding to other operational issues—work that can be both time sensitive and stressful.

“Whenever a new incident comes in, a DRI might have to elevate a lot of production permissions before they can get any insight into it. That’s one of the reasons we wanted to build something that can bring all the information into one place.”

Payal Awasthi, senior software engineer, Microsoft Digital

Meanwhile, the information that our DRIs need to take action on is typically distributed across several different portals and dashboards, along with troubleshooting guides, SharePoint sites, OneNote notebooks, and other engineering documentation.

Finding and assembling that information takes a while. For engineers who are new to a service or take over an unfamiliar DRI rotation, it can take even longer, as they may have to seek out the right subject matter expert to understand the situation.

“Whenever a new incident comes in, a DRI might have to elevate a lot of production permissions before they can get any insight into it,” Awasthi says. “That’s one of the reasons we wanted to build something that can bring all the information into one place; you don’t have to spend a lot of time switching between portals and elevating permissions.”

In some cases, important context can get lost in the shuffle, which can bog down the process and make it harder for DRIs to take the necessary next steps to resolve an issue.

“We depend on DRIs to be very proactive, and monitor these services 24/7 to make sure that everything is running smoothly,” says Sudhakar Sadasivuni, a principal group engineering manager in Microsoft Digital. “As part of our company’s native AI transformation, our idea was to make live site monitoring more proactive than reactive. We thought, why not use AI to make these decisions more intelligent, so that when DRIs come into the live site situation they’re already well informed.”

“We created goals and a 30-60-90-day plan and started making it more generic, where any service could simply onboard and start using that intelligence. It’s been one of the most successful open-source projects I’ve ever seen internally.”

Sudhakar Sadasivuni, a principal group engineering manager, Microsoft Digital

Teams in Microsoft Digital and the Microsoft HR organization were exploring related ideas for helping their DRIs, although the solutions they were considering were not identical. Once the teams recognized that they were addressing the same underlying operational problem, they decided to join forces.

Engineers from the two organizations created the first iteration of the agent during a Microsoft hackathon. As the prototype developed, they decided to treat it as a more structured effort.

“Later, we realized this was something that we should seriously invest in,” Sadasivuni says. “So we did a reset: We created goals and a 30-60-90-day plan and started making it more generic, where any service could simply onboard and start using that intelligence. It’s been one of the most successful open-source projects I’ve ever seen internally.”

Fine-tuning the product

One particularly challenging part of developing the agent was taking a scattered system and making it more uniform.

“Some services respond in different ways, and the reports that DRIs generate during handoffs can differ,” Sadasivuni says. “As we started interviewing various DRI teams, the requirements were piling up. We struggled with that for two or three months; eventually, we made some design changes to make it more generic.”

The initial implementation included more hard-coded behavior, but the team later separated configuration from the core logic and allowed teams to supply templates for workflows, such as DRI handoff reporting.

“We leveraged the framework functionality, but we also built a lot of custom code to support good results and good navigation through the system,” Awasthi says.

The team used Microsoft Semantic Kernel as an orchestrator. Different operational domains, such as incident management and Service 360, use separate sets of functions. The orchestrator determines which functions to call in response to a request.

“It’s easy, and there’s no limit on the number of services that I can query. That’s one of the biggest advantages that this agent brings.”

Lakshmi Kothamasu, principal engineering manager, Microsoft Digital

For similar-incident retrieval, the solution indexes incident information in Azure AI Search. The team found that search indexing alone didn’t always return useful matches, so developers added custom logic to assess similarity and improve the results supplied to DRIs. 

“It’s easy, and there’s no limit on the number of services that I can query,” says Lakshmi Kothamasu, a principal engineering manager in Microsoft Digital. “That’s one of the biggest advantages that this agent brings.”

When an incident comes in, the agent automatically provides an analysis that includes suggested troubleshooting steps and historical context from similar issues in the past, which speeds up the resolution process.

“It will connect to your telemetry and get all the relevant traces, logs, and exceptions that happened in the last few minutes.”

Bhupesh Guptha Muthiyalu, principal engineering manager, Microsoft HR

Based on the incident details, title, description, and other data, the agent will search your troubleshooting guide, identify all the matching patterns, explain how to troubleshoot this issue, and give you needed context.

“It will connect to your telemetry and get all the relevant traces, logs, and exceptions that happened in the last few minutes,” Guptha Muthiyalu says.

It can also function proactively, such as delivering a summary first thing in the morning that identifies what an engineer needs to complete that day. All of the information is presented directly in Teams, helping it to seamlessly fit into engineers’ workflows.

Looking ahead: Agentic incident management

In future iterations, the team hopes to train the agent to actually take action and mitigate some incidents for the DRIs, keeping them in the loop and enabling them to sign off on the steps the agent takes.

For teams considering a similar project, the starting point is the DRI workflow itself. Identify where engineers spend time, where information is fragmented, and which repeated steps are strong candidates for assistance. Then, connect the workflow from detection and context gathering through to mitigation and continuous improvement, while keeping real people responsible for operational decisions.

“Without a strong live site culture, where the reliability and health of production services are a priority, this agent is of no use at all,” Sadasivuni says. “They go hand in hand; otherwise, it’s an unnecessary use of computing and artificial intelligence. This agent was designed with a single goal: to empower the human DRI. So, the more the DRI is demanding, the more the system will respond.”

End-to-end coverage is also key. DRIs should be able to turn to the agent anytime, at any stage for guidance, helping engineers to move faster and with more confidence. And with even more proactive measures in place, DRI work will only become more seamless in the future.

The post Automating engineering operations at Microsoft with a Smart DRI Agent appeared first on Inside Track.

• Introduction: How empowering employees to create agents generates value for your organization
• Chapter 1: Choosing the right path for agent creation
• Chapter 2: Empowering every employee with Agent Builder
• Chapter 3: Leveling up citizen developers with Microsoft Copilot Studio
• Chapter 4: Building enterprise AI solutions with Microsoft Foundry and pro-code tools
• Conclusion: Creating pathways for every type of agent builder

Generating value by empowering your employees to build agents

AI agents are an increasingly important way for organizations to scale knowledge, help their employees boost productivity, and automate business processes.

In Microsoft Digital, the company’s IT organization, we think of agents as more than tools that the company rolls out and employees adopt. They represent a new way of working, where employees have the power to create and modify their own AI tools to help them solve problems, streamline their work, and create value in their unique roles.

As our use of AI has matured, we’ve seen employees across the company identify opportunities to create agents that help them retrieve information, answer common questions, automate repetitive tasks, and reimagine workflows. Rather than limiting agent creation to developers, we’ve made agent-building capabilities available for every employee at Microsoft. Our goal is to enable the people closest to the work to build their own agentic solutions.

Meeting the needs of different builders across the organization

As we expanded agent adoption across Microsoft, we quickly discovered that not all employees need to build the same kinds of agents. A knowledge worker who wants to create a simple retrieval agent for a project team has very different requirements from a business unit building workflow automations or an engineering organization tasked with creating enterprise-scale agentic solutions.

In the course of this journey, we’ve faced a challenge that’s becoming common for many organizations: Equipping different groups of employees to build agents without forcing everyone into the same development model.

“Frontier transformation depends on empowering every employee to turn ideas into AI-powered solutions. This democratization of AI creation can unlock new levels of productivity, innovation, and business value.”

Mohit Chand, general manager, Corporate Functions Engineering, Microsoft Digital

We’ve learned that effective agent enablement is about matching the right tools to the right builders and scenarios:

• Some employees benefit from natural-language tools that allow them to build agents with little or no technical experience.
• Others need low-code environments that support more sophisticated workflows and integrations.
• Still other workers require pro-code platforms that provide the flexibility, extensibility, and governance needed for enterprise-scale solutions.

Creating pathways for every type of agent builder

Our approach has been to enable a spectrum of agent-building pathways that meet employees where they are. We focus on providing clear guidance, skilling resources, governance frameworks, and tool recommendations that help people move from ideas to working solutions with the appropriate level of complexity and oversight. Crucially, this approach helps us ensure innovation can scale responsibly as agent usage continues to grow.

“Frontier transformation depends on empowering every employee to turn ideas into AI-powered solutions,” says Mohit Chand, general manager of Corporate Functions Engineering in Microsoft Digital. “This democratization of AI creation can unlock new levels of productivity, innovation, and business value.”

In this IT playbook, we’ll share how we’ve approached agent enablement across Microsoft, how we think about different agent-building audiences and tools, and the lessons we’ve learned along the way. We believe these insights can act as a blueprint for your own strategy as you empower employees to build agents with confidence, regardless of their level of technical knowledge.

Chapter 1: Choosing the right path for agent creation

Laying the foundation for different builders to meet their needs with agents

As agent creation became more widespread at Microsoft, one question stood out: How could we help employees use the tool that’s best for the problem they’re trying to solve or the opportunity for innovation they’re trying to explore?

Early on in our journey, we observed a common pattern: Employees would often gravitate toward the most powerful and flexible tools available because they seemed like the safest choice. If a platform could handle every scenario, why not start there?

In practice, that approach often introduced unnecessary complexity. Some builders found themselves navigating security, privacy, and compliance requirements that far exceeded the scope of their projects, let alone their level of technical expertise. Others invested time learning advanced capabilities they ultimately never needed to use.

“The most successful agents often start with a practical business problem. Once builders see the results, they quickly discover new opportunities to automate and improve adjacent processes.”

Naveen Jangir, principal architect, Microsoft Digital

We’ve learned that enabling successful agent building depends on providing the right development path. The goal is to help employees innovate quickly with AI agents while applying the right level of governance and technical complexity. When employees start with a clear objective and work backwards from there, they tend to build faster, encounter fewer obstacles, and achieve stronger results.

“The most successful agents often start with a practical business problem,” says Naveen Jangir, a principal architect in Microsoft Digital. “Once builders see the results, they quickly discover new opportunities to automate and improve adjacent processes.”

We also learned that choosing the wrong tool can have unintended consequences. Employees may run into compliance reviews they weren’t expecting. Projects might slow down as builders try to understand governance processes.

Choosing the tool that isn’t the right fit for a builder’s scenario might also direct them to the wrong model for their agent. In some cases, that could lead to using something more complex and expensive instead of a lower-cost model that’s ideal for a simpler scenario. More advanced tools often assume more advanced models that aren’t inherently necessary for every task.

In some cases, builders abandon promising ideas before they deliver value. The result is losing out on innovation, not because an idea wasn’t worthwhile, but because the path to implementation became more difficult than necessary.

To avoid these situations, we encourage builders to start by considering their problem scenario, rather than the tool itself. When evaluating an idea, we ask employees to think through a few key questions:

• What business outcome are they intending to achieve?
• What data does the agent need to access?
• Is that data personal, team-based, or enterprise-wide?
• Is the agent retrieving information, taking actions, or operating autonomously?
• How broadly do they intend to share the agent?

Understanding the needs of different builders

Roughly speaking, we describe agents as belonging to three categories, ranging from simpler and lower risk to more complex and more reliant on sensitive or external data.

As AI maturity at Microsoft has advanced, three primary sets of needs and cohorts of agent builders have emerged, based on user scenarios, needs, and technical capability.

1. Simple retrieval agents for any employee: Agent Builder in Microsoft 365 Copilot

The first cohort consists of employees solving everyday productivity challenges. These builders need agents that retrieve information, summarize content, answer questions, evaluate inputs against a declared rubric to provide feedback, and help them work more efficiently with existing knowledge sources.

For these scenarios, Agent Builder in Microsoft 365 Copilot provides an accessible starting point. Employees can use natural language to create lightweight retrieval agents grounded in their Microsoft 365 knowledge and data. It lets employees begin creating value without requiring extensive technical skills or development experience.

Many employees discover that this level of functionality meets their needs. By starting here, they can experiment quickly without introducing unnecessary complexity.

2. Citizen development without DevOps overhead: Microsoft Copilot Studio

Some employees want agents to do more than retrieve information. These builders need tools that can connect to business systems, automate workflows, orchestrate processes, and take actions on behalf of users.

Copilot Studio serves as an important middle ground for agent development, supporting retrieval, task, and autonomous agent scenarios while providing access to a broad ecosystem of connectors and integrations. It combines low-code development with broader connector capabilities, workflow automation, built-in telemetry, and evaluation tools.

These features allow employees to create more sophisticated business solutions without needing to be professional software developers.

3. Professionally developed agents that meet organizational needs: Microsoft Foundry and pro-code tools

The most complex and flexible tools are primarily the domain of professional developers and engineering teams building line-of-business or enterprise-scale solutions. These builders need architectural control, advanced integrations, specialized security requirements, and custom orchestration capabilities. For these scenarios, Microsoft Foundry and other pro-code pathways like Microsoft 365 Agents Toolkit provide the flexibility they need to design highly customized systems.

But they also introduce additional responsibility. Teams operating at this level assume ownership for architecture, compliance, privacy, security reviews, operational monitoring, and lifecycle management.

The distinction is important. Advanced development platforms create tremendous possibilities, but they’re most effective when a scenario truly requires that level of control.

“Get people excited about agents first, and then educate them. Encourage people to become builders and get their hands dirty through advocacy and education.”

Myron Wan, principal group product manager, Microsoft Digital

These aren’t rigid boundaries, but they’re helpful in understanding what each agent creation tool can accomplish. We’ve distilled our perspective into the following list of characteristics of different agent builders and of the creation platforms most appropriate for their needs:

Whatever your potential builder’s level of expertise or needs, we’ve discovered that encouraging experimentation while providing learning opportunities is key.

“Get people excited about agents first, and then educate them,” says Myron Wan, a principal group product manager in Microsoft Digital. “Encourage people to become builders and get their hands dirty through advocacy and education.”

What IT organizations should consider

In Microsoft Digital, our objective is to enable innovation while maintaining appropriate safeguards.

Good agentic architecture starts with data

Whenever we’re considering any agent development path, data is at the center of our thinking. Where data resides, how agents access it, who can use it, and the actions agents will execute with it form the foundation of every agent-building solution. In many cases, the data itself determines the appropriate platform.

We take a “self-service with guardrails” approach to our productivity estate, which means we give employees the ability to create new workspaces across their Microsoft 365 applications while securing assets by default and expanding access based on employee needs. The same is true for agent creation. We rely on well-established governance in the form of sensitivity labels, established software development lifecycle procedures, and risk-based app and agent management policies that trigger reviews when we detect risk.

We also frame governance in terms of who owns the risk: the platform itself, the administrator, or the organization.

• Agent Builder operates according to intrinsic governance, where the platform itself is responsible for managing risk. Users build agents within existing Microsoft 365 boundaries, which include built-in guardrails that simplify governance for lower-risk scenarios. Builders can create meaningful experiences while staying within well-understood security and compliance frameworks.
• Copilot Studio follows a configurable governance model where the administrator owns the risk. It introduces greater flexibility through workflows, actions, and connectors. That flexibility creates powerful opportunities, but it also requires thoughtful governance around how data moves between systems and which connectors are approved for use.
• Microsoft Foundry features explicit governance, where the organization owns the risk. That need arises from more complex capabilities like model selection, custom orchestration, architectural design capabilities, and more.

Regardless of the agent-creation platform your employees use, investing in data classification, labeling, and governance will put you in a better position to scale safely.

AI-ready data is especially crucial for more advanced agent creation tools like Foundry. It ensures secure access to the high-quality, accurate information employees need when they need it.

AI-ready data rests on five pillars:

• Accelerating time to value through managing powerful AI models
• Data democratization to promote informed decision-making, innovation, and continuous learning
• Connectivity among subject matter experts across domains and regulatory divisions to accelerate innovation
• Quality and governance to maintain data accuracy, reliability, and compliance
• Unification, delivering accessible and scalable infrastructure by breaking down silos

Balancing risk and innovation

A risk-based approach informs one of our primary guiding principles: reviews should scale with risk.

Thanks to the guardrails we’ve established, we don’t subject a personal productivity agent created in Agent Builder to the same review process as an autonomous agent operating across enterprise systems. The result is a matrixed model of agent governance that matches the need for oversight and review to the level of risk, access, or action-taking ability an agent presents.

This principle helps employees experiment early, validate ideas quickly, and gain confidence as builders. It also helps us avoid creating unnecessary barriers that can discourage adoption before builders can realize value.

We also learned that excessive control isn’t necessarily a safe default; it’s another category of risk. Over-restriction doesn’t eliminate risk, but redirects it to shadow AI, where we lose both productivity and compliance visibility.

Before setting your employees free to create, speak with stakeholders from IT, security, privacy, legal, compliance, data, and other teams responsible for policy and protection. Together, these groups can help you work toward a cohesive but flexible approach to agents and the risks they present.

Throughout this process, it’s important to stay curious about the processes builders will face before, during, and after creating their agents. Administrators should ask several key questions:

• What is the origin of this process, how relevant is it for agent creation, and which products are affected?
• What can we do to remove friction and allow safer innovation?
• Where can we get involved with processes when agents present more risk, while allowing agents that meet our standards to get built and shared broadly, with minimum friction?

Adoption through intention

We’ve invested a lot of time, strategy, and effort into adoption efforts across Microsoft. First, builders can use self-serve tools to meet their own needs. Builders Central is our internal SharePoint page dedicated to supporting agent builders at Microsoft. Crucially, it features a section that helps people understand which agent-creation tool might be best for them, along with instructions and factors to consider throughout the process.

AskMica (Ask Microsoft Intelligent Compliance Agent) is our IT agent built on top of this content. It walks makers through the decision-making process and answers questions about what tool to use, what environment to choose, how to get exceptions for security, privacy, sensitive data access, and more.

AI Skills Navigator, a program now available to customers, extends AI learning by allowing leaders to create structured paths that guide teams toward specific outcomes, such as becoming AI literate, managing agents in the enterprise, or building expertise in agent development. In Microsoft Digital, we’ve tailored AI Skills Navigator content to differentiate learning for our IT employees.

This is an essential piece of the equation, because IT teams are so integral to AI implementation efforts. As you promote AI more widely, it’s important to ensure your own IT team has the skills they need to support this technology.

Since we’ve put these efforts in place at Microsoft, the number of active makers has grown roughly 10 times, from around 2,000 to 20,000 a month. We now have about 150,000 personal development environments under governance, and we drove measured maker friction down from roughly 50% to 5%.

Letting outcomes drive platform selection

We encourage teams to focus on business outcomes first. Before selecting a platform, builders should identify the process they want to improve, define success criteria, and determine whether an agent is the best solution. We have an entire SharePoint site dedicated to helping employees navigate agent creation, including decisions around which agent tool to choose.

In some cases, a traditional workflow, existing application, or agent that someone else has already created may be the better choice. But if an employee or team sees a genuine need, starting small allows them to validate assumptions, measure impact, and expand capabilities over time.

As you consider ways to empower your own employees to create agents of their own, the lessons we’ve learned can form a foundation for creating policy, enabling workers, and protecting your organization.

Chapter 2: Empowering every employee with Agent Builder

Giving employees a starting point for agent creation

As agents began to show their potential for significant impact on day-to-day work, we wanted employees across the organization to participate in creating them—not just developers and technical specialists. We found that the people closest to a business problem were often the ones best positioned to identify opportunities for AI to help. That insight helped shape our approach to Agent Builder in Microsoft 365 Copilot.

“Everyone at Microsoft is a potential builder. The key is giving people opportunities to learn through hands-on experience, because reading about AI is very different from building with it. Agent Builder provides a safe environment where they can do just that.”

Tom Heath, change management lead, Microsoft Digital

Agent Builder provides an accessible entry point into agent creation. Employees can create simple retrieval agents using natural language and ground them in approved knowledge sources, including SharePoint sites, websites, and Microsoft Graph connectors. The experience allows users to create agents that retrieve information, summarize content, answer questions, and help employees navigate organizational knowledge without requiring software development expertise.

The audience for Agent Builder is intentionally broad. It serves knowledge workers, subject matter experts, project teams, and employees looking to solve specific productivity challenges without learning advanced development tools. It also provides an important first step for employees who are new to building AI solutions.

“Everyone at Microsoft is a potential builder,” says Tom Heath, a change management lead in Microsoft Digital. “The key is giving people opportunities to learn through hands-on experience, because reading about AI is very different from building with it. Agent Builder provides a safe environment where they can do just that.”

Understanding Agent Builder’s place in the agent journey

Agent creation should scale with the complexity of the problem an employee wants to solve. Agent Builder creates agents devoted to knowledge-centric experiences: finding information, accessing expertise, navigating documentation, and surfacing insights from existing content.

We encourage employees to start with a simple question: Does this agent primarily need to help people find, understand, use, interpret, or validate information? If the answer is yes, Agent Builder is usually the right starting point.

Common use cases include team knowledge hubs, readiness and adoption resources, project-specific information assistants, meeting preparation support, and product or policy guidance. These scenarios allow employees to create immediate value without taking on unnecessary complexity.

As they experimented with agent building tools, many employees assumed they needed a more advanced platform than they actually did. In practice, we’ve observed that most use cases are knowledge-centric, and employees can address them effectively with Agent Builder.

“Agents are only as good as the data behind them. Strong data governance and protection practices create the foundation for successful agent experiences.”

Ken Auguillard, principal program manager, Power Platform

At the same time, it’s important to understand where the tool’s boundaries lie. Organizations should consider moving to Microsoft Copilot Studio when requirements expand beyond knowledge retrieval and begin to include higher-order capabilities:

• Accessing data outside Microsoft 365
• Integrating with external business applications
• Automating business processes
• Triggering workflows and actions
• Including advanced telemetry and evaluation capabilities

This distinction helps employees select the right platform without overwhelming them with technical decisions: Agent Builder helps people interact with information, while Copilot Studio helps them take action on it.

The Customer Zero story: Implementing Agent Builder internally at Microsoft

Because Agent Builder is intentionally simple, most of our work enabling it within Microsoft has been organizational rather than technical.

Skilling and education

Employees understand Microsoft 365 Copilot, but they’re often less familiar with the role agents can play in their daily work. Successful adoption means helping people understand when prompting alone is sufficient and when an agent they build can deliver more value. Adoption initiatives like Agent Launchpad that feature hands-on ideation and gamified building activities have helped our employees make those leaps.

Agent sprawl

As adoption grows, organizations need visibility into what agents exist, who owns them, and whether they remain relevant over time. Ownership, automation, explicit lifecycle policy, and governance become increasingly important for monitoring the proliferation of agents. For example, we use a 60-day inactivity threshold that triggers alerts and automated cleanup. In this context, Microsoft Agent 365, the control plane for agents, is emerging as a powerful tool for observability, management, tracking, and security.

Data readiness

Agent Builder surfaces information users can already access, which means the quality of an agent experience depends heavily on the quality of the underlying data. We saw this principle in action repeatedly during our adoption efforts. Well organized, properly governed content consistently led to stronger agent experiences. Meanwhile, poorly maintained data limits an agent’s usefulness, regardless of how well it was designed. The systems we’ve put in place for governing agents provide the necessary guardrails for this level of agent.

“Agents are only as good as the data behind them,” says Ken Auguillard, a principal program manager in the Power Platform product group. “Strong data governance and protection practices create the foundation for successful agent experiences.”

We also found that many employees wanted agents to perform actions, automate workflows, or orchestrate processes across systems. Those are valuable scenarios, but they sit outside the scope of Agent Builder. Helping employees understand boundaries reduced frustration and created clearer pathways into Copilot Studio when additional functionality was needed.

Enabling Agent Builder at Microsoft

As adoption accelerated, our focus shifted from simply making Agent Builder available to helping employees use it successfully.

It may seem elementary, but people need to understand why they may want to create an agent instead of simply using Copilot. They do that by learning how agents apply AI capabilities more specifically than a broad-based assistant. Then, they need help knowing how to make their idea a reality.

We invested heavily in hands-on learning experiences. Employees learned fastest when they could experiment with real scenarios, test ideas, and refine agents in the context of their own work. Workshops, guided exercises, office hours, and peer-to-peer sharing all played important roles in helping employees build confidence. Real examples from early adopters were especially useful for encouraging people to take their first steps.

Our priority was making agent creation approachable, so we positioned Agent Builder as a capability available to everyone, not just technical users. We then encouraged employees to start with a business problem, build agents with a clear purpose, and focus on measurable outcomes rather than experimentation for its own sake.

We also emphasized thoughtful agent design. We found that employees who spent more time defining an agent’s purpose, instructions, and knowledge sources produced significantly better outcomes.

“Be direct, be descriptive,” Heath says. “Spending more time in the ‘Describe’ interface typically means spending less time troubleshooting later.”

Because Agent Builder benefited from the governance foundation already present in Microsoft 365, we had already embedded identity controls, permissions models, data loss prevention policies, sensitivity labels, and compliance protections within the environment. As a result, we didn’t need to invest too heavily in building employees’ understanding of governance structures. Instead, we could focus more on helping them understand how to use the platform effectively.

As a result, much of our enablement work centered on communication, celebrating wins, peer-led support, and helping employees understand where Agent Builder fit within the broader agent ecosystem.

For us, Agent Builder became an important foundation for employee agent creation. It provides a low-friction way for our employees to build, learn, and solve business problems quickly. While many builders remain within the platform long-term, others use it as a stepping stone to more advanced tools. In both cases, it helps create a culture where building with AI is accessible to everyone.

As you look for ways to empower a workforce of agent builders at your organization, consider ways that our efforts can provide a framework for secure and empowering experiences for employees who want to solve their day-to-day business challenges.

Chapter 3: Leveling up citizen developers with Microsoft Copilot Studio

A bridge between simple and advanced agent development

Plenty of agentic scenarios require more than knowledge retrieval but less than a fully custom agentic application. At Microsoft, we wanted employees and teams to have the opportunity to connect their agents with business systems, automate workflows, retrieve information from external sources, and take action on behalf of users without necessarily demanding a full-featured DevOps process complete with reviews. We also wanted to provide greater capabilities while still allowing for a lightweight coding approach that accommodates citizen developers.

Copilot Studio is a low-code platform for creating agents that can automate processes, connect to enterprise systems, and extend Microsoft 365 Copilot with organizational data and business workflows. It supports both custom agent creation and agentic automations while remaining accessible to users without extensive software development experience. Everyone at the company has the freedom to create their own agents in their own Copilot Studio environment.

Unlike Agent Builder, which focuses primarily on knowledge-based experiences, Copilot Studio gives builders the tools to sync up agents to external systems through approved connectors, workflows, skills, and actions. These capabilities allow agents to move beyond answering questions and begin supporting real business processes.

At Microsoft, we’ve identified several primary builder cohorts for Copilot Studio:

• Citizen developers and makers who want to build beyond knowledge retrieval
• Business teams that seek to automate departmental processes
• Power Platform users who need to extend existing workflows
• Technical teams that want low-code flexibility instead of moving to pro-code development

Understanding Copilot Studio’s place in the agent creation journey

One of the most important lessons we learned is that not every scenario requires Copilot Studio. In the simplest terms, agents created using this platform are more suited for organizational or team-level tools, or for individuals who need more complexity than Agent Builder can accommodate.

Copilot Studio becomes the natural next step when teams need more capabilities or connections:

• Access to data that resides outside Microsoft 365
• Connectors that provide access to external business systems
• Workflow automation
• Triggered or autonomous behaviors
• Agents that perform actions rather than simply retrieving information
• Broader publishing and sharing options

Within Microsoft, we often describe the progression between platforms as moving from knowledge to action. A retrieval agent might answer questions about policies or project documentation. A task agent built in Copilot Studio can retrieve information from multiple systems, generate reports, update records, trigger workflows, and communicate with users based on business events.

“Once builders need data beyond Microsoft 365, connectors become a critical part of the solution,” says Aisha Hasan, a principal product manager in Microsoft Digital. “Copilot Studio is often the next step, because it lets teams combine enterprise data, workflows, and actions in a way that scales with business complexity.”

Most employees only have access to a subset of approved connectors that Microsoft Digital and our Office of the Chief Information Security Officer (CISO) govern. That helps us enable the Copilot Studio experience for all employees while limiting the most advanced capabilities to qualified engineers.

Copilot Studio may not be ideal for all scenarios, which is why we have pro-code alternatives. But many business processes never require that level of complexity. We’ve consistently found that Copilot Studio can support a broad range of organizational and team-level solutions before additional development investment becomes necessary.

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What our experts say:

“We focus on helping builders understand the risk profile of their agents as early as possible. By providing guidance during development, we can reduce friction, increase accountability, and move solutions through the process more efficiently.”

Aisha Hasan, principal product manager, Microsoft Digital

“Copilot Studio provides far more visibility into how agents perform over time. The evaluation and telemetry capabilities help builders improve quality, identify gaps, and build confidence that agents are delivering meaningful business value.”

Jean Hsu, product designer, Copilot Studio

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The Customer Zero story: Implementing Copilot Studio internally at Microsoft

Our experience implementing Copilot Studio has helped us identify some of the key areas to carefully consider while enabling Copilot Studio at your organization.

Managing governance without slowing innovation

As capabilities increase, governance naturally becomes more important.

The reality is straightforward. When agents connect to additional data sources, integrate with external systems, and perform actions on behalf of users, organizations need appropriate safeguards.

At the outset of our journey, we already had robust systems in place for securing custom connectors, and Microsoft 365’s built-in governance capabilities ensure Microsoft 365 agents respect our labeling taxonomy and the policies it articulates. We have the power to introduce sharing limits that restrict how widely builders can distribute their agents depending on their purpose and scope.

“We focus on helping builders understand the risk profile of their agents as early as possible,” Hasan says. “By providing guidance during development, we can reduce friction, increase accountability, and move solutions through the process more efficiently.”

Three agents help us do this work:

The Risk-O-Meter

Auto-classifies submissions as green, yellow, or red based on their risk profile, then auto-approves, auto-declines, or routes them to the relevant reviewer

The Action-O-Meter

Applies the same logic to new connectors and MCP servers

The Friction Meter

Surfaces maker pain as a first-class metric

For simpler self-service agents that individual employees create and use, we define policies at the Copilot Studio environment level. Tenant administrators and partners on the security team apply data loss prevention policies to configure what individual employees can and can’t do. At this level, everyone in the company has the same configuration and tools available, and automation largely handles agent reviews and assessments based on pre-configured settings.

For more wide-reaching apps that operate at the line-of-business level or that we might publish enterprise-wide, we need to apply greater rigor. Discipline-specific professionals in security, privacy, and other spaces conduct robust reviews to ensure internal teams meet our high standards.

Governance expands through bounded, risk-based arguments, not a general philosophy of openness. Internally at Microsoft, it was more than just flipping a switch. We negotiated incrementally with the Office of the CISO, then scoped sharing. That supported the CISO’s need to only approve precise, bounded requests with measurable controls.

Telemetry and evaluation matter

As agents become more sophisticated, understanding performance is increasingly important.

Observability is one area where Copilot Studio provides significant value. Agent administrators gain access to telemetry, analytics, evaluation capabilities, and performance insights that help them understand how their organization is using agents and where improvements are necessary.

We’ve found that these features become particularly important as agents move beyond personal productivity and begin supporting larger groups of users. Data-driven evaluation helps teams improve outputs, validate business outcomes, and build trust in agent experiences.

“Copilot Studio provides far more visibility into how agents perform over time,” says Jean Hsu, a product designer for Copilot Studio. “The evaluation and telemetry capabilities help builders improve quality, identify gaps, and build confidence that agents are delivering meaningful business value.”

Consider both areas carefully as you put Copilot Studio in the hands of your employees and teams. Because it straddles both simple and more complex agents, you’ll need to consider the implications of flexibility and how to maintain proper oversight and governance.

Empowering employees to use Copilot Studio at Microsoft

As adoption grew, we worked to balance empowerment and responsibility. Our Builders Central SharePoint page provides guidance on prerequisites, agent creation environments, types of agents people can build, and more.

It’s worth noting that builders who are already familiar with Power Platform concepts like environments, connectors, and data loss prevention policies will have an advantage when they use Copilot Studio, but they typically won’t need deep technical expertise.

More broadly, we’ve implemented support systems to help our employees and teams create agents safely and effectively, without a massive knowledge burden. They include:

• Making a wide variety of approved connectors available by default so builders can move quickly.
• Clearly defining when builders should transition from self-service to IT-guided development.
• Providing automated guidance during development rather than waiting until publication.
• Using AI-assisted compliance resources to keep builders working in context.
• Investing in maker education around connectors, data governance, and Power Platform fundamentals.
• Encouraging teams to validate business outcomes before scaling solutions broadly.
• Creating dedicated pathways for both self-service and enterprise scenarios.

Our experience showed that user education is just as important as technical controls. Builders need help understanding what they’re creating, what data their agents access, and what responsibilities accompany increasing capability.

We often use this statement to guide our work internally: Build compliantly, use intelligently.

As you explore ways to help employees create more advanced and capable AI agents, our experience can provide an example of how low-code platforms can enable sophisticated business solutions without requiring every scenario to become a large-scale software development effort.

Chapter 4: Building enterprise AI solutions with Microsoft Foundry and pro-code tools

Applying software development practices to agent creation

Some business challenges require capabilities beyond the reach of low-code development. Teams need to build solutions that can operate across complex business processes. At Microsoft, these scenarios made it necessary to cultivate a professional developer practice for building agents centered around pro-code tools, especially Microsoft Foundry. It provides a flexible environment for building sophisticated AI solutions using a broad catalog of foundation models, evaluation frameworks, orchestration tools, enterprise AI services, and unique dashboard experiences.

Foundry empowers teams to compare and evaluate models, fine-tune them for domain-specific scenarios, integrate large volumes of enterprise data, orchestrate multiple agents, build custom agentic applications, and monitor performance over time. It also allows builders to combine AI capabilities with enterprise architecture patterns, security controls, observability tools, and operational workflows.

“Model selection is one of the most important decisions in enterprise AI. Different model families excel at different tasks, and understanding those tradeoffs helps teams build solutions that align with business goals while minimizing spend and maximizing ROI.”

Michael Leung, senior software engineer, Microsoft Digital

Microsoft 365 Agents Toolkit provides a simplified creation path for pro-code builders. Using Visual Studio Code, developers can create agents grounded in multiple data sources, integrated with APIs, enhanced through adaptive cards, and customized through instructions, plug-ins, and manifests. The toolkit enables developers to build and deploy role-specific and task-oriented agents without creating extensive back-end services from scratch.

The primary audience for Foundry and Agents Toolkit includes software engineers, enterprise architects, IT solution owners, and advanced AI builders responsible for delivering production-grade capabilities at organizational scale.

“Model selection is one of the most important decisions in enterprise AI,” says Michael Leung, a senior software engineer in Microsoft Digital. “Different model families excel at different tasks, and understanding those tradeoffs helps teams build solutions that align with business goals while minimizing spend and maximizing ROI.”

Understanding when Microsoft Foundry and pro-code tools are right for the job

One of the most important lessons we learned is that the right platform depends on the problem we want to solve. Not every solution requires pro-code development.

In many cases, Copilot Studio provides more than enough functionality, even for agents meant to serve enterprise needs. Teams can create agents that connect to business systems, automate workflows, retrieve external information, and support business processes without requiring custom engineering. Copilot Studio supports knowledge retrieval, actions, autonomous behaviors, and extensive connector ecosystems.

Employees should consider moving to Foundry or other pro-code tools when they require greater technical depth to accomplish the following goals:

• Fine-tune foundation models using domain-specific data
• Compare and evaluate multiple model families
• Build custom multi-agent orchestration frameworks
• Implement advanced observability and evaluation pipelines
• Develop autonomous systems that operate across multiple enterprise services
• Integrate custom APIs, services, or proprietary architectures
• Build solutions that require extensive engineering control and customization

The distinction between different agent-builder tools often comes down to three dimensions:

• Low-code versus pro-code requirements
• Solution complexity and organizational maturity
• Short-term delivery goals versus long-term platform strategy

Don’t select Foundry simply because it is the most powerful option. Instead, start with the simplest platform that supports your business needs. That accelerates deployment and adoption while minimizing cost and operational overhead.

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What our experts say:

“Foundry helps us bring together data, expertise, and capabilities that often exist across separate teams. Many enterprise AI challenges are collaboration challenges, and the platform gives us a way to break down those silos.”

Naval Tripathi, principal software engineering manager, Microsoft Digital

“Enterprise AI depends on data access and integration. The technology is powerful, but success ultimately comes from ensuring models have access to trustworthy, well-governed information.”

Shweta Mallurwar, senior software engineer, Microsoft Digital

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Enabling enterprise AI development at Microsoft

At Microsoft, our approach focuses on helping teams build responsibly while maintaining flexibility for innovation.

As organizations expand beyond information retrieval, complexity increases. AI solutions begin interacting with multiple data sources, business systems, APIs, and operational workflows. Governance, architecture, and engineering practices become increasingly important.

“Foundry helps us bring together data, expertise, and capabilities that often exist across separate teams,” says Naval Tripathi, a principal software engineering manager in Microsoft Digital. “Many enterprise AI challenges are collaboration challenges, and the platform gives us a way to break down those silos.”

Several principles guide how we enable professional developers working with agents:

• Start with clearly defined business outcomes before selecting architecture.
• Align development approaches with the complexity of the scenario.
• Involve domain experts early when designing AI solutions.
• Establish evaluation frameworks before deploying agents at scale.
• Build governance and compliance requirements directly into development workflows.
• Use human-in-the-loop models when introducing autonomous decision-making.

This approach helps teams focus on solving problems, rather than pursuing technology for its own sake.

The Customer Zero story: Implementing Microsoft Foundry internally

While Foundry enables powerful capabilities, it also introduces new areas for oversight. In Microsoft Digital, we play an integral role helping teams to create agents responsibly and effectively.

Security and compliance

Data integration and retrieval quality

Evaluation and continuous improvement

Microsoft Foundry and other pro-code agent creation tools help developers bring high-value opportunities to life through agentic AI. By following our approach to enablement, management, and governance, you have a solid starting point for unlocking these capabilities in your own organization.

Conclusion: Creating pathways for every type of agent builder

As we’ve advanced our Frontier Transformation journey, we’ve learned that successful agent adoption is not about choosing a single tool or creating a single development model. The main goal is to give employees the right pathways to solve problems at the level of complexity their scenarios require.

“The future of agentic AI depends on giving people the freedom to create while providing the structure they need to do so responsibly. When organizations establish clear pathways, strong governance, and the right enablement support, they can empower every employee to become a builder and accelerate innovation at scale.”

Stephan Kerametlian, senior director, Microsoft Digital

At Microsoft, we’ve worked to create an ecosystem where every employee can participate in agent creation.

• Some begin with Agent Builder, creating retrieval agents grounded in trusted organizational knowledge.
• Others expand into Copilot Studio to automate workflows, connect systems, and support business processes.
• Professional developers can build sophisticated enterprise solutions with Microsoft Foundry and other pro-code tools.

Each pathway serves a different audience, but they all share the common goal of empowering employees to turn ideas into innovation.

Throughout this guide, we’ve emphasized a principle that has shaped our own approach: Start with the business problem, not the technology, then choose the simplest tool to achieve your goal. When employees focus first on the outcome they’re trying to achieve, they’re in a better position to select the right platform, apply the appropriate level of governance, and create solutions that deliver measurable impact. We’ve found that this approach not only reduces cost but also accelerates adoption and builds confidence as employees move from simple use cases to more advanced scenarios.

Just as importantly, we’ve learned that agent enablement requires more than providing access to tools. Success depends on creating an environment where employees understand when to use each platform, how to build responsibly, and where to find support when they need it. Hands-on learning, strong data governance, clear guidance, and risk-based oversight have all played important roles in helping us scale agent creation while maintaining trust.

As agents become more capable, governance continues to evolve alongside them. Our goal is never to slow innovation. Instead, we’ve focused on creating guardrails that help employees build safely and confidently. Whether someone is creating a personal productivity agent or developing an enterprise-scale solution, the same foundational roadmap applies: Experiment, govern, scale, and optimize. All along, keep business outcomes at the center of the effort.

The AI-native organization of the future isn’t one where IT says yes to everything or no to everything. It’s one where governance is intelligent enough to know the difference and fast enough to act at machine speed.

“The future of agentic AI depends on giving people the freedom to create while providing the structure they need to do so responsibly,” says Stephan Kerametlian, a senior director for Microsoft Digital. “When organizations establish clear pathways, strong governance, and the right enablement support, they can empower every employee to become a builder and accelerate innovation at scale.”

Our experience has shown that there is no single destination in the agent journey. Employees will continue discovering new opportunities to automate work, scale expertise, and solve business challenges.

New capabilities, tools, and methods of building will emerge over time. What matters most is creating a framework that allows employees to begin where they are today and grow as their needs evolve.

As your organization develops its own approach to agent creation, we hope the lessons we’ve learned and shared can help you build a strategy that balances empowerment with oversight, innovation with governance, and experimentation with long-term value. By creating pathways for every type of builder, you can unlock the collective creativity of your workforce and help transform great ideas into lasting business impact.

The post Our Customer Zero guide: Enabling agent creation across Microsoft 365 Copilot, Copilot Studio, and Foundry appeared first on Inside Track.

On the Infrastructure Engineering Services (IES) team within Microsoft Digital, our product managers (PMs) support the tools and services that keep Microsoft’s global network secure, resilient, and available. As Microsoft’s Customer Zero, our larger Microsoft Digital team—the company’s IT organization—is exploring how AI can accelerate individual tasks and help reshape the operating model that our PMs use to plan, govern, and communicate their work.

“One of the key things we do is make sure the most important work is getting done by the engineering teams, because our engineers are a pretty precious resource,” says Martin O’Flaherty, a principal PM manager in IES. “We need to be able to prioritize the work as it flows in. We have to understand what the engineering teams need, make trade-off decisions constantly, and make sure we’re delivering requirements and specifications that are essential for building the right thing.” 

That has led O’Flaherty and his team to experiment with AI across a broader Continuous Planning model. Rather than treat planning as a periodic exercise, this approach creates an ongoing view of the work in progress as well as upcoming priorities, deliverables, engineering investment, and strategic alignment. Planning dashboards, engineering-capacity visibility, ADO data quality, recurring leadership reviews, monthly delivery reporting, and AI-generated summaries work together as parts of the same operating model.

“We need to make sure that we’re getting the right level of leadership visibility and support. Senior leaders understand that engineering resources are finite, so they want to make sure we’re investing the right amount of resourcing in the appropriate areas.”

Martin O’Flaherty, principal PM manager, Microsoft Digital

The objective isn’t simply to automate existing PM tasks, but to also create a better feedback loop between planning, execution, data quality, reporting, and decision making, giving our PMs more time to work directly with customers and engineering partners.

Turning planning data into leadership insight

As a product management leader, O’Flaherty often has to gather reporting on his team’s scenarios, including which are active versus closed, how the work aligns to organizational priories, and how engineering capacity is being invested. This not only helps him and his team make better prioritization and trade-off decisions but also provides crucial information for advocating with senior leadership.

“We need to make sure that we’re getting the right level of leadership visibility and support,” O’Flaherty says. “Senior leaders understand that engineering resources are finite, so they want to make sure we’re investing the right amount of resourcing in the appropriate areas.”

Most of that data already exists within systems such as Azure DevOps. The challenge is gathering the information and turning it into easily digestible insights. For O’Flaherty, that became an opportunity to use AI not simply to automate reports but also to connect planning data, data quality, and leadership reporting together into a more continuous system.

To accomplish this, O’Flaherty and his team built an AI-assisted reporting capability that can comb through all scenarios completed during the previous month, organize them around established strategic priorities, and summarize the details into leadership-ready reporting and visualizations. These show where the engineering investment is going and outcomes that the organization is delivering.

Below is an anonymized example of a dashboard O’Flaherty might use in an operational planning discussion or business review to provide near real-time visibility into planning health, delivery progress, resource allocation, and overall execution:

Early on, the AI-generated reporting wasn’t always accurate or complete.

Rather than treating those missing details as an AI problem, O’Flaherty and team used them to improve the system around it. He and the team refined the reporting logic, strengthened the underlying planning data, and improved how this capability weighs the documented evidence of what was actually delivered. Human review remains an important part of the process, but the amount of manual effort required has fallen significantly.

“Last month, we had almost no feedback. We looked at the report and were like, ‘Yep, it’s good to go,'” O’Flaherty says. “We’re talking about a task that used to take weeks to do. It would be the end of August by the time you’d have July’s report. We can now send that report on the first day of August.”

During this process, O’Flaherty also learned the importance of clean, comprehensive data in getting the right outputs. Initially, missing or inconsistent information in Azure DevOps could lead to incomplete insights. And because that same planning data informs leadership reporting, improving the quality at the source became more important than improving the AI consuming it.

So, he built a second AI-assigned “hygiene” capability that continuously checks in-scope scenarios against a defined set of rules. It can identify data gaps and prompt team members with specific guidance on how to fill those gaps within Azure DevOps.

“The agent has driven the right behavior, and that’s honestly the key reason why our end-of-month reports don’t really require much human review anymore.”

Martin O’Flaherty, principal PM manager, Microsoft Digital

Together, the capabilities create a feedback loop: Better planning data produces better AI-generated insights, while AI continuously helps identify the data-quality issues that could undermine those insights.

“At the end of the day, the AI can only be as accurate as the data it has in front of it, and we were potentially making bad decisions based on that data,” O’Flaherty says. “The agent has driven the right behavior, and that’s honestly the key reason why our end-of-month reports don’t really require much human review anymore.”

Upskilling a diverse workforce

A common misconception with agents is that you need to be an AI expert to build one. O’Flaherty wanted to remove that barrier by making the process accessible to all product managers with a wide range of technical expertise. The goal was to teach people about AI and also encourage them to build capabilities against real problems in their own workflows.

As he and other team members developed their skills, they created short how-to videos of their process that broke the learning curve down into manageable steps. He began this effort by assuming the viewers knew nothing about the tools.

“We made a video on how you install Visual Studio Code, how you log into GitHub Copilot,” O’Flaherty says. “And then when you’ve done that, here’s how you access an MCP server.”

Those bite-sized explainers gave PMs a practical path into the tools and helped them to get comfortable applying what they learned directly to their own work. Rather than requiring everyone to become an AI expert first, the approach allowed people to start small, solve something useful, and build their skills through experience.

But as adoption grew, another challenge emerged: Product managers sometimes created multiple agents to solve similar problems. That presented a different scaling question: How do you encourage experimentation without recreating the same solution over and over again?

In response, the team began promoting reusable skills and encouraging PMs to build on existing work rather than starting from scratch.

“Our challenge was, how do we empower people to build without removing all the efficiencies?” O’Flaherty says. “We’ve partially solved that by creating a repository that gives people an easy way to upload stuff and encourages them to reuse where they can. Or to take the skill that’s there and customize it for their organization.”

That shift, from individual experimentation to reusable building blocks, helped turn AI adoption into more of a shared engineering discipline. PMs could still tailor capabilities to their own needs, but successful patterns could be reused, improved, and extended instead of repeatedly rebuilt.

Speeding up product planning and prototyping

For individual PM contributors, agents have made context gathering faster and simpler.

For example, Astha Sinha, a senior product manager on the IES team, says sorting through documents, meeting notes, and the various stacks her team leverages and turning that into reports could take hours every day. That was before she started using Copilot to summarize all the information for her.

Now she uses agentic skills to automate the reports that she sends to different PMs each month, further simplifying her everyday routine.

“One of the biggest benefits has been automation, which I’m empowered to achieve very easily,” Sinha says. “I support multiple partners, all with different reporting rhythms, so I created a set of skills that automatically generate reports for them on the right schedule.”

What used to be a recurring manual task for her now happens in the background. It’s reduced administrative overhead and freed up more time for strategic product work, making her workload lighter.

“Now, by making my agent aware of the code base and incident reporting, and stitching together the logs and the data models, I can get that insight faster without using engineering time. Then I can take it back to my customers and actually get down to what really matters to them.”

Owais Khan, senior product manager, Microsoft Digital

Senior product manager Owais Khan, who also works on the IES team in Microsoft Digital, has also set up agents to analyze usage data.

Previously, it might have taken him a few hours to sit down and look at where customers are clicking, what kind of APIs they’re using, and what their call patterns look like, then actually investigate improvements on that site.

“Now, by making my agent aware of the code base and incident reporting, and stitching together the logs and the data models, I can get that insight faster without using engineering time,” Khan says. “Then I can take it back to my customers and actually get down to what really matters to them.”   

AI builder tools like Copilot Studio also provide a low-code solution for product managers that allows them to produce prototypes communicating their vision to engineers.

Nevedita Mallick, a principal product manager on the IES team, owns a lot of the UI experience on our corporate network. Her typical process for prototyping used to be to create a one-pager, then build out the idea manually in Figma.

“With Copilot Studio, it’s much faster,” Mallick says. “Things that used to take a day or two I can now be done in less than an hour, even in minutes. I can give a very rough sketch to Copilot Studio and it’s able to render the mockup really quickly. It also gives me the complete HTML with all the CSS and JavaScript, so I can just click through and explain to my developers, ‘This is what I want,’ or, ‘This is the enhancement I’m looking for.'”

Using AI-generated prototypes, PMs can also show customers interactive experiences earlier in the design cycle. This helps everyone align on requirements faster and reduces the amount of work required later in the development cycle.

The value of AI is amplified through thoughtful prompting and clear context.

“Prompting is everything,” Mallick says. “When troubleshooting a network issue, I asked Copilot to think like a network engineer so it could evaluate the problem through the lens of network topology, dependencies, and operational context. It moved from delivering generic answers to providing insights tailored to the problem at hand.”

The result, she says, is that the quality of your outcome is directly tied to the context and expertise you bring into the prompt.

“What once required specialized expertise and significant development time can now be done in minutes. For example, I recently created a custom skill for a specific business need in under five minutes, allowing me to move from idea to solution almost instantly.”

Astha Sinha, senior product manager,
Microsoft Digital

Practice truly does make perfect. Creating their first agents took some time, but the PMs say that the ramp-up period has significantly shortened the more they do it.

“What once required specialized expertise and significant development time can now be done in minutes,” Sinha says. “For example, I recently created a custom skill for a specific business need in under five minutes, allowing me to move from idea to solution almost instantly.”

The future of PM work

The PMs all agree that human oversight of agentic AI will always be necessary, even as they allow agents to take over some tasks.  

O’Flaherty notes that even as his agent can estimate time spent on scenarios, there are still a lot of nuances in how productivity is evaluated.

For example, while the agent can predict how many hours a specific task might take, someone also needs to factor in who’s completing the task and how fast they can work. There’s also the need for a fact-checking pass. Because of these variables, O’Flaherty believes we still need humans to gather the full picture and oversee the process.

“We’ve become agent bosses—the agents do the work and we review and approve it,” he says. “We’re almost like a governance layer for the agents in how we do our work.”

“The real opportunity with agents isn’t just efficiency, it’s prevention. By embedding intelligence throughout the operational lifecycle, we can identify and address issues before they become incidents, fundamentally changing how we operate at scale.”

Nevedita Mallick, principal product manager, Microsoft Digital

PMs are also already seeing how their work has changed, and that it will continue to evolve in the age of AI. Mallick observes how AI is enabling product managers to have more room for deep, critical thinking and broader product vision work. She’s excited to see how agents work proactively down the line.

“The real opportunity with agents isn’t just efficiency, it’s prevention,” Mallick says. “By embedding intelligence throughout the operational lifecycle, we can identify and address issues before they become incidents, fundamentally changing how we operate at scale.”

The team’s ultimate goal isn’t to automate product management, but to make it easier for PMs to apply their expertise where it matters most.

As Microsoft Digital continues its important Customer Zero work, that mission is helping shape how product managers use AI to scale their impact while maintaining human judgment, collaboration, and accountability.

The post Boosting product manager productivity at Microsoft with AI appeared first on Inside Track.

Across Microsoft, our researchers, engineers, and product teams are building new models, agent frameworks, benchmarks, applications, and research projects at a steady pace. But even when innovative work exists, it has limited impact if people can’t find it, understand it, or put it to use.

That challenge inspired the transformation of our Microsoft Foundry Labs website.

Originally launched as a simple online catalog of AI innovations across Microsoft, Foundry Labs gave teams a place to showcase their research projects, models, and experiments.

But as the volume of innovations increased, the experience became difficult to navigate.

In response, principal product manager Saumil Shrivastava brought together a small cross-functional team of three—a product manager, a marketer, and an engineer—to rebuild the site. Along the way, they discovered something: Modern AI tools could fundamentally change how digital products are designed, prototyped, and delivered.

The team used AI throughout the development process to move from concept to production in approximately three months. The result was a new Foundry Labs experience that helps customers discover innovations across Microsoft while serving as an example of AI-assisted product development in practice.

“The whole purpose of Foundry Labs is to bridge the gap between Microsoft’s latest AI innovations and the people building with them, turning breakthroughs into tools you can explore, test, and build with today.”

Gulsimo Osimi, technical program manager, Microsoft Foundry

Solving the discoverability problem

The mission of Foundry Labs has remained consistent since its creation: Provide a destination where our teams can showcase AI innovations and help people learn about emerging technologies. These innovations come from organizations including Microsoft Research, Microsoft AI, Microsoft Planetary Computer, and other Microsoft teams and collaborators.

But the original site had been designed when the catalog was much smaller.

“When we had 20 innovations, it was pretty reasonable to scroll and click on each innovation,” says Gulsimo Osimi, a technical program manager at Microsoft and member of the three-person site build team, alongside Pratyansh Agrawal, a software engineer, and Patrick Widjaja, a senior product marketing manager. “As the number of projects grew, we wanted the user experience to provide the option to filter by innovation, domains, status, capability, or simply browse with images that help visualize what the innovation is about.”

The challenge was compounded by the pace of AI innovation itself.

“So many teams have lots of AI innovation happening, and staying on top of it is really hard,” Osimi says. “The whole purpose of Foundry Labs is to bridge the gap between Microsoft’s latest AI innovations and the people building with them, turning breakthroughs and experiments into tools you can explore, test, and build with today.”

From traditional specs to a working prototype

The project began with a traditional product management process. Shrivastava set the broader Foundry Labs product vision and strategy, and Osimi translated that direction into a detailed, 32-page specification for the new site experience. But as the document grew, the team realized it wasn’t the ideal way to communicate the vision. Osimi began experimenting with AI-assisted prototyping.

Using AI development tools, Osimi rapidly transformed product concepts into interactive experiences. The process became highly iterative: Specification informed prototype development, and the prototype informed revisions to the specs.

The resulting prototype helped stakeholders understand the vision much more quickly than a document alone.

As the prototype gained visibility, the team presented it at a CoreAI demo fair, where it attracted attention from stakeholders and leadership. That validation helped secure support from engineering and marketing partners, accelerating the site rebuild.

“We’re able to take the project from a sketch to a prototype and then to a full website in only a month.”

Patrick Widjaja, senior product marketing manager, Microsoft Foundry

Three people, multiple disciplines, supported by AI

A key aspect of the project was its deliberately small team. The initiative brought together expertise from product, marketing, and engineering, with AI tools helping each discipline contribute in new ways.

Shrivastava created the broader strategy and brought together the cross-functional team. Osimi led detailed product requirements, user experience design, and AI-assisted prototyping; Widjaja focused on brand identity, community experiences, content strategy, and site storytelling; and Agrawal transformed the prototype into a production-ready experience.

“We used AI extensively in different ways,” Widjaja says. “Gulsimo, from the product side, used it to prototype the website, and I used it to make all of the branding, design, copy, and images. We were able to take the project from a sketch to a prototype and then to a full website in only a month.”

The team’s approach highlighted how AI could support different stages of the development lifecycle.

Building an AI-assisted development workflow

The development process followed a repeatable pattern: receive intent, inspect the codebase, plan changes, create isolated branches, implement functionality, validate outcomes, and merge approved work.

“Microsoft is producing extraordinary AI innovation across research and product teams, but breakthroughs create value only when customers can discover, understand, and put them to use. Our strategy for Foundry Labs is to create a shared path from emerging innovation to real-world impact—giving teams a way to bring promising work forward, learn from customer engagement, and use those insights to accelerate the journey from research into practical use.”

Saumil Shrivastava, principal product manager, Microsoft Foundry

AI-assisted workflows were used to help deliver features across the site, including:

• Interactive innovation catalogs
• Storytelling and community experiences
• Taxonomy-based discovery and filtering
• Searchable innovation pages
• Responsive user experiences
• Performance and accessibility improvements

The engineering team also developed a structured WordPress content model, creating custom content types, taxonomies, field groups, and content management workflows designed to keep the growing catalog maintainable over time.

This focus on content architecture proved especially important because Foundry Labs was never intended to be a static destination. The catalog continues to grow as more innovations are added across Microsoft.

“Microsoft is producing extraordinary AI innovation across research and product teams, but breakthroughs create value only when customers can discover, understand, and put them to use,” Shrivastava says. “Our strategy for Foundry Labs is to create a shared path from emerging innovation to real-world impact—giving teams a way to bring promising work forward, learn from customer engagement, and use those insights to accelerate the journey from research into practical use.”

Designing for discovery

The new experience places discoverability at the center of the user journey.

Visitors can browse innovations by domain, capability, and innovation type. Innovation pages provide concise descriptions, technical details, links to research papers, GitHub repositories, deployment experiences, and related resources.

The site supports a broad range of innovations, including models, SDKs, agent systems, benchmarks, applications, and research projects. Those innovations span disciplines ranging from biomedical research and chemistry to earth science, geospatial intelligence, and general-purpose AI models.

For users, the goal is simple: Arrive with an area of interest and leave with a clear next step.

A central lesson from the site build is that successful AI-assisted development still depends on clear direction and continuous feedback. During prototyping, Osimi found that starting with broad concepts and iterating produced better outcomes than attempting to define every detail up-front.

“I found that having a general idea of what I want and then going back and forth between refining the specification and building the UI has been really effective,” Osimi says.

The team also relied heavily on stakeholder conversations and user feedback throughout development. Those interactions helped clarify assumptions, identify gaps, and refine the final experience.

Human expertise still leads the way

Although AI accelerated many aspects of development, the team emphasized that human expertise remained critical.

“Developing a prototype is one thing, but making it a product is different. You have to ensure performance, security, architecture, and privacy considerations.”

Gulsimo Osimi, technical program manager, Microsoft Foundry

Prototypes can be generated quickly, but production systems require additional considerations, including architecture, scalability, security, privacy, accessibility, and performance.

“Developing a prototype is one thing, but making it a product is different,” Osimi says. “You have to ensure performance, security, architecture, and privacy considerations.”

That collaboration between product, marketing, and engineering helped ensure that Foundry Labs delivered a polished experience rather than just a working prototype.

The result is a platform that continues to evolve as new innovations are added and new community experiences emerge.

The post Rebuilding our Microsoft Foundry Labs site with a three-people build team and AI appeared first on Inside Track.

“I’ve always dreamt of being an engineer, and at the time engineering was not a common path for girls in our community,” she says. “But I was always encouraged by my parents and my teachers to pursue my ambitions.”

With the “unwavering support” of her mother, father, and community, those ambitions have led her to a thriving career in software engineering in IT at Microsoft. Priya now works on AI-powered employee experiences, Copilot agents, and enterprise solutions that improve the ways people interact with technology.

From her first day in Microsoft Digital—the company’s IT organization—in 2021, Priya learned to embrace a culture of ownership, working closely across disciplines to support her colleagues.

“At Microsoft, you are always a full-stack engineer,” Priya says. “You’re not a back-end developer, you’re not a front-end developer, you’re not a dev ops engineer—you are everything. And that was a great thing to know. You do not just own one part of the application, you own everything. That gives you a sense of responsibility.”

Forging a career path at an early age

That sense of responsibility, ownership, and leadership started for Priya within her own family. Her mother has a master’s degree in architecture, and Priya grew up seeing how education, leadership, and perseverance could open doors for a family and a community.

Priya carried that lesson into school. She had excellent grades in high school and earned admission to the computer science and engineering program at NIT Jalandhar, a well-respected engineering institute. Entering this field still felt like a challenging step for Priya.

Her early career experiences gave her a broad view of how software supports different kinds of businesses. At Publicis Sapient, she worked across e-commerce, fintech, and retail customers. Moving on to Expedia Group, Priya worked as a software development engineer on large-scale systems serving customers globally.

Learning the Microsoft way of engineering

Joining Microsoft nearly five years ago meant learning a new technical stack and a different operating model. Priya came from a Java and Python background, learning .NET and C# as she moved into the company’s engineering environment.

“A growth mindset means being comfortable with being uncomfortable. Every major step in my career happened when I chose to leave something familiar and challenge myself to learn something new.”

Kanu Priya, software engineer, Microsoft Digital

The bigger shift was not about the language or framework, but a change of mindset. It was the expectation that an engineer should understand the customer problem, build the solution, monitor production health, and keep improving the experience after release.

“A growth mindset means being comfortable with being uncomfortable,” Priya says. “Every major step in my career happened when I chose to leave something familiar and challenge myself to learn something new.”

That philosophy matched the way Amit Raghuwanshi, a principal software engineering manager at Microsoft, describes Priya’s approach to her work. Priya has been part of Raghuwanshi’s team for more than four years, keeping up as the work ranged across asset management, ServiceNow development, and—more recently—agentic development using Azure AI Foundry and Copilot Studio.

“Throughout this journey, we keep changing the tech stack and the challenges, and Kanu has shown a really good growth mindset,” Raghuwanshi says. “She’s always willing to take on new challenges in whichever tech stack it may be.”

For Priya, ownership also means staying connected to what happens after the first release.

“Microsoft taught me that engineering doesn’t end when the code is written,” she says. “Real ownership means understanding the problem, delivering the solution, supporting it in production, and continuously improving the experience for customers.”

Building experiences with AI agents

Priya’s current work brings that ownership model into the world of AI-powered employee experiences. She contributed to the development of our Employee Device Information agent, a self-service experience that helps Microsoft employees manage primary devices and correct inaccurate device information and assignments.

The work matters to our organization, because device data can create friction for employees and support teams when it’s wrong or difficult to update. The agent reduced 70% of manual effort for support teams and BPO organizations by automating work that previously required human intervention.

Priya also contributed to the first version of a purchase experience agent that helps employees explore available devices, compare options, and understand recommendations based on employee persona and company requirements. Then they can move through device selection using a unified, conversational experience.

Raghuwanshi points to the enterprise device work as an example of Priya’s ability to learn quickly and work from the problem outward. Priya was part of that work from the very beginning, helping launch the agent with Copilot Studio so employees could answer device questions and resolve requests quickly.

“The AI era is one of the most exciting shifts I’ve seen in technology. It isn’t just helping us work faster; it’s helping us reimagine how we solve problems and create experiences for people.”

Kanu Priya, software engineer, Microsoft Digital

Microsoft dev teams are also changing how they approach solving future problems. Raghuwanshi says AI is now part of the software development lifecycle, from requirements and specifications through code and deployment. The power of AI has also added a new design question for just about any business process: Can an agent help solve this?

Priya sees that change as more than just a productivity gain.

“The AI era is one of the most exciting shifts I’ve seen in technology,” she says. “It isn’t just helping us work faster; it’s helping us reimagine how we solve problems and create experiences for people.”

Today, Priya is working on next-generation multi-agent experiences that bring device selection and purchasing workflows directly into Microsoft 365 through Copilot-powered experiences.

Raghuwanshi connects that work back to the same trait that brought Priya into engineering in the first place: Agility.

“She is a very fast learner,” Raghuwanshi says. “You give her a new problem and she quickly adapts, then starts showing further curiosity.”

Priya’s promotion in 2023 was an important career milestone, and she credits mentorship from Raghuwanshi and support from teammate Shreekantha Tarikere Indraiah as important factors in that growth.

Helping others follow her path

Priya’s story circles back to Nangal-Shama. During a recent visit home, a young woman from her neighborhood told her, “I want to be like you; I want to be where you are one day.” She says the moment filled her with pride.

“I think that’s what matters: clearing paths for the kids who can dream big,” Priya says. “I always want to make sure people from my hometown are getting resources and opportunities.”

For Priya, this is what it means to succeed at Microsoft—and beyond.

“I think success is not about reaching a destination,” she says. “It’s about continuing to grow, learn, and create opportunities for others along the way.”

The post Building a career in IT with a growth mindset: Kanu Priya’s Microsoft journey appeared first on Inside Track.

“We have different tools to detect if there’s some outage in the network. After we have those alerts converted into tickets, we work to make sure the corporate network gets back up and running.”

Neeraj Mohan, senior site reliability engineering manager, Microsoft Digital

At the scale of Microsoft, a company with more than 900 sites, 500 datacenters, and tens of thousands of network devices, those incidents translate into thousands of tickets. And each ticket requires some combination of decision making, action, collaboration, and communication.

Managing connectivity for that internal network falls to Microsoft Digital, the company’s IT organization.

“We’re like a backbone for supporting the business,” says Neeraj Mohan, a site reliability engineering manager in Microsoft Digital. “We have different tools to detect if there’s some outage in the network. After we have those alerts converted into tickets, we work to make sure the corporate network gets back up and running.”

Through agentic AI, we’re changing how that work gets done. Instead of asking people to chase every diagnostic, status update, ticket handoff, and stakeholder question, our teams are building AI agents that can sense what’s happening, act within clear limits, and validate outcomes before we do the work.

The labor behind every alert

Network operations has always depended on strong tooling, disciplined processes, and experienced engineers. The challenge is that the volume of work keeps rising.

The Microsoft environment includes more than 75,000 devices, over 300,000 users, and in excess of 1 million connected endpoints. The monthly volume of tickets that require human intervention rose as high as 70,000 in January 2025. Eighteen months later, we’d reduced it to less than half—about 27,000—through the application of automation and AI.

That showed great progress, but this many tickets a month still demands a lot of effort. Many of these tickets require enrichment to provide engineers with information like the current device state, neighboring device status, links, logs, and prior troubleshooting. Others require coordination with internal teams, datacenter operations, site services, telecommunications providers, or network device vendors.

The difficult aspect isn’t just the technical fix. It’s the time spent changing context from one system to another, checking partner portals, scheduling work windows, answering leadership questions, and validating whether a fix has actually restored service.

To make matters more complicated, we can’t let just anyone log in to network devices or run changes in a secure environment. We need to design read access, execution permissions, and human approvals deliberately.

To address these challenges, we looked across the incident lifecycle, reviewed ticket volume and manual effort, and prioritized agent opportunities based on impact, user need, and development effort. The first set of agents focused on three connected problems: dynamic troubleshooting, cross-system handoffs, and outage communications.

Three agents, one operating model

The network operations agents we’ve created aren’t a single monolithic utility. They’re a set of focused tools that map to how operations work already happens. We created them using first-party, cloud-based technologies that include Microsoft Azure OpenAI, Microsoft Foundry, Microsoft Agent Framework, Microsoft Copilot Studio, and Microsoft Teams, and then integrated them into our internal incident management (ICM) tool.

Each agent sits close to the work, respects the permissions required for network operations, and gives engineers or stakeholders the next useful action without forcing them to assemble context manually.

Falcon Agent: Fresh diagnostics on demand

Falcon Agent is the action engine in our AI network operations loop. Before this expansion of agentic capabilities, Falcon already supported automated troubleshooting and remediation for known scenarios like access-point-down incidents. A service line could onboard a scenario once, define the troubleshooting steps, and let Falcon handle repeat tickets without asking a directly responsible individual (DRI) to rerun the same actions every time.

The Falcon Agent adds an on-demand layer. If Falcon has already run a workflow and a DRI starts work later in the day, the earlier results might be stale.

“We want to be where the users are. Falcon Agent is available through Teams and the incident management tool, giving engineers a choice of where to work while keeping the controls in place.”

Shayoni (mononym only), product manager 2, Microsoft Digital

With the agent, authorized engineers can refresh troubleshooting, retrieve current logs, check device reachability, validate service restoration, and enrich the ticket with updated information. By the time an engineer opens a ticket, the agent has already refreshed and summarized the ticket.

Falcon Agent also restricts executional prompts to the right engineering users on secure admin workstations (SAWs). Meanwhile, informational prompts can serve a broader audience without exposing device-level control.

“We want to be where the users are,” says Shayoni, a product manager for Falcon in Microsoft Digital. “Falcon Agent is available through Teams and the incident management tool, giving engineers a choice of where to work while keeping the controls in place.”

Smart Bonding Agent: Closing the handoff gap

We can’t fix every incident remotely. If we need to restart a router on site, reseat a cable, access a power supply, or call in a telecommunications provider for a fiber cut, the work shifts from remediation to coordination. That’s where the Smart Bonding Agent comes in.

“This agent provides for lifecycle continuity across systems. It closes the handoff gaps where we would often lose context in the past.”

Suvodip Moitra, senior product manager, Microsoft Digital

The Smart Bonding Agent contains several flows:

• A recommendation agent reviews the incident and identifies the likely issue and the team to engage.
• A ticket steward agent creates the right ticket after human approval and monitors updates.
• A work-window scheduling flow helps connect the right people when the receiving team needs help from Microsoft engineers.

The goal is to keep the engineer in the incident record instead of asking them to jump across portals, emails, and partner systems. At the same time, human approval is intentional by design. We don’t want to reduce one team’s work by flooding a partner with unnecessary tickets.

“This agent provides for lifecycle continuity across systems,” says Suvodip Moitra, a senior product manager responsible for the Smart Bonding Agent in Microsoft Digital. “It closes the handoff gaps where we would often lose context in the past.”

As confidence improves, lower-risk workflows can move toward more automation. The agent already covers Global Datacenter Operations and Puget Sound Network Operations, with expansion underway across more issue types, telecommunications providers, and network device vendors.

Network Outage Insights Agent: Less talk, faster restoration

When a site has a network problem, engineers aren’t the only people who need information.

IT managers want to know whether their location has connectivity. Leaders are responsible for answering to business stakeholders. Help-desk teams need to know whether an employee issue is local to a device or tied to a site-level outage.

“Part of our role is knowing the people we support. This agent helps us understand who we’re serving and what the best forums are where we can interact with them, all in one place.”

Laxman Bhinnale, senior technical program manager, Microsoft Digital

Built on Copilot Studio, the Network Outage Insights Agent supports both push and pull communications. It can send targeted Teams notifications to impacted IT managers on the push side and answer natural-language queries when people need to pull answers.

An IT leader might ask, “Are there any active site outages on my campus?” The agent returns the incident link, current status, estimated time to resolve, impact, region-level details, and historical trends that can help teams spot recurring issues with a site or provider.

The agent also changes how the team learns. It can track feedback in a backlog and connect improvements back to the people who requested them.

“Part of our role is knowing the people we support,” says Laxman Bhinnale, a senior technical program manager in Microsoft Digital. “This agent helps us understand who we’re serving and what the best forums are where we can interact with them, all in one place.”

Great results, exciting opportunities

The measurable impact of our AI network operations agents is still emerging, but the signals of success are clear.

Falcon Agent users report saving around 300 hours in the first few months of operation, equating to 10–15 minutes saved per ticket. The agent has a 60% retention rate and 100% positive user feedback.

We’ve observed that Falcon can handle about 60% of total incident volume already, with an expectation it can reach about 80% as more scenarios mature. The remaining work is likely to include physical or cross-team activity where the Smart Bonding Agent is a better fit.

Meanwhile, our Network Outage Insights dashboard showed 149 outages in one month, with 32 hours saved on communications and reporting.

“The future is multi-agent orchestration: monitoring, troubleshooting, partner engagement, and communication working together.”

Raghavendran Loganathan, principal software engineering manager, Microsoft Digital

The next step is connecting these agents’ efforts. In future flows, the Smart Bonding Agent will be able to engage the right internal or external party while Falcon Agent validates that the issue is resolved. Finally, Outage Agent Insights will communicate impact and recovery status before a post-incident agent generates the postmortem. Each agent keeps its focus, but the work moves as one incident lifecycle.

“The future is multi-agent orchestration: monitoring, troubleshooting, partner engagement, and communication working together,” says Raghavendran Loganathan, a principal software engineering manager in Microsoft Digital.

For our customers, the lesson is that you don’t need to copy our exact network or ticketing model. Instead, study your own top sources of work. Analyze telemetry and see what constitutes your most significant pain points, then try to attack the problem with agents.

That’s the practical pattern: Start with the work that repeats, put agents where people already operate, keep humans in the loop in places where actions carry risk, and measure whether the agent changes real behavior. At Microsoft scale, that’s meant fewer stale tickets, fewer missed handoffs, clearer communications, and more engineering time spent on restoring service so the company can achieve at top performance.

The post From alerts to action: How AI agents are reshaping network operations at Microsoft appeared first on Inside Track.

That’s certainly true for Doug Pierson, a recently retired US Marine Corps Major General who manages our Microsoft Executive Support Team. As part of that role, he works with our company’s senior leaders and created an Executive Cyber Defense service offering to assist them with protecting themselves and the company from Malicious Cyber Actors (MCAs).

“Yes, this is a different situation, but not so much that the lessons don’t apply. We need our executives to be very vigilant.”

Doug Pierson, senior director of IT Service Management, Microsoft Digital

While serving in the U.S. Marines, one of his assignments was deputy commander of Marine Corps Forces Cyber Command, U.S. Cyber Command, where he led multiple Joint Task Forces protecting the United States worldwide from cyber and asymmetric threats via nonstop offensive and defensive cyber operations. At Microsoft, he and his team have created a service offering that is founded on his time in direct contact with near peer and other adversary MCAs to help our executives protect themselves and the company from cyber threats that they face.

The attackers and battlefields may be different, but the concept is the same.

“Yes, this is a different situation, but not so much that the lessons don’t apply,” says Pierson, now a senior director of IT Service Management in Microsoft Digital, the company’s IT organization. “We need our executives to be very vigilant.”

As technology advances and proliferates, so too do the different ways that MCAs hack into businesses.

Executives—and the people around them, such as executive assistants, chiefs of staffs, and other closely aligned administrators—are particularly vulnerable to cyberattacks. As criminals look to access trade secrets, impersonate individuals, and use sensitive or confidential information for malicious means, we’ve seen an expansion in their unique approaches to finding and exploiting vulnerabilities.

Recently, Pierson and our Executive Support Team saw an opportunity to scale up how we protect our leaders’ communications and data, with approaches tailored to each person’s workflows and potential vulnerabilities.

They tackled the problem from three angles:

1. People. Besides Microsoft executive leadership, we advise support staff, clients, client stakeholders, and anyone else who might unintentionally create access vectors that lead bad actors to our executives.
2. Places. Many of our executives travel to high-risk locations that require proactive security measures. We also take into consideration whether a destination has Microsoft infrastructure in place, making it safer. Areas without Microsoft infrastructure might leave someone vulnerable. Airports, for example, might require you to sign onto a public Wi-Fi network or put you at risk of “shoulder surfing,” meaning someone looking at your laptop and gaining access to sensitive information. Our Executive Support Team provide awareness of how to safely navigate through these locations, while partnering with our Executive Protection Unit to enable a multilayered virtual and physical cyber shield while on the road.
3. Things. No matter who the staff member is and where they’re located, we ensure they have devices that are updated with the latest software, functional for mobile use, and include secure measures like cloud backup or VPN. We set them up with travel routers, provide insight on IoT home network devices, and offer support for other tools that secure communications and internal information.

As we continue to refine and build out our operating principles for executives that allow them to conduct business safely from any device and location, we learned several key lessons along the way. In simplest terms, executive support regularly ties in with an internal partnership on executive cyber protection in a “triad”:

• Our Executive Support Team maintains cyber security in proximate access: coordinating directly with the executives and their teams on IT security practices, devices, travel, and immediate concerns.
• Our Executive Protection Unit maintains physical security overwatch for executives in the office and during travel—most notably when in a high-risk area.

Lesson 1: Partner with your CISO team

Our team works with the Microsoft Office of the CISO to monitor and consistently review network and account activity on-net, and off-net for anomalies.

Our initiative has been a success in large part because we set clear, easy-to-follow guidelines and invested time and resources into educating staff on those guidelines. As an example, we deliver quarterly Protect Your Tech upskilling sessions that routinely attract 20 executive assistants (EAs), who learn about things not readily apparent as well as how to find cyber protection capabilities via Executive Support or other teams within Microsoft.

“It started with a foundational set of tips and tricks due to very real and specific need, given active targeting of the Executive Admin community and how much passion EAs have around protecting their executive on multiple levels,” Pierson says.

In newsletters, informational booklets, and the regularly offered Protect Your Tech sessions, we discuss the foundational importance of using multifactor authentication (MFA), strong passwords, keeping devices updated, and avoiding untrusted Wi-Fi networks. We then go from there into more specific measures.

“We provide executives with a secure physical device enabled with Cloud PC, a system that essentially mirrors their primary computer within a highly secure virtual environment.”

Nolan Mitchell, IT service manager, Microsoft Digital

We also regularly educate support staff on how to manage their devices with Entra ID or Intune, save files to OneDrive and SharePoint, and identify scam links or fake accounts.

In addition, we meet with executives prior to travel to high-risk locations, setting them  up with a secure environment in advance of their trip.

“We provide executives with a secure physical device enabled with Cloud PC, a system that essentially mirrors their primary computer within a highly secure virtual environment,” says Nolan Mitchell, an IT service manager in Microsoft Digital.

That means that if a device is lost or stolen, data is designed to remain accessible through the cloud rather than being stored locally on the device.

“We’re seeing governments put laws and policies in place that enable them to view what is on your devices, so we remind our folks that those laws and policies exist, and that they should be mindful or give very specific guidance related to traveling with their devices.”

Elias Gonzales, director of executive protections, Global Security

We also might advise them on what they shouldn’t bring with them.

“We’re seeing governments put laws and policies in place that enable them to view what’s on your devices, so we remind our folks that those laws and policies exist, and that they should be mindful or give very specific guidance related to traveling with their devices,” says Elias Gonzales, a director of executive protection in Microsoft Global Security.

This strong foundation has led to our team being more proactive before a threat emerges, rather than being reactive to attacks that might have already impacted them.

“The fact that folks are engaging us regarding security, for high-risk travel briefs, and to understand the environment they will be operating in before they start moving around—that’s a good thing,” Mitchell says.

Lesson 2: Collaboration is the way

It’s all about working together.

“We realized early on that leveraging our expertise and collaborating across organizations would make our initiative way more effective,” Pierson says.

Much of Pierson’s work at Microsoft Digital, for example, is informed by his extensive experience commanding offensive and defensive cyber units in the Marines.

Likewise, Asaf Kashi, a Microsoft vice president and deputy CISO, taps into his deep knowledge and experience leading teams that maintain Microsoft capabilities, systems, and tool security.

“We notify and involve executives on threat actors and actions around areas of ownership. We tell them,‘Here’s how to keep yourself and your team secure in an agile environment.’”

Asaf Kashi, vice president and deputy CISO

Collaboration across physical and digital security is crucial.

“I think sometimes the can be very siloed, but increasingly, there’s this understanding across Microsoft that it’s a blended threat—you can’t be good at one and ignore the other,” Gonzales adds.

Equally as important is the third arm of the triad: executive support.

Our Executive Support Team also works closely with other executive teams, both within the Microsoft universe (LinkedIn Executive Support as an example) and at other companies via showcase opportunities, to trade best practices.

“We notify and involve executives on threat actors and actions around areas of ownership. We tell them, ‘Here’s how to keep yourself and your team secure in an agile environment,’” says Asaf Kashi, a Microsoft vice president and deputy CISO.

Lesson 3: Stay vigilant and proactive

Cybersecurity is a rapidly moving target, which means we have to stay on top of the latest hacks and trends.

“It changes fast,” Mitchell says. “You need to be dynamic to keep ahead of MCAs.”

One way we do this is by reviewing case studies of attacks and benchmarking our security protocols against our peers and partners.

“It really does involve engagement with the industry and having a pulse on what’s going on there,” Gonzales says.

We also have to remind staff to remain vigilant in even the safest of environments.

“People are the easiest and most vulnerable link in the chain,” Pierson says. “You have to constantly remind yourself to be alert.”

This may mean trading some conveniences for peace of mind.

“Security is intended to be an inconvenience,” Gonzales says. “If it’s an inconvenience for you, it will be for bad actors. And so we need to accept the criticality of it, and do as we’re asked to do when it comes to our devices and our data.”

The post Keeping leaders safe from cyber actors: Three lessons learned from our experience at Microsoft  appeared first on Inside Track.

That was the guiding mantra behind an ambitious cross-company effort involving our team in Microsoft Digital—the company’s IT organization—and a number of our product teams. The effort, internally referred to as the Securing AI Agents initiative, brought together teams from Microsoft Digital, Windows, Entra, Intune, Defender, Purview, and Microsoft Security to validate secure AI agent scenarios inside Microsoft’s corporate tenant.

Together, we set out to prove that AI agents could operate safely inside a real enterprise environment, not just in a controlled demonstration.

“Securing AI in the enterprise at pace requires an integrated, full-stack approach. By validating these capabilities together at enterprise scale, we’re generating the real-world learning to strengthen Microsoft’s products and give customers a trusted blueprint for secure AI adoption.”

Ragini Singh, partner group engineering manager, Microsoft Digital

As part of our role as the company’s Customer Zero, this work was recently showcased by Samantha Song and Scott Hanselman at the Microsoft Build 2026 conference.

“Securing AI in the enterprise at pace requires an integrated, full-stack approach,” says Ragini Singh, a partner group engineering manager in Microsoft Digital. “By validating these capabilities together at enterprise scale, we’re generating the real-world learning to strengthen Microsoft’s products and give customers a trusted blueprint for secure AI adoption. Looking ahead, our vision is to make this integrated security foundation the standard for every enterprise, so organizations can scale autonomous agents with speed, confidence, and trust.”

The Secure AI Agents initiative was the result of an all-hands-on-deck project behind the scenes here at Microsoft: Months of testing, coordination, validation, and refinement that transformed an emerging concept into a governable enterprise pattern.

“All of these teams came together and not only enabled the environment in our IT tenant but also ensured that all the right policies were deployed to make it so that these agents can run safely. That meant not bypassing any of the security parameters that we’ve already deployed.”

Shyam Sunder Gogi, technical program manager, Microsoft Digital

Shyam Sunder Gogi, a technical program manager in Microsoft Digital, served as the organizer of the effort, which initially began with a two-week sprint to get ready for Microsoft Build 2026. The project eventually involved more than 70 stakeholders representing different product and business groups.

“All of these teams came together and not only enabled the environment in our IT tenant but also ensured that all the right policies were deployed to make it so that these agents can run safely,” Gogi says. “That meant not bypassing any of the security parameters that we’ve already deployed.”

Testing in real-world scenarios

Rather than validating solutions in an isolated sandbox, we ran a pilot inside Microsoft Digital, standing up a dedicated Windows 365 Cloud PC environment for roughly 100 internal users.

Pilot participants put the solution through its paces across common developer scenarios. These included:

• Windows OS images with Copilot CLI and OpenClaw pre-installed
• Windows 365 Cloud PC provisioning
• Entra Agent IDs to distinguish human and agent identities
• Defender runtime protection and Purview data loss prevention policies
• Intune device and agent configuration policies
• Microsoft Entra Global Secure Access (GSA) network security controls at runtime

Our goal was to pressure-test a full stack of security guardrails, and to do it the way an actual customer would: with real users and real workloads.

“Customer Zero only works if you’re willing to be the first to hit the rough edges,” says Tom McCleery, a principal group cloud network engineering manager on the Microsoft Infrastructure, Network and Tenant (MINT) team here in Microsoft Digital. “We took the agent scenarios into a live tenant with real users, found the issues product teams couldn’t have surfaced in a lab, and fed every one of them back to the team to address before this ever reached broader enterprise readiness.”

The decision to use Windows 365 Cloud PCs in the pilot proved important.

“We want these device agents to run on our employees’ primary machines, and when necessary, to run on a secondary machine that can be easily isolated and reset if needed,” says Dave Rodriguez, a principal product manager for the Endpoint Experience (EE) team in Microsoft Digital. “That’s why we chose to go with Windows 365 Cloud PCs as a corporate-bound, non-primary environment, where we could have our end users work with those machines as they would with any other device.”

The approach created a safe environment for experimentation while giving teams realistic deployment conditions.

“With Windows 365, there’s no real impact to the devices that we’re using,” says Harshitha Digumarthi, a senior product manager on the EE team. “I really love how we leveraged Windows 365 for piloting this, iterating each time there was a change.”

Digumarthi and her team helped to establish and validate all administrative controls and policies, confirm that they function as expected, and execute a phased rollout strategy—starting with pilots and expanding incrementally while proactively monitoring for risks and user impact.

Identity, data, and network controls under load

Much of the runtime security work fell to our Microsoft Digital team, which validated how the controls behaved once agents were actually operating. A foundational aspect was telling humans and agents apart, so that rules and governance can be more clearly defined based on who is overseeing a process.

“Entra Agent IDs let us give an agent its own distinct identity, instead of having it borrow a person’s,” says Joshua Green, principal software engineering manager on the Microsoft Digital team. “Once you can cleanly separate human and agent identities, everything downstream—access decisions, auditing, runtime protection—gets dramatically more trustworthy.”

On the protection and data-governance side, MINT exercised Defender and Purview against agent activity.

“We validated Defender runtime protection and Purview data loss prevention against live agent behavior,” says Diego Baccino, a principal software engineering manager on the MINT team. “It’s one thing to write a DLP policy; it’s another to confirm that it actually catches what an autonomous agent might try to move. That testing is exactly the kind of value that Customer Zero adds.”

Network-layer controls rounded out the stack.

“Entra Agent IDs let us give an agent its own distinct identity, instead of having it borrow a person’s. Once you can cleanly separate human and agent identities, everything downstream—access decisions, auditing, runtime protection—gets dramatically more trustworthy.”

Pradeep Kunjunny, principal PM manager, Microsoft Digital

This was the advantage of using Global Secure Access, showing that the effort worked with traffic loads generated by a real agent.

“Entra Agent IDs let us give an agent its own distinct identity, instead of having it borrow a person’s,” says Pradeep Kunjunny, a principal PM manager in Microsoft Digital. “Once you can cleanly separate human and agent identities, everything downstream—access decisions, auditing, runtime protection—gets dramatically more trustworthy.”

From pilot to platform

Across the initiative, Microsoft Digital drove coordination and execution across multiple organizations.

“Our close collaboration with Microsoft Digital demonstrates the power of validating security capabilities for AI agents in one of the world’s largest and most complex enterprise environments. The insights we gain from real users and workloads help us strengthen our products and give customers greater confidence as they adopt AI agents securely.”

Aakarsh Nair, vice president of engineering, Microsoft Security

Pilot onboarding, validation of Intune-managed control rollouts, and rapid issue-triage loops improved decision confidence before it was demonstrated at Build, and it shaped the product to prepare it for broader enterprise use.

“Our close collaboration with Microsoft Digital demonstrates the power of validating security capabilities for AI agents in one of the world’s largest and most complex enterprise environments,” says Aakarsh Nair, vice president of engineering in Microsoft Security. “The insights we gain from real users and workloads help us strengthen our products and give customers greater confidence as they adopt AI agents securely.”

The result is a validated blueprint—endpoint, identity, data, and network controls working together—that our customers can now look to as they bring AI agents to their own tenants.

“The winners in enterprise AI won’t just be the teams with the best model experience. They’ll be the teams that make AI operationally trustworthy within the enterprise.”

Tunde Makinde, senior service engineer, Microsoft Digital

It was an initiative that was all about proving that secure AI agent scenarios could work in a real enterprise environment, not just in a demo or a lab setup.

“The winners in enterprise AI won’t just be the teams with the best model experience,” says Tunde Makinde, a senior service engineer for tenant integration and management engineering in Microsoft Digital. “They’ll be the teams that make AI operationally trustworthy within the enterprise.”

Our Secure AI Agents initiative is reinforcing a lesson we’ve learned repeatedly as Customer Zero for the company: Successfully deploying AI in the enterprise isn’t just about delivering innovative capabilities, it’s about ensuring that identity, endpoint, network, runtime, and data protections work together as a cohesive system, enabling employees to use new technologies with confidence while maintaining the governance and security standards that organizations expect.

“By validating these capabilities together at enterprise scale, we’re generating real-world learning to strengthen our products and give customers a trusted blueprint for secure AI adoption.”

Ragini Singh, partner group engineering manager, Microsoft Digital

By bringing together teams from across Microsoft and validating these capabilities in a live enterprise environment, we were able to test how AI agents perform under real-world conditions. The result was more than a successful Build demonstration. It was a practical blueprint that informs how we build our products and provides valuable guidance for companies preparing to adopt agents at scale.

“Securing AI in the enterprise at pace requires an integrated, full-stack approach,” Singh says. “That’s why our team in Microsoft Digital brought together Microsoft Agent 365, Windows 365, Intune, Entra Agent ID and Global Secure Access, Defender, and Purview to secure our agents.”

Working in concert across endpoint, identity, network, runtime, and data, our partnership helped establish the layered security model needed to secure our AI agents.

“By validating these capabilities together at enterprise scale, we’re generating real-world learning to strengthen our products and give customers a trusted blueprint for secure AI adoption,” she says.

Looking ahead, our vision is to make this integrated security foundation the standard for every enterprise, so organizations can scale autonomous agents with speed, confidence, and trust.

The post Securing AI agents in the enterprise: Learnings from our journey at Microsoft appeared first on Inside Track.

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.”

Learn more

Read more of our stories about Managed Cloud Labs.

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.

The post Resolving repetitive support tickets at Microsoft with AI automation appeared first on Inside Track.

At Microsoft, that question emerged from a much larger challenge: How to rapidly upskill our customer and partner-facing teams in a way that is scalable, role-specific, and grounded in real business conversations. To bring this to life, we focused on how our sellers could practice and apply these skills in real-world scenarios.

One example of this approach is Agent J.ai. This agent is an on-demand, AI-powered coach we designed to enhance the skills and performance of 64,000 of our Microsoft Commercial employees serving in sales roles. And it’s not just any AI-powered coach—it’s modeled off of Judson Altoff, CEO of our commercial organization.

For our sales, partner, and service teams, this project represented a shift in how we approach skilling internally. We’re not just delivering training on Microsoft AI solutions; we’re using AI itself to power how those skills are learned in practice.

At Microsoft, this is what Customer Zero looks like in real-world scenarios—using our own AI solutions to solve a core business challenge: Helping customer-facing teams build skills for real customer and partner conversations at scale.

We’re building and testing AI tools inside the organization, observing what consistently works, and sharing practical insights with our customers.

Learn more

Check out more stories on our Customer Zero journey with our products and services here at Microsoft.

Upskilling employees on Microsoft AI solutions

Upskilling thousands of employees at scale—and giving them customized instruction that fits their specific needs—is a tremendous challenge for any organization. Teams have limited time, and there’s the issue of making sure the content is up to date and in line with organizational strategy. These teams also need to access accurate, specific information across a vast content repository.

Our internal AI Transformation team had already embarked on a global campaign to educate and train our customer and partner-facing teams on Microsoft AI solutions. They had developed job aids, demos, and videos tailored to sales scenarios like account planning, opportunity qualification, and customer meeting prep. They’d also launched campaigns, Learning Days, and contests like “The Road to 60,” which encouraged teams to reach 60% daily active usage of our Al solutions.

These initiatives showed positive results, with a 68% boost in customer planning efficiency for an account executive, and a 62% gain in agile workflows for a business program manager. But the teams also realized that AI itself could help scale and customize this training even further. That’s where Agent J.ai came in.

Upskilling with a familiar AI coach

According to Stacey Herod, a senior learning manager here at Microsoft, project members gathered focus groups of employees in varying roles across the commercial organization to help understand the issues that customer and partner-facing teams face. They pinpointed challenges with speaking the language of the customer, establishing executive presence, connecting solutions to business outcomes, and understanding how to best serve as a trusted advisor to customers.

The team wanted to develop a coaching experience that could simulate real customer conversations and reflect how strong sellers actually operate in the field.

At the center of that experience is a familiar voice modeled after Althoff. For customer and partner-facing teams, Althoff’s approach represents how experienced leaders define the vision and share deep expertise that speaks to our customers’ needs.

“It started as a fun experiment—replicating Judson as a mini version of himself, where you could always have him by your side, coaching you in real time,” Herod says. “But it quickly revealed an opportunity to scale executive-level guidance. Now, customer and partner-facing teams can bring their own scenarios and engage with a trusted voice—making learning more personal and impactful.”

Instead of a one-to-many model, we can now interact with and learn from that perspective on demand.

Learning isn’t about just accessing information; it’s about understanding how experienced leaders navigate conversations, connect solutions to business outcomes, and build trust with customers and partners. Modeling the experience on that perspective helped translate those behaviors into a scalable, practical format.

Althoff was fully onboard with the project and closely involved with its creation, adhering to the company’s principles around responsible and ethical AI use. We also knew that having executive buy-in and sponsorship is critical when it comes to increasing adoption of tools like Agent J.ai across the company.

“We want people to feel comfortable practicing conversations with artificial intelligence, so they can refine their approach and be prepared ahead of a customer meeting, not practicing with their customers.”

Stacey Herod, senior learning manager, Microsoft

Rethinking how skills are built with AI

At Microsoft, this shift is already underway. Agent J.ai is part of a broader ongoing effort to rethink how skills are built with AI, and our employees are trusting the experience. Importantly, conversations that our employees are having aren’t tied to individual identities, rather the content is curated to the specific needs around upskilling employees. That sense of privacy made it easier for sellers to engage openly and use it without hesitation.

“There’s behavioral change happening at the agentic level—people’s level of comfort using agents,” Herod says. “We want people to feel comfortable practicing conversations with artificial intelligence, so they can refine their approach and be prepared ahead of a customer meeting, not practicing with their customers.”

Practice is central to this model.

“Agent J.ai fundamentally changes skilling—from static training to dynamic, scenario-driven coaching. It adapts in real time to each seller’s context, enabling hundreds of roles and limitless customer scenarios to be addressed instantly—something our industry has never achieved at this scale.”

Jennifer Wheeler, senior learning manager, Microsoft

Role-playing helps simulate realistic, highly customized conversations that customer and partner-facing teams might have with a certain type of client or executive. For example, an account executive might need to prepare for an upcoming conversation with a chief security officer.

Our AI coach’s training must be able to recognize that context.

“Agent J.ai fundamentally changes skilling—from static training to dynamic, scenario-driven coaching,” says Jennifer Wheeler, a senior learning manager at Microsoft. “It adapts in real time to each seller’s context, enabling hundreds of roles and limitless customer scenarios to be addressed instantly—something our industry has never achieved at this scale.”

An AI agent trained on curated content, Microsoft’s customer engagement and coaching frameworks, and a trusted persona could finally deliver what teams lacked—authentic conversation practice tailored to real scenarios, industries, and executive audiences.

Launching and refining our AI coach

We rolled Agent J.ai out quickly, with a focus on iterating based on real-world use. From the start, members of the cross-functional team prioritized capturing feedback and continuously refining the experience based on how sellers use the tool in practice.

“We are seeing the rise of voice-first subject matter expertise agents across industries, and Agent J.ai has demonstrated what’s possible in the Frontier.”

Max Felker, principal product manager, Microsoft

The team began the Agent J.ai project in earnest in March 2025 and shipped the first version just five months later in July. Input from super users helped test specific scenarios, while broader anonymous forms surfaced key insights.

One example: In the user experience, we discovered that using overly realistic or animated avatars for the AI coach could be perceived by users as gimmicky and might prove more expensive to produce. A simple photo avatar ended up doing the trick.  

As a voice-based coaching agent, Agent J.ai is designed for spoken, two-way conversations with users.

“We are seeing the rise of voice-first subject matter expertise agents across industries, and Agent J.ai has demonstrated what’s possible in the Frontier,” says Max Felker, principal product manager for Microsoft.

Incorporating updated industry trends and live events

Staying current is critical for customer-facing teams, especially when conversations shift quickly based on new announcements and industry trends.

“I had a couple of people who said, ‘This is incredible. This saved me hours, or weeks, and I was able to have it in 5 minutes.’ That’s something we never thought would be possible.”

Stacey Herod, senior learning manager, Microsoft

Typically, pulling information together for real customer conversations took time. Powered by Microsoft Azure and Azure AI, Agent J.ai can incorporate announcements from events like Microsoft Ignite, so the AI coach could help prepare sales teams for questions about the latest news.

“I had a couple people who said, ‘This is incredible. This saved me hours, or weeks, and I was able to have it in 5 minutes,’” Herod says. “That’s something we never thought would be possible.”

Others reported saving up to 50% of their standard prep time, not to mention more than $80 million in deals that were influenced through refined strategy and approach.

In the end, the team was laser-focused on making something valuable and accessible for their sales teams.

“If the Agent wasn’t useful, then our learners were never going to come back and use it,” says Herod, a seller in the past herself. “You have one shot to impress sellers. We’re a tough community.”

The post How Microsoft built an AI coach to scale personalized sales training appeared first on Inside Track.

Enter the power of sophisticated data tools and agentic AI. 

A great example of this came when our Microsoft Digital engineers and product managers were trying to get a handle on our sprawling software licensing landscape—thousands of third-party tools that our employees rely on in their work.

“We realized there were all these fragmented, scattered repositories of licensing data across many teams, all with different ownership,” says Ahmed Musa, a senior software engineer in Microsoft Digital, the company’s IT organization. “There was no visibility into what contracts existed or how they were being used. We needed a single solution.”

The answer was IntelLicense, an enterprise-wide intelligence platform that collects product information, licensing contracts, cost data, employee usage telemetry, and supplier details in one system. This all comes together in our Software Asset Management (SAM) portal, where we deliver enterprise-grade governance through a modern user experience.

“IntelLicense is a game-changer for us. Before, internal software licensing was manual and labor-intensive—it could take months to make sense of the data. Now, an answer that used to take up to six months for us to track down can be generated immediately using this platform we’ve created.”

Ahmed Musa, senior software engineer, Microsoft Digital

Our IntelLicense platform uses the advanced capabilities of agentic AI to answer queries and generate insights on the data, giving us much greater understanding and visibility while enabling us to reduce license duplication, simplify procurement, and cut spending.

This benefits our employees who need to license software, our software asset managers, and our Procurement team, which manages the process on our back end to make sure the company isn’t wasting money and resources—especially considering the company makes significant annual investments in third-party software licensing.

This solution shows how at Microsoft we’re constantly looking for ways to apply AI to help solve enterprise-level challenges at scale—the hallmark of a Frontier Firm.  

“IntelLicense is a game-changer for us,” says Musa, the principal architect for the project. “Before, everything around internal software licensing was manual and labor-intensive,—it could take months to make sense of the data. Now, an answer that used to take up to six months for us to track down can be generated immediately using this platform we’ve created.”

Uncovering the challenge

With more than 200,000 employees working across over 100 countries worldwide, attempting to centralize information at an organization the size of Microsoft is never easy. The state of our third-party software licensing system was no different.

“As we looked beyond just employees finding software and deeper into the process, we began to see the challenge was also about purchasing, how we dealt with suppliers, and how we managed the licenses at a higher level. That’s where we saw the big opportunity.”

Revanth Chandra Pydimarri, senior product manager, Microsoft Digital

We began this journey nearly three years ago. The first big need we identified came from employee feedback that indicated it was difficult to figure out how to identify and license third-party software tools. But as we began to analyze the larger picture, we realized that the problem was much more layered and complex.

“As we looked beyond just employees finding software and deeper into the process, we began to see the challenge was also about purchasing, how we dealt with suppliers, and how we managed the licenses at a higher level,” says Revanth Chandra Pydimarri, a senior product manager in Microsoft Digital. “That’s where we saw the big opportunity.”

But by expanding the scope of the project, we were setting off on a long and technically daunting quest.

“I think we counted 19 different systems that contained relevant licensing data. Working with all the different teams to pull that data together was the first big challenge we had to go after.”

Jay Selveraj, principal software engineering manager, Microsoft Digital

Tackling the data first

The first step was to gain visibility into all our third-party software contracts, our suppliers, and the actual product usage across the company. But our teams were operating in silos, each maintaining their own agreements and data about software licenses.

“This was fundamentally a data problem,” says Jay Selveraj, a principal software engineering manager in Microsoft Digital. “The enterprise data for license management is highly distributed, non-standard, and spread across the company. I think we counted 19 different systems that contained relevant licensing data. Working with all the different teams to pull that data together was the first big challenge we had to go after.”

To fully understand the software asset management process, Selveraj and Chandra Pydimarri were charged with creating a journey map to show the steps, dependencies, and stakeholders involved.   

“It was a very daunting task for us,” Selveraj says. “We identified so many different bottlenecks. And that’s when we decided we can’t just troubleshoot the existing process—we needed to build a new platform that would span the enterprise.”

To accomplish this, they turned to Microsoft Fabric, which at the time was a relatively new product. Fabric provided the power and flexibility needed for this kind of project.

“Microsoft Fabric was designed as a unified data platform for engineers, making it an ideal fit for this project.”

Misrak Ararso, senior software engineer, Microsoft Digital

And as Customer Zero for Microsoft, we were excited to be early adopters of Fabric (it had just gone into public preview). The fact that we were able to try it out on a real enterprise challenge we were facing was both a strategic advantage and a bonus.

“Microsoft Fabric was designed as a unified data platform for engineers, making it an ideal fit for this project,” says Misrak Ararso, a senior software engineer in Microsoft Digital, who also worked on IntelLicense. “It has great features like Data Wrangler, which allowed us to drill down on the data and clean it up quickly. We also used Microsoft OneLake, which meant we avoided having to duplicate data before working on it.”

Ararso appreciates how Microsoft Fabric continues to evolve with new AI capabilities, making it an increasingly powerful and beneficial tool for data engineering.

“Early adoption wasn’t always smooth,” she says. “We encountered challenges, sharing feedback when we did, and we benefited from improvements as the platform matured alongside our implementation.”

Reducing waste and saving money in procurement

Before we developed IntelLicense, our Procurement team at Microsoft also struggled to answer basic questions about our software licenses.

“It was very difficult to gauge usage, consolidate agreements, and do cost optimization. And when we tried to audit our contracts and move licenses around, it all had to be done manually and took a lot of time and effort. IntelLicense addresses that.”

Rasa Amiri, senior sourcing manager, Financial Operations

The Procurement team is responsible for negotiating contracts, pricing, and terms and conditions with thousands of different suppliers. However, it can be difficult to negotiate volume discounts and manage the other aspects of licensing if you don’t have a holistic view across the enterprise.

In a typical example, one group at Microsoft might purchase 20 software licenses from a particular supplier, but then only use 15 of them. Another team needs 5 licenses, but they have no idea that there are unused licenses they could tap from the other group, so they purchase their own. And when an employee moves teams or leaves the company, their software licenses often go unused rather than get reassigned.

“It was very difficult to gauge usage, consolidate agreements, and do cost optimization,” says Rasa Amiri, a senior sourcing manager in our Financial Operations group. “And when we tried to audit our contracts and move licenses around, it all had to be done manually and took a lot of time and effort. IntelLicense addresses that.”

IntelLicense structure

The IntelLicense solution consists of three parts: a UX layer, an agentic AI layer, and a data layer.

According to Amiri, one helpful feature of IntelLicense is the ability to see if a software license is not being used, and then directly contact that employee (or license owner) to say, “Hey, it looks like you’re not using this license. Can we reallocate it?”

“We have a real-time dashboard called the SAM portal that we can now use for that, focused on our top 200 suppliers,” Amiri says. “Now, every time we negotiate a deal, it’s uploaded into IntelLicense with all the details—the cost, the contract, the number of licenses. Not only does it help us with reallocation, it helps us quickly resolve issues we have with suppliers who want to charge us for overuse.”

Musa agrees.

“The IntelLicense platform can identify overlapping tools already in use and surface relevant alternatives, enabling more informed, cost-efficient decisions across the organization,” he says.

Introducing these kinds of efficiencies can quickly generate significant cost savings at an organization the size of Microsoft. Our internal data shows that IntelLicense drove substantial savings in software licensing fees over the last fiscal year. And we have greater ambitions for the future—Chandra Pydimarri cited industry studies that show up to 20% of third-party software spending is unnecessary. That’s huge potential savings for an enterprise organization.

“As we evolved the platform, we realized that users don’t want just another dashboard—they need decision intelligence. They need a system that can connect signals across datasets, surface actionable insights, and guide decisions in real time, so they can move faster and act with confidence.”

Urvi Sengar, senior software engineer, Microsoft Digital

Adding an AI layer

The Software Asset Management portal was a strong foundation for centralizing licensing data, but we wanted to take the solution further.

It was one thing to centralize and surface data with all the relevant data about third-party software licenses. It was a whole different challenge to build a system that helped the user understand the data, ask the right questions, and turn insights into decisions.

“As we evolved the platform, we realized that users don’t just want another dashboard—they need decision intelligence,” says Urvi Sengar, a senior software engineer in Microsoft Digital. “They need a system that can connect signals across datasets, surface actionable insights, and guide decisions in real time, so they can move faster and act with confidence.”

So Sengar and her fellow engineers set to work adding an agentic layer to IntelLicense that could provide those AI-driven insights. They used Microsoft Foundry to create a multi-agent solution that could handle all the various needs users of the system might have.

“With the multi-agent architecture that we followed, we have a workflow manager that delegates any user query to specialized agents,” Sengar says. “One agent handles license management, another deals with supplier management, another can support audit scenarios. Each agent understands the user’s intent and can call any deterministic workflows when needed.”

Sengar sees the agentic layer as the transformation of IntelLicense from a reporting tool into an intelligent system that can deliver contextual, on-demand insights and help users take action through workflows. It also aligns with the growing expectations for a more conversational, Copilot-like AI experience, where users can ask questions naturally and receive meaningful, actionable responses in real time.

“The platform delivers proactive insights through the portal while also enabling users to explore them on-demand, in the context of their work,” Sengar says.

She goes on to describe a scenario where a software asset manager is in the middle of negotiating a contract with a supplier. If they have a new idea, question, or strategy they want to validate, the portal can surface relevant recommendations and insights right away. If they want to go deeper, they can use the chat interface to ask questions and get instant access to the latest context-aware information in a dynamic way, without having to leave the flow of their work.

“That’s where we see the future of AI-driven work going,” Sengar says. “It’s using AI not only to surface insights, but to help people explore them further, act on them, and make better decisions faster.”

Applying intelligence across the enterprise

Large enterprise organizations like Microsoft face this kind of challenge in many areas: how to maximize efficiency by centrally managing a process that is scattered across many different teams and systems, with data that is often inaccessible or systems that are incompatible. Teams have often developed different ways of accomplishing the same task and are reluctant to change.

“We want to make the biggest difference for the company—that’s the ultimate goal. At the end of the day, we want to make sure there is plenty of cost savings produced. Beyond that, we want to apply as much intelligence as possible to the problem, so that AI is impacting all aspects of the process.”

Senthil Selveraj, principal group product manager, Microsoft Digital

Our approach is to apply AI where it makes sense, using continuous improvement principles to guide us. We also look to our AI councils to make sure that we’re following best practices.

This ensures that when we at Microsoft Digital tackle something like software licensing, we’re going to achieve a transformational result that will pay big dividends across the company.

“We want to make the biggest difference for the company—that’s the ultimate goal,” says Senthil Selveraj, a principal group product manager in Microsoft Digital. “At the end of the day, we want to make sure there is plenty of cost savings produced. Beyond that, we want to apply as much intelligence as possible to the problem, so that AI is impacting all aspects of the process. That’s where we’ll see the largest, most impactful benefits.”

The post Taming software licensing sprawl at Microsoft with an AI-driven solution appeared first on Inside Track.

Today, the company has grown into a global organization with more than 200,000 employees. They all rely on Microsoft Digital—the company’s IT organization—to provide the tools, technologies, and solutions that empower them to accomplish more every day.

The need for digital transformation

The history of information technology is one of constant evolution, and the pace of change has never felt greater than it does right now. The AI capabilities and other groundbreaking innovations unveiled in the last few years show the potential to radically transform our world and change the way we think about and operate all IT services.

When the world pivoted to remote online work and collaboration because of the COVID-19 pandemic, it was just one example of how digital transformation doesn’t always happen in a straight line or on a predictable schedule. Our company’s history of shaping and adapting its IT organization to the latest challenges faced by employees and partners is no different; marked by big bets and strategic shifts that reflect our ever-changing world.

Mapping our IT journey

Today, Microsoft Digital is the team that powers, protects, and transforms the digital employee experience across all devices, applications, and hybrid infrastructure at the company. Using our deep knowledge and experience in enterprise IT, we’re pivoting to help lead the company’s AI transformation while also sharing our journey with customers so they can take advantage of this generational opportunity to reshape their businesses and IT operations.

To understand where we’re going, it helps to take a look at where we’ve been. This article explores the details of the major eras of our IT history and then shifts to examine the trendlines and technological innovations that are shaping Microsoft now.

On-Premises IT (founding to 2009)

It’s useful to break the history of our IT operations into different eras. For the first three decades or so from its founding in 1975, Microsoft operated with on-premises IT systems. This era was characterized by the setup, operation, and maintenance of onsite physical technology—servers, datacenters, and other hardware infrastructure.

During this time, IT roles were narrowly defined. IT team members functioned primarily as “order-takers,” with limited influence over strategic decisions.

Because funding was inconsistent, our IT organization had limited growth opportunities and relied on vendors for development work. Gaps were filled in with “shadow IT,” where internal teams would sometimes procure their own hardware or software without formal IT approval or standards.

We established security as an early priority for the company. Cofounder Bill Gates launched the Trustworthy Computing initiative more than two decades ago, an effort emphasizing the importance of security, privacy, and reliability across Microsoft products and services both internally and externally.

Our On-Premises IT era established the foundation that would become crucial to the company’s future digital transformation.

All in on the cloud: The Cloud and Culture era (2010-2018)

Cloud computing marked the next significant shift in the history of IT at Microsoft. It began in 2010 under the leadership of CEO Steve Ballmer, signaling a major break with the previous era of physical IT infrastructure and an important step toward today’s distributed-computing world.

The launch of the cloud computing platform then known as Windows Azure heralded this new era, as we transitioned away from an IT philosophy focused on the Windows desktop client toward a more platform-agnostic view. Cloud computing offered extensive advantages for customers and for our own IT environment, in terms of cost, performance, security, and scalability.

We started our journey by moving productivity workloads (Exchange and SharePoint) to the cloud. Then, we shifted new development to Azure and optimized modern applications to run in the cloud. We also moved existing applications targeted for migration to virtual machines. Today, more than 98% of our IT workloads run on Azure.

Cultural transformation

Another important shift during this era was the profound cultural transformation at Microsoft sparked by new CEO Satya Nadella, who rose to the top job at the company in 2014. Nadella had previously run the Microsoft cloud computing and enterprise group, so he was already steeped in the idea of transformational change at the company.

“Achieving our mission requires us to evolve our culture. It all starts with a growth mindset—a passion to learn and bring our best every day to make a bigger difference in the world.”

Satya Nadella, CEO, Microsoft

Before Nadella’s ascension, Microsoft had long been known for its extremely competitive, “know-it-all” culture. Employees succeeded by showcasing their own individual achievements and how their accomplishments exceeded their peers.

Nadella changed this ethos by championing a growth mindset, encouraging employees to be “learn-it-alls” rather than “know-it-alls.” The shift included placing new importance on how employees contributed to the success of others, a value that was incorporated into individual performance reviews. Nadella made this transformation his personal mission and directed leadership to propagate the new philosophy at all levels across the organization.

“Achieving our mission requires us to evolve our culture,” Nadella says. “It all starts with a growth mindset—a passion to learn and bring our best every day to make a bigger difference in the world.”

The combination of the shift to cloud computing infrastructure and overhauling the company culture helped set the stage for the major technological innovations to come.

A new vision: The Modern Engineering era (2018-2023)

For years, IT at Microsoft had been order takers, doing what the business requested with limited ability to impact strategic priorities. That changed as we shifted to become a modern engineering organization. With support from our executive leadership, IT was elevated to become a peer engineering function at Microsoft.

Rather than simply taking orders, the team was empowered to lead with a strong vision for the future. In fact, leading with vision is the primary hallmark of our Modern Engineering era. As we moved into this era, we needed a clearly articulated view of our goals as an IT organization aligned to the needs of our business partners, as well as the resources needed to achieve them.

Role transformation

Transitioning to become a modern engineering organization required Microsoft Digital to adapt our legacy approach to IT.

Operating an engineering organization in a cloud environment meant new roles, new skills, and a new mindset. With no need to manage physical hardware, our modern IT professionals were freed to work more closely with business partners, requiring greater strategic acumen. The team was now focused on DevOps, Agile program management, and user-centric design principles.

User-centric, coherent design

Our design philosophy puts the user—an employee or guest—at the heart of every decision we make at Microsoft Digital.

The goal of this approach is to make tasks that might have previously caused friction to become simpler. Instead of dealing with disconnected systems, user-centric design introduces consistent and logical flow between services. This makes it easier for people to access services, learn how to use them, and then put them to good use.

Microsoft also embraces coherent design across all our products. A familiar look and feel, along with consistent usage patterns, accelerates employee usage and adoption. 

Embracing work-from-anywhere capability

During the pandemic, when our workforce was still fully remote, our organization was already starting to think about what the new hybrid workplace would look like when people started returning to the office. We identified three key dimensions of the employee experience:

• Physical spaces: We partner with Global Workplace Services to plan and deploy meeting spaces with amazing digital capabilities that support an inclusive approach to hybrid productivity.
• Digital capabilities: We keep employees productive and their digital environment safe and secure, no matter where they’re located or how they connect.
• Culture: A strong partnership with HR ensures that digital experiences support our company culture.

Managing shadow IT with a culture of trust

Shadow IT is the unknown and unmanaged set of applications, services, and infrastructure that are developed and managed outside standard IT policies. Shadow IT typically crops up when engineering teams are unable to support the needs of non-engineering partners, a situation that could arise from a lack of available capacity or the need for specialized domain solutions. 

While earlier eras of our IT history focused on trying to prevent shadow IT, we are now concentrating on managing it. We use Azure best practices to optimize shadow IT and Microsoft 365 governance policies to ensure that our corporate security, privacy, and accessibility standards are met. We empower our employees to create whatever they need within our tenant, including PowerApps, SharePoint sites, Teams channels, or agents, mitigating the need for “shadow” solutions while also providing visibility into how our employees are using our own technology.

Learn how optimizing our Microsoft Azure usage is helping us manage our Shadow IT.

The Era of AI (2023 to present)

The latest chapter in the history of our organization’s digital transformation is defined by the integration of AI and agents into IT operations. AI is revolutionizing how Microsoft does IT at enterprise scale, driving efficiency and innovation across the board. From the apps, workflows, and services that power our employee experience to the network, infrastructure, and devices that enable employee productivity, our AI-focused investments provide a solid foundation for the innovations that we are constantly implementing. As we look at the future of Microsoft Digital, we’re focused on four key priorities: security, service fundamentals, acting as Customer Zero, and AI-powered innovation. We’re working to excel in all four domains with the help of our industry-leading AI capabilities.  

“Our mission is to power and protect Microsoft, and that starts with an unwavering commitment to the Secure Future Initiative.”

Brian Fielder, vice president, Microsoft Digital

Securing our future

Security is our highest priority. The Microsoft Secure Future Initiative aligns every team with a shared approach, common priorities, and consistent milestones to harden our security posture across all products and services.  

“Prioritizing security above all else is critical to our company’s future,” Nadella says. “Every task we take on—from a line of code to a customer or partner process—is an opportunity to help bolster our own security and that of our entire ecosystem. If you’re faced with a tradeoff between security and another priority, your answer is clear: Do security.”

The Secure Future Initiative is built on three core principles: Secure by design, secure by default, and secure operations. As the company’s IT organization, we work relentlessly to fulfill the key pillars of the Secure Future initiative across all our systems, including:

• Safeguarding identities and secrets
• Protecting tenants and isolating production systems
• Securing networks and engineering systems
• Enhancing threat detection
• Expediting response and remediation

“Our mission is to power and protect Microsoft, and that starts with an unwavering commitment to the Secure Future Initiative,” says Brian Fielder, vice president of Microsoft Digital.

Secure Future Initiative | Microsoft

Foundations: Service fundamentals

The second pillar is to maintain the highest standards of service fundamentals. These are the essential capabilities and practices that enable us to deliver reliable, secure, and compliant services companywide. Adhering to the highest standards of service fundamentals ensures that our organization continues to play a critical role in running the company’s business and enabling innovation, agility, and resilience in a fast-changing and competitive environment.

Customer Zero

The third pillar is acting as Customer Zero for Microsoft’s most important products and services, like Copilot Studio, Microsoft Teams, and Agent 365. In Microsoft Digital, we take pride in being the first customer for a wide variety of Microsoft products and services, relentlessly focusing on our own employee experience to create products that enable every person on the planet to achieve more.

Being Customer Zero means forging a deep partnership between our IT organization and product engineering groups to envision the right experiences, co-develop innovative solutions, and then listen to and act on insights gathered from our employees. We work together to stay grounded in the way our employees use our products every day, so your employees can benefit from our insights prior to external product launches.

Read about how we’re improving our employee experience through our Customer Zero focus.

AI-powered innovation

The final pillar of this era is innovating with AI to transform the digital experience at Microsoft. By doing all the fundamental work detailed above—security, foundations, and Customer Zero—extremely well, we gain the confidence and earn the trust necessary to embed AI across our full portfolio of services. We do this over three key dimensions: core IT services, employee experiences, and corporate functions.

Core IT services: Transforming and securing our network and infrastructure

We’re focused on using AI to infuse data-driven intelligence into every part of our infrastructure and network operations. This allows us to optimize operations and increase security while simultaneously improving outcomes.

Examples include:

• Network observability and governance: Ensuring data accuracy, eliminating non-compliant hardware and software, and real-time updates
• Securing endpoints: Device management, asset management, and patching
• Zero Trust networking: Isolating device classes and limiting attacker’s movements across the network
• Network access: Azure VPN, identity management, and Secure Access Workstation (SAW) infrastructure security

Learn how we’re transforming our enterprise IT operations at Microsoft.

Core IT services: Tenant management

We manage one of the most complex tenants anywhere. Governance today is a somewhat fragmented experience, with no clear mechanism for IT to safely enable self-service asset creation for sites, Teams, groups, Power Apps, and so on. These unmanaged assets increase the risk of over-sharing sensitive data and compromise the health and security of our IT environment.

In the world of AI, security through obscurity is no longer a viable option. This means data hygiene, permission management, and data protection are essential to providing trustworthy AI tools that don’t overexpose sensitive content, while still providing quality responses.

Read about one way we’re improving security by protecting elevated-privilege accounts at Microsoft.

Core IT services: Support

We’re using generative AI to transform the way our employees interact with our support services. IT issues will be either auto-remediated or resolved remotely and instantly through conversational, personalized, and contextualized solutions, often without a human agent’s intervention.

We’ll accomplish this with a focus on the following:

• User experience: Our employees are using the AI-powered Employee Self-Service Agent to access personalized, accurate, and cost-effective issue resolution. Future goals include implementing a seamless transition to a human agent while the user stays within the agentic Copilot experience.
• Human agent experience: Operational efficiency and automation are being integrated into the Service Operations Workspace. The service includes chat and incident summarization that recommends best next actions and drafts contextual answers to queries.

Find out how we’re transforming IT support at Microsoft with AI and the Employee Self-Service Agent.

Defragmenting the employee experience

The second dimension where we’re implementing our AI vision to make a difference is our employee experience. Our vision is to deliver a unified, connected, and personalized experience where users can access employee data, tools, and insights from one place.

“We see AI as the key to unlocking the full potential of our employees, delivering personalized experiences that empower us to work smarter, faster, and happier—unleashing the innovation and collaboration necessary for our success.”

Vijaya Alaparthi, principal group product manager, Microsoft Digital

One of the key ways we’re doing this is with Microsoft 365 Copilot, which functions as a “UI for AI” across our employee tools and services. An example is our Employee Self-Service Agent, an AI-driven tool based on Copilot that helps employees more efficiently find context-specific answers to their questions using natural language queries.

“We see AI as the key to unlocking the full potential of our employees, delivering personalized experiences that empower us to work smarter, faster, and happier—unleashing the innovation and collaboration necessary for our success,” says Vijaya Alaparthi, a principal group product manager in Microsoft Digital.

To achieve our vision, we’re building a workplace where AI defragments the employee experience by:

• Providing contextual support in the flow of work
• Reducing the number of sites and apps an employee must remember
• Using Microsoft 365 Copilot as the “UI for AI,” making it simple for employees to find information, take action, and even fully automate certain repeatable tasks

Corporate functions growth

Our third major priority in Microsoft Digital is to improve how we support the company’s corporate functions organizations, including legal and real estate and facilities.

“With AI, we have so many new ways to innovate. From optimizing building occupancy, to streamlining commute services, to automating contract and document management, we have incredible potential to make our corporate functions more efficient and impactful.”

Becky West, principal group product manager, Microsoft Digital

This is a particular challenge, as these teams are being asked to do more with less today; Microsoft can no longer afford to grow operational costs at the same rate as in the past.

AI is playing a fundamental role in transforming the business workflows of our corporate functions partners while improving operational efficiency, user productivity, regulatory and corporate compliance, and data-driven decision making. It’s revolutionizing the way they operate by automating repetitive and time-consuming operational tasks.

“With AI, we have so many new ways to innovate,” says Becky West, a principal group product manager in Microsoft Digital. “From optimizing building occupancy, to streamlining commute services, to automating contract and document management, we have incredible potential to make our corporate functions more efficient and impactful.”

Some of the corporate functions taking advantage of AI capabilities and related increased efficiencies include:

• Real estate and facilities: In supporting the technology needs for more than 500 company buildings worldwide, we are poised to use AI and related innovations to implement cost savings in the areas of workspace systems, facilities management, and space management.

Find out how we’re transforming facility operations at Microsoft with AI maps.

• Travel and expense: Our plan is to work for near-elimination of the traditional expense reporting process through AI-based and touchless experiences, driving simplification and productivity gains.

Check out how OneExpense transformed our employee expense reporting.

• Legal: Our vision for integrating AI into Corporate, External, and Legal Affairs (CELA) includes more discoverable legal findings, better corporate document management with the Docufy platform, enhanced engagement with Microsoft Philanthropies, and accelerated support for business-critical functions such as immigration, contracting, and insider trading compliance.

Read how AI is revolutionizing the way we support corporate functions at Microsoft.

Agentic AI: Becoming a Frontier Firm

This era of AI in IT has quickly morphed into a world in which agents are having major impacts across the enterprise. Microsoft Digital plays a central role in helping the company embrace this change and transform into a Frontier Firm: an organization that has deeply embedded AI and agents into its operations, products, and culture

As a Frontier Firm, we go beyond simply adopting AI as a discrete tool or additional technology. We’re actively integrating intelligent systems, rich data platforms, and human knowledge into a unified operating model, where automation, decision making, and innovation combine to spark acceleration at scale. Agentic AI is a core enterprise capability for us, powering everything from employee productivity to customer experiences and strategic decisions.

As Microsoft progresses into this agentic AI future—where autonomous or semi-autonomous AI agents understand context, take actions, and collaborate alongside humans—Microsoft Digital has played a lead role in deploying these capabilities internally. We’ve led the early adoption of tools like Microsoft 365 Copilot, Azure AI services, and custom-built agents that help us automate repetitive tasks, surface insights, and orchestrate workflows across systems while enforcing strict governance policies. Examples include AI-powered agents that assist in IT service management, network monitoring, and enterprise knowledge retrieval, which allow employees to focus on higher-value work and maximize their individual impact.

As AI agents continue to grow in power and functionality and become more deeply integrated into the daily workflows of knowledge professionals, Microsoft IT will maintain our leadership role and operate at the bleeding edge of this technological revolution. 

A catalyst for change and growth

Microsoft’s digital transformation is a story of evolutionary change, resilience, and adaptation across multiple eras of information technology. From our origins as a traditional IT organization to becoming a modern engineering organization focused on driving AI-powered innovation, we in Microsoft Digital remain a catalyst for change within the company and our industry.

With our insights born from customer and employee obsession, we’re committed to streamlining IT operations while prioritizing security, revolutionizing user services, and facilitating corporate functions growth and development. All with the overarching goal of making Microsoft employees everywhere more productive while showing our customers and partners what’s possible as we move forward together into the future of IT.

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