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.

“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.

It worked, up to a point. Individual developers got faster. But the gains stopped there.

“We quickly identified that improving the individual productivity of a developer was not resulting in a boost to team productivity. That was our hard lesson.”

Sudhakar Sadasivuni, principal group engineering manager, Microsoft Digital

The problem wasn’t the tools, but rather the process around them. The traditional software development lifecycle was built for human handoffs, and every handoff introduces a gap in intent.

“We quickly identified that improving the individual productivity of a developer was not resulting in a boost to team productivity,” says Sudhakar Sadasivuni, a principal group engineering manager in Microsoft Digital, who leads our AI-native engineering initiative. “That was our hard lesson.”

Eventually, our leadership posed the question: What would the lifecycle look like if it were designed from the start as a collaboration between humans and AI agents?

Exploring that concept led us to spec-driven development (SDD), an AI-native engineering approach that treats specifications as the primary artifact in the software development lifecycle. Instead of relying on code, documents, or prompts as the main source of truth, our teams now anchor development around a living specification, which captures the business intent and acceptance criteria before implementation begins.

Our engineers can now generate code faster, accelerate testing, and move from idea to implementation more quickly than ever before, thanks to the power and speed of AI.  

This is a step towards Frontier Firm ways of working, where humans provide oversight and direction and AI tools handle task execution. And because we serve as the company’s Customer Zero, the solution is a model that our customers can learn from and use in their own engineering organizations.

Shifting left

By shifting critical thinking and collaboration to the requirements phase of development, SDD resolves ambiguities, surfaces edge cases, and gets everyone on board early in the process, dramatically reducing late-stage chaos.

“Speed without direction is just expensive chaos—and we learned that the hard way through vibe coding. After burning our hands on those traps, we realized that SDD gives you both velocity and vector.”

Mridul Verma, senior software engineer, Microsoft Digital

The result is a development process that helps teams align stakeholders earlier, reduce rework, and make more effective use of AI coding agents while maintaining a clear connection to business goals.

Another advantage of SDD, according to Mridul Verma, a senior software engineer in Microsoft Digital, is that it focuses not only on velocity, but also on vector. (Velocity is how fast your development moves, while vector reflects the direction you’re moving.)

“Speed without direction is just expensive chaos—and we learned that the hard way through vibe coding,” Verma says. “After burning our hands on those traps, we realized that SDD gives you both velocity and vector.”

Why specs matter more in the AI era

Traditional software development often treats specifications as planning documents. After development begins, these documents can quickly become outdated as requirements evolve and implementation details change.

“In the AI era, the best dev teams aren’t the ones that generate the most code. It’s about how they’re best able to preserve intent.”

Vignesh Vijayaraghavan, senior software engineer, Microsoft Digital

SDD flips that model.

The specification becomes a living artifact that evolves alongside the product, serving as a shared reference point for product managers, software engineers, architects, designers, testers, and AI coding agents.

That shift is especially important in AI-native development environments. While AI tools can generate large amounts of code quickly, they depend on clear instructions and guardrails. Teams that can’t consistently communicate intent typically discover that increased coding velocity simply produces more variability and additional rework.

“In the AI era, the best dev teams aren’t the ones that generate the most code,” says Vignesh Vijayaraghavan, a senior software engineer in Microsoft Digital. “It’s about how they’re best able to preserve intent.”

There are five pillars that drive the transformational value of spec-driven development, based on our experience at Microsoft:

Spec as the source of truth

The spec becomes the single authoritative reference for all stakeholders.

Living, executable artifacts

Specs evolve alongside the project, remaining synchronized with the code and tests. They are co-authored by various stakeholders.

AI-assisted automation

SDD leverages AI coding agents to generate, test, and validate code, accelerating development and enhancing productivity.

Human validation and collaboration

Automation is central to SDD, human expertise is crucial for reviewing specs, refining requirements, and validating outcomes.

Predictability and measurability

SDD enables teams to track progress, measure outcomes, and improve predictability.

How the SDD workflow works

At Microsoft Digital, our developers and PMs begin by creating specifications that capture business goals, user requirements, edge cases, and acceptance tests. These specifications are version-controlled and continuously updated as the project evolves. Rather than serving as static documentation, they guide every major decision throughout the lifecycle.

“The spec becomes the shared source of truth for the entire team. Instead of handing requirements from one group to another, everyone collaborates around a living document that guides development and keeps decisions aligned.”

Garima Gaurav, senior product manager, Microsoft Digital

It’s important to note that before creating the specs, the team agrees on a constitution: A shared set of architectural principles, governance requirements, security standards, and development limitations that everyone adheres to. Developing this constitution is a team-wide exercise rather than an individual one, allowing employees and AI agents to work from the same facts and the same context.

“The spec becomes the shared source of truth for the entire team,” says Garima Gaurav, a senior product manager in Microsoft Digital. “Instead of handing requirements from one group to another, everyone collaborates around a living document that guides development and keeps decisions aligned.”

Our teams use GitHub Spec Kit to support the process. This workflow includes six major stages:

1. Define the business problem and expected outcomes
2. Clarify ambiguities and unanswered questions
3. Create a technical implementation plan
4. Break work into traceable tasks
5. Validate alignment between requirements and plans
6. Implement and test the solution

“As teams mature, they learn the right level of granularity, and breaking large requirements into multiple specs becomes a key practice for maintaining quality and preserving intent.”

Ajeya Kumar, principal software engineer, Microsoft Digital

Because each stage builds on the spec, teams can trace implementation decisions back to their related business requirements more easily than with traditional code-first approaches. AI coding agents enhance the spec and then generate code, tests, documentation, and other tasks from it. (Specs are version-controlled in the same repository to aid tracking and avoid confusion.) Engineers focus on validating intent, reviewing outputs, and refining requirements.

“Effective SDD adoption relies on well-scoped specifications,” says Ajeya Kumar, a principal software engineer in Microsoft Digital. “Smaller, focused specs generate outputs that are easier to review and validate, enabling teams to build incrementally rather than all at once. As teams mature, they learn the right level of granularity, and breaking large requirements into multiple specs becomes a key practice for maintaining quality and preserving intent.”

How SDD impacts different roles

Adopting spec-driven development across our organization involved more than just introducing new tools. Existing roles were tightly coupled to the old software development lifecycle, so some had to change while other new roles and skills were created. Microsoft Digital teams documented these changes at every step, because the shift in mindset is what makes SDD work.

Overall, we found that the biggest change was behavioral: Specifications stopped being documents that simply supported development and actually became the artifact that drove it.

Here’s how Microsoft’s shift toward an SDD-driven approach has impacted various roles across the company:

Leadership

For engineering leaders, success is no longer measured solely by development activity. Leaders need to create an environment that rewards clarity, alignment, and collaboration before actual implementation begins. Dev teams are encouraged to invest time in resolving ambiguities and documenting expectations early in the software development lifecycle because this reduces costly rework later.

Leaders also play an important role in driving adoption. Our dev teams say that SDD delivered the greatest value when it became a standard way of working rather than an optional process that individuals can bypass. The adoption has to be deliberate.

“It’s not something where you decide, today I woke up and I’ll start using SDD,” says Apoorv Gupta, a principal software engineer in Microsoft Digital. “It doesn’t work like that. It’s a mindset shift and a learning curve.”

Developers

For developers, the transition to SDD often requires breaking long-standing work patterns. For years, software engineers were encouraged to move quickly from requirements to implementation. Under SDD, their first step is developing a complete understanding of the problem, including defining success, before any code is created.

“As developers, we get excited and want to jump directly into coding,” Verma says. “Changing that habit was really tricky.”

That change runs deeper than just a habit. The developers must make sure the agent fully understands the task before starting the coding process.

“SDD makes you clarify and refine your thoughts first for agents, not for yourself,” Sadasivuni says. “This is agent-first solutioning, not human-first solutioning.”

Our Microsoft Digital engineers now spend more time refining requirements, identifying edge cases, evaluating plans, and validating AI-generated outputs. Their role has shifted away from producing every line of code to making sure that code implementation accurately reflects intent.

Program and product managers

Our program and product managers continue to perform many of their traditional responsibilities, including stakeholder management, prioritization, and roadmap planning. What’s changed is the degree of ownership that they maintain throughout the development lifecycle.

“In spec-driven development, PMs become the owners of the spec; they’re no longer just responsible for planning and prioritization. Everything starts with the spec, so they become the starting point for the entire system.”

Apoorv Gupta, principal software engineer, Microsoft Digital

In an SDD environment, product managers become stewards of the specification itself. They help define success criteria, resolve ambiguities, document business requirements, and check to make sure that priorities remain visible throughout implementation. The specification becomes a shared contract among stakeholders and a common source of truth for the entire team.

“In spec-driven development, PMs become the owners of the spec; they’re no longer just responsible for planning and prioritization,” Gupta says. “Everything starts with the spec, so they become the starting point for the entire system. If the specs are right, then everything falls into place.”

Architects

Architects establish the guardrails that govern the development process. One of the SDD first activities is defining a constitutionthat captures architectural principles, governance requirements, security standards, and development constraints. Architects are responsible for creating and maintaining those guardrails.

Because architectural decisions are made earlier in the process, teams can identify issues before implementation begins rather than discovering them during code reviews or testing cycles.

Looking ahead

Spec-driven development continues to evolve at Microsoft. But our development teams already view it as a vital component of AI-native engineering. The goal is to create a system that preserves intent as work moves from business requirements to implementation.

“SDD gives PMs a level of empowerment that wasn’t there before. You can move from an idea to a prototype, show it to customers and leaders, and start generating feedback much faster. Once teams experience that, they keep creating value, generating new ideas, and building on what they’ve learned.”

Gopal Panigrahy, principal product manager, Microsoft Digital

As AI coding agents become more capable, clear specifications become more valuable. Teams that can define requirements, establish guardrails, and maintain shared ownership of intent will be better positioned to scale AI-assisted engineering without sacrificing quality or governance.

For Microsoft Digital, SDD represents an important step toward Frontier Firm ways of working, where humans provide direction, judgment, and accountability while AI helps accelerate execution. Preserving intent, not simply generating code, is what enables teams to move faster while continuing to deliver outcomes that meet business needs.

“SDD gives PMs a level of empowerment that wasn’t there before,” says Gopal Panigrahy, a principal product manager in Microsoft Digital. “You can move from an idea to a prototype, show it to customers and leaders, and start generating feedback much faster. Once teams experience that, they keep creating value, generating new ideas, and building on what they’ve learned.”

The post Engineering the Frontier Firm: Sharing our AI-native approach to software development appeared first on Inside Track.

At that scale, Python dependencies have emerged as a significant source of risk for us—representing the third-largest vulnerability surface across the company.

The good news is that we have strong visibility into these vulnerabilities, with tools that continuously detect and surface risks across our codebases. The bad news is that turning those insights into action required a complex remediation process.

Updating a single code package often caused changes across multiple interdependent libraries. This required coordinated updates, validation, and testing to maintain system stability.

“When AI arrived, I saw it as a great opportunity to finally fix a very complex problem we had: The level of entanglement involved in Python code dependencies. A simple script wasn’t going to resolve it—you needed the power of AI.”

Humberto Arias, senior product manager, Microsoft Digital

Multiply this by thousands of projects throughout our enterprise, and vulnerabilities accumulated much faster than we could resolve them. To address this challenge, we turned to AI.

Microsoft Digital—the company’s IT organization—has developed an AI-powered solution called Python Dependency Remediation. Designed to work directly within the developer workflow, this solution analyzes dependency chains, applies required updates, and automatically adjusts the code. This enables our engineers to remediate vulnerabilities quickly and consistently at enterprise scale.

“I’ve worked for years in the vulnerability management space at Microsoft,” says Humberto Arias, a senior product manager in Microsoft Digital. “When AI arrived, I saw it as a great opportunity to finally fix a very complex problem we had: The level of entanglement involved in Python code dependencies. A simple script wasn’t going to resolve it—you needed the power of AI.”

The tool has shown so much promise that we have begun releasing it externally, so that millions of Python developers around the world can take advantage of it.

“I used to have this problem all the time. I upgrade one library, and then I’ve got to upgrade 17 other things, and something else breaks, and now my code is completely different.”

Rich Chiodo, principal software engineer, Python and Tools for AI

Get started

Try using the Python Dependency Remediation tool

Flexibility leads to dependencies and risk

Python is a very flexible language, which is why it’s so popular among software developers. But that same flexible nature—it can be used across a wide range of scenarios—also means it forms deeply interconnected dependency chains. When one code library is updated, it can trigger changes across many others.

“I used to have this problem all the time,” says Rich Chiodo, a principal software engineer on the team responsible for Python Tools and AI. “I upgrade one library, and then I’ve got to upgrade 17 other things, and something else breaks, and now my code is completely different.”

“Developers avoid the upgrades because the dependency web is so complex. This means the vulnerabilities accumulate over time and can become a real security risk.”

Chintan Sheth, principal engineering manager, Viva Glint

Because the code is so interdependent and remediation is time-consuming, many developers skip updating their code packages, which can lead to security vulnerabilities.

Security compliance was often seen as a burden because it slows people down.

“Developers avoid the upgrades because the dependency web is so complex,” says Chintan Sheth, a principal engineering manager on the Viva Glint product team. “This means the vulnerabilities accumulate over time and can become a real security risk.”

“After my manager mentioned it, I reviewed the idea on the hackathon page, and it looked really interesting to me. So I jumped in, and we created a prototype and a demo video with a quick solution. That’s how it started.”

Shiva Krishna Gollapelly, senior software engineer, Microsoft Digital

Hacking our way to a solution

Like some of the best internally developed tools and processes, Python Dependency Remediation came out of a Microsoft hackathon project. These grassroots events allow our engineers to tackle interesting technical challenges in a collaborative, creative way.

“After my manager mentioned it, I reviewed the idea on the hackathon page, and it looked really interesting to me,” says Shiva Krishna Gollapelly, a senior software engineer in Microsoft Digital and the lead developer on the project. “So I jumped in, and we created a prototype and a demo video with a quick solution. That’s how it started.”

The fact that this solution came from a hackathon highlights the ideas-driven culture that we promote at the company.

“This really speaks to our special culture of innovation,” says Snigdha Bora, a principal group engineering manager for Employee Experience. “After this emerged from the hackathon, our developers realized it could solve a problem at scale—that it was worth taking through the full development cycle so we can release it for all of Microsoft, and maybe beyond.”

Solving the issue with one click (and AI)

Because the challenge was not detecting vulnerabilities but fixing them, we had to rethink how we addressed Python dependencies.

“The extension automatically finds the right updates and then fixes the vulnerabilities, so developers don’t need to do the research, the manual upgrades and fixes, run test cases, debugging—all those things that used to take so much time. With our solution, it’s just one button click and it does all of that automatically.”

Shiva Krishna Gollapelly, senior software engineer, Microsoft Digital

In the past, when engineers received a vulnerability notification, they would have to step outside their development workflow and address the issue. What was needed was a solution that could be enacted within their normal workflow—integrating remediation directly into the tools they were already using.

So, we created the Python Dependency Remediation extension for Visual Studio Code, a common Python development environment. Once installed, engineers can address vulnerabilities in the flow of their work.

“The extension automatically finds the right updates and then fixes the vulnerabilities, so developers don’t need to do the research, the manual upgrades and fixes, run test cases, debugging—all those things that used to take so much time,” Gollapelly says. “With our solution, it’s just one button click and it does all of that automatically, with the help of AI.”

The extension uses the APIs built into Visual Studio Code to connect with any AI model the user has access to. (If there is no AI model available, Gollapelly explains, the extension will still make the package updates but won’t do the remediation fixes to the code.) It also produces a report of the changes for the developer to review in case there’s a snag that needs troubleshooting.

“This tool removes a significant burden from our developers,” Bora says. “We are shifting the entire remediation process left, embedding it early in the development workflow. Developers can review the changes and move forward immediately, making the whole process more efficient.”

“We’ve upgraded the library with new methods, calls, and structures. Now, let’s make sure everything works, check for errors in the code, etc. That’s the gap we’re bridging with AI.”

Angel Saldivia, software engineer, SharePoint

The result is that fixes and upgrades that used to take multiple hours of developer time now take minutes, and the code is much more reliable.

What the agent does in this solution is help close that loop, something that the engineer used to have to do.

“We’ve upgraded the library with new methods, calls, and structures,” says Angel Saldivia, a software engineer on the SharePoint product team. “Now, let’s make sure everything works, check for errors in the code, etc. That’s the gap we’re bridging with AI.”

From Customer Zero to global impact

One of the powerful things about working at Microsoft is that you get to help develop technology tools that can change the world. This is the case with Python Dependency Remediation as well.

“We realized this technology had much broader value. There are hundreds of millions of Python users worldwide, so the impact could be massive.”

Snigdha Bora, principal group engineering manager, Employee Experience

As Bora explains, while the solution was being developed it was presented to Guido van Rossum, the creator of Python (and a Microsoft employee). He immediately saw the incredible potential of the concept.

“He suggested that we could take this solution to the world, not just to Microsoft,” Bora says. “We realized this technology had much broader value. There are millions of Python users, so the impact could be massive.”

To help make this happen, Microsoft Digital approached Graham Wheeler, a principal group engineering manager on the Python and Tools for AI team. Wheeler’s team is responsible for shipping Pylance, a development extension for Visual Studio Code used by more than 180 million developers worldwide.

“One of the things we could do was provide a jumping-off point for this extension, so that when users installed Pylance they’d be prompted to also download Python Dependency Remediation. It can help raise awareness, because many users don’t actually do the dependency scanning and updating that they should.”

Graham Wheeler, principal group engineering manager, Python and Tools for AI

Wheeler and his team are in the process of incorporating the Python Dependency Remediation extension as an option during Pylance installation. This will open up a convenient vector for getting the tool in front of a huge audience, potentially revolutionizing Python development.

“One of the things we could do was provide a jumping-off point for this extension, so that when users installed Pylance they’d be prompted to also download Python Dependency Remediation,” Wheeler says. “It can help raise awareness, because so many users don’t actually do the dependency scanning and updates that they should. So, we’re helping with that challenge.”

Beyond Python, the AI-powered technology behind this extension might be applied to other dependency challenges as well. What started as a simple hackathon project could have huge ramifications for the future of software development.

“This solution can easily be adapted to other libraries, other programming languages,” Gollapelly says. “Whether you’re talking about C#, Angular, React, or another language, the concept is the same. The implications are vast.”

The post Taming our Python dependencies at Microsoft with AI appeared first on Inside Track.

Working in Microsoft Digital, the company’s internal IT organization, means being part of a group that operates at massive scale, deploying and managing the technology solutions that enable the company to collaborate, achieve, and fully embrace its shift to a Frontier Firm.

“Being successful in today’s fast-paced environment requires more than technical expertise. Success comes from embracing change, adapting quickly, and continuously learning alongside others. The most impactful teams combine technical capability with curiosity, collaboration, and a mindset of continuous evolution.”

Miguel Uribe, principal PM manager, Microsoft Digital

Our work touches on nearly every aspect of the business, from the network our employees rely on to safely connect to corporate resources, to the productivity apps they rely on every day, to the devices that power our global enterprise. We’re also key to the internal deployment and adoption of agentic AI tools for a global workforce of over 200,000 people.

Microsoft Digital employees have the daily opportunity to tackle complex, real‑world challenges while shaping how Microsoft develops new technologies, serving as Customer Zero for the company’s use of its own products and services.

“Being successful in today’s fast-paced environment requires more than technical expertise,” says Miguel Uribe, a principal PM manager in Microsoft Digital. “Success comes from embracing change, adapting quickly, and continuously learning alongside others. The most impactful teams combine technical capability with curiosity, collaboration, and a mindset of continuous evolution.”

The path to Microsoft Digital

Managing the full breadth of IT responsibilities at an organization the size of Microsoft requires a workforce with a diverse range of perspectives and lived experiences. Accordingly, the people who work here possess a wide variety of backgrounds and skill sets, and hail from around the world.

Networking and relationship-building are often helpful in finding your way into the organization. Mykhailo Sydorchuk, a principal product manager in Microsoft Digital, started his career in Ukraine at a SharePoint solution startup with some prominent global customers. After a successful implementation with one of them, he built a strong relationship with a manager there.

When that person’s company later opened up a SharePoint role, Sydorchuk applied, was hired, and relocated to Los Angeles. He wore many hats in the job, serving as a SharePoint administrator, Microsoft 365 tenant administrator, developer, and project manager for internal IT rollouts and large-scale change management efforts.

Eventually, he was ready for a change.

“My colleague eventually joined Microsoft,” Sydorchuk says. “She felt I would be a good fit and suggested I apply. I went through the interview process and landed the role here about six years ago. So, I got the job very much through networking.”

Some employees at Microsoft Digital have an extensive work history, while others are just getting started in their careers.

“The internship structure is very supportive. Interns are given broad, open‑ended problems rather than tightly scoped tasks, which allows for deeper exploration.”

Jeni Huang, product designer, Microsoft Digital

Internships offer a great opportunity for many candidates who are new to the job market, giving them a way to get a foothold at the company. Microsoft hires thousands of interns each year globally, with year-to-year fluctuations based on hiring conditions and program scope. Within that broader program, design interns are part of a smaller, close-knit cohort, making mentorship and studio connections especially meaningful.

“The internship structure is very supportive,” says Jeni Huang, a product designer in Microsoft Digital who started with the company as an intern in 2022. “Interns are given broad, open‑ended problems rather than tightly scoped tasks, which allows for deeper exploration.”

In Huang’s early work as an intern at the company, she was encouraged to explore more forward-thinking design concepts rather than incremental improvements. That freedom helped her build strong relationships with her manager and others working in the design studio.

“Even though I’m now on a different team, many of the same people remain,” Huang says. “Those connections played a big role in my return as a full‑time Microsoft employee.”

Interesting, impactful work

The people who work at Microsoft Digital routinely tackle ambitious, forward‑thinking projects, with an eye toward reimagining how IT operates at a global scale. Our teams focus on building intelligent, AI‑powered employee experiences, using cloud-native platforms and data-driven insights to simplify work, boost productivity, reduce friction, and help everyone at the company do their best work.

“Microsoft, even after a long and storied history, remains one of the best places for employees to thrive professionally and personally. Experimenting and innovating are at our core—managers are encouraged to provide the time and space for innovation, and to celebrate both successes and learnings.”

Andrew Osten, general manager, business operations and programs, Microsoft Digital

Many of our projects involve large-scale automation, modernizing legacy systems, and embedding responsible AI into everyday workflows, including personalized self‑service technologies, adaptive productivity tools, and predictive insights for decision making. This environment creates a feeling of autonomy for employees and allows them to make significant impact.

“Microsoft, even after a long and storied history, remains one of the best places for employees to thrive professionally and personally,” says Andrew Osten, general manager for business operations and programs in Microsoft Digital. “Experimenting and innovating are at our core—managers are encouraged to provide the time and space for innovation, and to celebrate both successes and learnings.”

Microsoft Digital employees work on front-line technologies that matter. Their efforts serve as living case studies for Microsoft products, testing them in real-world conditions before they reach our customers. The result is a portfolio of work that combines innovation, pragmatism, and long-term thinking.

“We run hackathons sessions like ‘Fix, Hack, Learn,’ where we train ourselves on new technologies and then actively experiment,” Osten says. “That’s one of the most exciting parts of working here: We’re always pushed to explore the latest and greatest technologies and find real value in them.”

The pace can be fast and intense, but it offers the opportunity to work at the cutting edge and be part of transformative software releases. Innovative products result from being given the time and trust to invest and iterate.

“Open-mindedness and flexibility are critical here,” Sydorchuk says. “Technology evolves too quickly to get attached to specific ideas or scopes. Constant change is the norm, and learning to live with uncertainty is essential.”

Customer Zero: Our defining mission

A central component to working in Microsoft Digital is our role as Customer Zero. This concept describes how we use our own products and services internally before releasing them to customers, subjecting them to security, compliance, and productivity demands at an enterprise-level organization.

“Because we deploy these products internally at scale, we learn a tremendous amount, especially since many of these capabilities are early-stage or newly released.”

Andrew Osten, general manager, business operations and programs, Microsoft Digital

This approach surfaces functionality gaps, risks, and usability issues early, turning internal teams into live stress tests for new technologies before they are released to customers. Customer Zero helps ensure our products are resilient, fit for purpose, trustworthy, and grounded in real-world needs, not idealized scenarios. Just as importantly, these practices help create repeatable governance, adoption, and change strategies that customers can reuse, translating internal learning directly into external value.

“Because we deploy these products internally at scale, we learn a tremendous amount, especially since many of these capabilities are early-stage or newly released,” Osten says. “Our role is to generate energy and interest, help teams adopt the tools in ways that deliver real value, and then capture those learnings.”

Customer Zero means that Microsoft Digital functions differently from a typical IT organization, even though we’re still on point for the fundamentals, like keeping the network and its related infrastructure running safely and securely, managing the tenant, providing IT support, driving deployment and adoption, and ensuring our employees have the right tools, devices, and AI-powered services to succeed in a complex global enterprise.

What makes us unique is that we get access to ground-breaking new Microsoft products, features, and capabilities first. We provide early feedback, are the first to try out new experiences, and validate them at enterprise scale.

“We’re often operating at the edge,” Osten says. “For example, I’m currently using early-stage hardware and agentic technologies that haven’t been released yet for general availability, to both provide product feedback and drive value realization as soon as possible. Years ago, through our internal dogfooding program called Elite, I was using a next‑generation Xbox before it launched publicly. Those experiences are part of how we learn about and improve our products.”

Growing AI-based skills

A good example of something truly transformative to emerge from Microsoft Digital recently was our enterprise‑wide deployment and operationalization of Microsoft 365 Copilot—acting as Customer Zero for generative AI technology at scale.

Rather than treating Copilot as a productivity add‑on, we led a full reinvention of how knowledge work happens at the enterprise level. Building everything from governance and data-hygiene standards to role‑based adoption models and change management playbooks, we went all out to change employee habits and safely embed AI into daily workflows across the company.

“AI is behavioral,” Osten says. “To get real value, we work closely with business units to understand the problems they’re trying to solve, map those processes, identify where people can focus on higher-value work, and then build and drive adoption of agents to support that shift.”

In essence, Microsoft Digital is engaged in building an entire business model with AI serving as a governed, trusted, role-aware layer of intelligence. The company refers to this as the Frontier Firm concept, combining human judgment with AI agents—tools that can reason, plan, and execute tasks across systems.

“Building agents just because we can isn’t the goal. The goal is value. Microsoft Digital plays a key role in identifying the right problems, ensuring the right tools are available, and scaling solutions responsibly, so we’re solving problems while not creating new ones.”

Aisha Hasan, principal product manager, Microsoft Digital

The work Microsoft Digital does to conceive, build, and incorporate agents falls under a company-wide initiative known as Microsoft Agent 365. It focuses on three broad questions:

• What problems are we trying to solve?
• How can we build AI agents and workflows to solve them?
• How do we manage and scale this work without creating sprawl or duplicative solutions?

“Building agents just because we can isn’t the goal,” says Aisha Hasan, a principal product manager in Microsoft Digital. “The goal is value. Microsoft Digital plays a key role in identifying the right problems, ensuring the right tools are available, and scaling solutions responsibly, so we’re solving problems while not creating new ones.”

Prospering in Microsoft Digital

In addition to the central role they play as Customer Zero and the opportunity to engage closely with agentic AI, Microsoft Digital employees also benefit from a wide range of opportunities that go beyond technical skills. Rather than limiting our roles within narrow job definitions, we focus on a more holistic career experience that supports pursuing growth opportunities across Microsoft.

“We invest in growth, exposure, innovation, and collaboration in a way that makes the work both challenging and fulfilling,” Osten says.

Employees at Microsoft Digital use traits like curiosity, empathy, and adaptability to thrive within a fast-moving technical landscape. Being curious leads to learning, learning enables adaptation, and empathy pulls it all together, helping people grow as they collectively manage challenges.

“Technology is evolving so fast that keeping up with everything is a challenge in itself,” Hasan says. “Empathy, for yourself and others, matters when everyone is navigating constant change.”

It’s common for employees to leverage a range of responsibilities both within and between different jobs. Open-mindedness and flexibility are critical. Technology evolves too quickly to get attached to specific ideas or job scopes.

“I began in engineering and operations, moved into network engineering, and then gradually ‘peeled back the onion’ by stepping into technical program management,” Hasan says. “That allowed me to see the end-to-end picture: business value, technology, end users, adoption, and long-term maintenance.”

To be successful at Microsoft Digital, technical skills are important, but what really matters is the ability to innovate and work through uncertainty.

“I look for people who thrive in ambiguity, who enjoy taking on new challenges rather than waiting for perfect clarity,” Osten says. “Collaboration is equally important. In an environment this dynamic, you may be accountable for an outcome, but your success depends on the work of many other teams.”

How Microsoft values drive our work

No description of what it’s like to work at Microsoft Digital is complete without a discussion of the principles that fuel us, both at the department level and for the company as a whole.

“It often feels like drinking from a firehose, in terms of the volume of information one needs to process. It’s high-intensity, but being able to work at the cutting edge and be a part of major technological transformation that empowers everyone on the planet to achieve more makes it totally worth it.”

Mykhailo Sydorchuk, principal product manager, Microsoft Digital

Here are four core Microsoft Digital value pillars, as Osten describes them:

1. People development and skilling. This includes technical skills—including around emerging technologies like agentic AI—as well as people skills. We focus on stakeholder management, storytelling, and career development skills that support long‑term employee growth.
2. Leadership and manager development. We continually build leadership capability through a growth mindset, reinforcing principles like creating clarity, generating positive energy, and driving success. We invest heavily in helping both current and future leaders build “model‑coach‑care” skills.

3. Connection and collaboration. We intentionally create opportunities for teams to understand one another’s dependencies, whether through global meetings or structured collaboration initiatives. It’s easy to become siloed in a large enterprise, and these connections are critical, especially as AI continues to blur traditional boundaries.
4. Inclusion. This means being inclusive across communities, geographies, languages, cultures, and work environments. We focus on how we meet, how remote participation works, and how to ensure everyone can contribute effectively, regardless of location or role.

Following our pillars, and being benchmark examples of Microsoft’s value model, contributes to the success of Microsoft Digital and enables our employees to thrive  working at one of the world’s most prominent tech companies.

“Microsoft is a fast-paced environment, primarily due to scale and constant innovation,” Sydorchuk says. “It often feels like drinking from a firehose, in terms of the volume of information one needs to process. It’s high-intensity, but being able to work at the cutting edge and be a part of major technological transformation that empowers everyone on the planet to achieve more makes it totally worth it.”

Key takeaways

Here are five keys to employee success at Microsoft Digital, which can be applied to any IT organization:

• To get a foot in the door, be resourceful. Microsoft Digital employees find their way into the company through a variety of channels, including personal networking, internships, vendor relationships, and Microsoft external and internal career sites.
• Embracing Customer Zero is crucial. The concept of using Microsoft employees as early adopters of new products and services is a strategic cornerstone and an essential aspect of how the company operates.
• Understand what it means to be a Frontier Firm. Orienting your approach to work in a way that corresponds with the benefits of agentic AI can help you align with Microsoft Digital’s journey, as we become a lighthouse example of a Frontier Firm for other IT organizations.
• Develop your curiosity, empathy, and versatility. Technical skills are valuable, but continuous learning and softer skills are foundational to professional and personal growth and success.
• Know your organization’s core values. Collaboration, connection, and inclusion are vital tenets for succeeding at Microsoft Digital, as reflected in the organization’s values.

Try it out

Empower your future with an IT career at Microsoft.

Related links

• Learn more about our company’s mission, values, and products.
• Check out the main Microsoft Careers page.
• Read about Olutunde Makinde’s journey to Microsoft.
• Learn about Anahit Hovhannisyan’s impact on IT at Microsoft.
• See how Microsoft Digital is transforming IT at the company.
• Discover how Microsoft Digital leverages Customer Zero to drive AI innovation.
• Understand more about how agentic AI is core to becoming a Frontier Firm with this deployment readiness guide.

The post IT on the cutting edge: Working in Microsoft Digital in the era of AI appeared first on Inside Track.

Yonatan Zunger, CVP and deputy CISO for Microsoft, suggests focusing exclusively on hardening a piece of software to security threats may make it difficult to use and introduce a new risk when users get frustrated and try to bypass controls. This is why engineers need to consider not just individual components but how they work together to maintain productivity.

“Think of every system as a socio-technical system containing many parts, and all of them working together in unison have to be secured,” Zunger says.

Learn from our experience 

Read our practical advice about applying security fundamentals to AI.

Try it out

Read more from Zunger about applying security fundamentals to AI.

Related links

• Explore more from Zunger about how to deploy AI safely.
• Discover what you need to know about governing autonomous agents.
• Learn about aligning user, developer role, and organizational intent in agent governance.
• Find out how to use Authorization Fabric to govern AI agents at scale.
• Read about agent abuse patterns.
• Identify ways to secure AI agents with Azure AI Foundry.
• Explore how Data Security Posture Management for AI can prevent runtime risks.

The post Microsoft CISO advice: Apply engineering fundamentals to securing AI appeared first on Inside Track.

Microsoft Azure DevOps (ADO) is our company’s end-to-end software development lifecycle (SDLC) solution for planning, coding, testing, and delivery. It combines tools for work tracking, source control, pipelines, and artifacts so teams can manage the entire SDLC in one environment.

Although ADO excels at streamlining the development process, we found that users were still spending significant time performing repetitive administrative tasks, like creating and breaking down work items, writing and managing queries for reporting, and reclaiming lost permissions.

Our Engineering Systems Platform team successfully embedded AI into ADO, resulting in ADO experiences that replace manual workflows and free up our IT professionals to concentrate on work that makes a real impact.

Identifying the opportunity

The Engineering Systems Platform team supports 15,000 active users across one of the largest ADO platforms at Microsoft.

“We saw the toll these processes took on users, whether they were compiling information or performing manual tasks. Even with automation, there was still an opportunity to give time back to engineers.”

Gopal Panigrahy, principal product manager, Microsoft Digital

Three years ago, the team began exploring opportunities to automate repetitive ADO tasks like creating and updating work items, navigating project data, gathering statuses, and breaking large initiatives into sprint-ready work.

While they found ways to automate some of these tasks, they discovered decision-making and information synthesis still consumed valuable time and occasionally introduced some human errors.

“We saw the toll these processes took on users, whether they were compiling information or performing manual tasks,” says Gopal Panigrahy, a principal product manager in Microsoft Digital. “Even with automation, there was still an opportunity to give time back to engineers.”

Adding AI to ADO workflows

ADO spans a vast area at Microsoft, serving a wide range of enterprise use cases and personas. What these workers have in common is heavy workloads. With this in mind, different categories of ADO users expressed the desire for AI-powered experiences that could help streamline workflows and speed up day-to-day development tasks.

As generative AI matured, our team explored whether they could embed AI technology inside ADO to act as a real-time assistant, handling administrative work and answering contextual questions using natural language.

“We saw it as a win-win experiment. If we could give engineers back in ADO, they could spend it building, not managing artifacts.”

Debashis Sahoo, principal group engineering manager, Microsoft Digital

The guiding principles of the experiment were simple: Stay in context and preserve user control while aligning with existing ADO permissions and processes.

That vision led to the creation of two complementary Microsoft Copilot agents: The DevOps Assistant and the AI Work Item Assistant.

“We saw it as a win-win experiment,” says Debashis Sahoo, a principal group engineering manager in Microsoft Digital. “If we could give engineers time back in ADO, they could spend it building, not managing artifacts.”

What makes this initiative distinctive is it brings AI closer to the core ADO product and its users. It allows for secure, confidential, and context-rich ADO data to be used safely for meaningful AI-powered experiences.

DevOps Assistant offers conversational, in-context support

DevOps Assistant is a chat‑based experience present in the ADO user interface (UI). It’s activated in a side panel where users can ask natural language questions to retrieve information, check project statuses, and run common DevOps actions without navigating away from their main ADO display.

The DevOps Assistant enables cross-source discovery, which reduces context switching and discovery time and helps lower the cognitive load for engineers and product managers. By reducing the time it takes to switch contexts and search for information, the DevOps Assistant helps ADO users move faster and stay focused on product delivery.

Under the hood, the DevOps Assistant is a constellation of specialized agents, each of which is focused on a different segment of the DevOps lifecycle:

• Work Item Agent creates, refines, and scopes work into sprint-ready backlogs
• Knowledge Board Agent surfaces the right DevOps knowledge at the right moment
• Permission Agent handles access and permission requests
• Bulk Complete Agent runs repetitive, large-scale updates
• Sprint Board Agent summarizes sprint status and provides instant, prompt‑driven insights

“We didn’t just build a chatbot. We built a distributed system of agents that understands the intent of the DevOps user and acts on it securely and in context.”

Apoorv Gupta, principal software engineer, Microsoft Digital

Agents are built in Copilot Studio and coordinated by Orchestrator Agent, Copilot Studio’s front door.

For example, if a user asks to create or refine work items, the Orchestrator Agent routes the request to the Work Item Agent to handle. If the question is about permissions, then it delegates the work to the Permission Agent. It does this for each task.

“We didn’t just build a chatbot,” says Apoorv Gupta, a principal software engineer in Microsoft Digital. “We built a distributed system of agents that understands the intent of DevOps user and acts on it securely and in context.”

At present, the DevOps Assistant is available across all our internal ADO environments at Microsoft. The plan is to make it available to external customers soon.

AI Work Item Assistant provides inline assistance

The AI Work Item Assistant is a real-time embedded experience within ADO work items. Powered by Microsoft Foundry, it helps users create and refine work items using context and business requirements.

The assistant works immersively, keeping users focused and within ADO as they structure work items or generate child items from the parent.

For product and program managers who start with high‑level ideas, the assistant understands intent. It can automatically suggest logical, sprint‑ready breakdowns, helping to dramatically reduce the time spent on planning, sorting, and prioritizing work items.

Turning newfound time into innovation

The key to reclaiming time for your workforce isn’t just the introduction of new AI-driven features. It’s using the technology to enforce structure and quality at the beginning, so that everything downstream moves faster.

Panigrahy describes the practice as three reinforcing feedback loops.

The first loop is upstream quality amplification. AI agents help consistently structure work items with clear acceptance criteria and templates. The structure then feeds other tools (such as GitHub Copilot), allowing them to generate higher-quality code and more predictable outcomes—shortening the overall software development lifecycle.

The second feedback loop is acceleration of execution. In a typical sprint planning session, a team of eight engineers might:

• Take an hour (or more) to manually break user stories into more than 100 tasks
• Create different tasks in their own style, introducing inconsistency and ambiguity
• Generate uneven details, then spend time clarifying data later

With DevOps Assistant and AI Work Item Assistant, that same task breakdown turns into a prompt-driven action that no longer requires hours of work.

“It burns a lot of time for everyone to manually create each item in their own way, making sure they’re using the correct inputs from the product manager and confirming they aren’t missing anything,” Panigrahy says. “Now, with AI magic, it takes less than three minutes.”

The third feedback loop is capacity reinvestment. Instead of spending hours on tactical DevOps mechanics, teams can now spend more time on engineering judgment, resulting in better estimation, technical decisions, and design. They can use these reclaimed hours to learn new tools, experiment with new agents, and innovate on the SDLC.

“Capacity saving keeps giving back, in a loop,” Gupta says. “You get more capacity back. You innovate. You learn. You do better.”

What’s next on the AI-in-ADO journey

The DevOps Assistant and the AI Work Item Assistant can help change user behavior, shifting from time spent doing tactical DevOps tasks to performing higher‑value, judgment-based work. These tools can help teams increase work quality and reduce wasted time.

“Our next chapter is about making AI smarter, more action-oriented, and truly agentic,” Sahoo says. “The goal is to reduce cognitive load and allow the experience to live wherever users are—from Azure DevOps to Microsoft Teams and Microsoft 365—so the agent works seamlessly across their workflow.”

AI-driven productivity gains are arguably the biggest opportunity in the industry. It’s fundamentally redefining the engineering experience at an unprecedented pace.

“While we’ve made huge strides embedding AI into the everyday Azure DevOps experience, it still feels like we’re just getting started,” Sahoo says. “Staying relevant means continuously evolving to deliver ever-greater value and efficiency to engineers.”

Key takeaways

Keep these tips in mind as you get started on your own journey with AI and Microsoft ADO:

• Treat AI as a strategic accelerator, not as an add-on. Identify where your engineering process can use AI to move from simple assistance to transforming your workflows.
• Target high-effort, high-volume tasks first. Analyze where your teams are spending significant manual time, even if AI tools are already in place in those workflows.
• Validate productivity with measurable data, not intuition. Track time reclaimed, workflow efficiency, reduction in manual steps, and user satisfaction. Tangible data can help your initiative earn trust and justify the expansion of AI tool use on your team.

Try it out

Get started using the AI Work Item Assistant in Azure DevOps.

Related links

• Discover how we’re powering the new age of AI-powered engineering in Microsoft IT.
• Learn how to build and manage agents in Copilot Studio.
• Read about ways we’re transforming our enterprise IT operations at Microsoft using AI.
• Check out our IT playbook for becoming a Frontier Firm in the era of AI.

The post Reclaiming engineering time with AI in Azure DevOps at Microsoft appeared first on Inside Track.

To accomplish these huge tasks, it’s essential to cultivate a range of perspectives, expertise, and lived experiences.

Olutunde Makinde is an example of this.

“A friend once laughed at me back in college when I said I wanted to work at Microsoft, like it was impossible. But I knew I could achieve the impossible if I could just be focused. I never gave up.”

Olutunde Makinde, senior service engineer, Microsoft Digital

Makinde, a senior service engineer in Microsoft Digital, came to the company the long way around—roughly 7,000 miles away from the Redmond, Washington, headquarters, in fact. He’s originally from Lagos, Nigeria.

As a global organization, Microsoft builds teams where people with different experiences and life journeys actively influence how products, services, and internal platforms are designed. Makinde, commonly known around the office as “Tunde” (“rhymes with Sunday,” he notes), embodies that diverse approach, bringing his unique insights and experiences to critical work at the company.

“A friend once laughed at me back in college when I said I wanted to work at Microsoft, like it was impossible,” Makinde says. “But I knew I could achieve the impossible if I could just be focused. I never gave up.”

Launching an IT career in Nigeria

Makinde’s journey to Microsoft began with earning a degree in computer engineering in Lagos, after which he found work as a network engineer. He spent the next several years developing his skills through certifications and other learning opportunities.

“I did a lot of self-paced training, learning how to configure Cisco routers. Eventually I became a Cisco-certified network professional (CCNP),” Makinde says. “Around that time, I had a friend who was preparing for Windows Server 2008 certifications, and through his study materials I started learning more about Microsoft and its products.”

Makinde’s first direct encounter with Microsoft came in 2014, when the company he worked for received a contract to deploy the first Microsoft Azure cloud installation in Nigeria.  

“I spent the last day of 2014 and the first day of 2015 at the customer site, figuring out how to connect their on-premises network to Azure,” Makinde says. “It had never been done before in Nigeria, and taking up that challenge really propelled me into the world of Microsoft-specific technology.”

From there, Makinde set his sights on a career at Microsoft. He parlayed his initial exposure to cloud architecture into a focus on Azure, as well as Amazon Web Services. After spending some time in the United Kingdom, he achieved his goal when he was hired by the Microsoft Digital team in 2022. He moved to the United States in 2025.

He credits support from his family, especially his wife, with helping him achieve his dreams.

“My wife was a pillar of support through every career transition, from Nigeria to the UK to the United States,” Makinde says. “She believed in me when I faced rejections, celebrated with me when I finally got the offer, and now keeps me grounded whenever work gets intense. I couldn’t have made this journey without her.”

Making an impact from day one

Kathren Korsky, a principal technical program manager in Microsoft Digital and Makinde’s hiring manager, remembers the impression he made right away. It was clear that Makinde’s experience and technical background were major assets.

“What caught my attention was how well-prepared he was for the conversation and how well he communicated,” Korsky says. “The stories he shared about his work with Azure deployment in Nigeria really drew my interest. But I was also intrigued by how he was able to bridge technology with the business world, working with different banks across the continent to gather requirements, understand them, and build solutions.”

Upon being hired at Microsoft, he initially worked remotely from the UK on a Redmond-based device and application management team. The team was looking to deploy Cloud PC internally and needed a system in which employees could request access and get approvals to use Cloud PCs.

“He was able to stand up a full Power Automate workflow within a short period, and with a very high degree of quality,” Korsky says. “Rarely did anyone find any defects or bugs in his system.”

Makinde’s designs drove value moving forward as well, as the team made updates to his initial workflows.

“His design was so strong that we were basically able to follow exactly what he had created in Power Platform and build that exact same design in ServiceNow. It really expedited that whole process.”

Kathren Korsky, principal technical program manager, Microsoft Digital

ServiceNow was more commonly used for systems that involved access requests and approvals, but when a platform update from Power Automate was initiated the team found Makinde’s original design was durable enough to weather the shift.

“His design was so strong that we were basically able to follow exactly what he had created in Power Platform and build that exact same design in ServiceNow,” Korsky says. “It really expedited that whole process.”

Driving efficiency and managing change

Since moving to the United States to work at company headquarters, Makinde has continued to push important projects forward—working with different stakeholders to deploy policy changes across Microsoft, managing the Change Advisory Board (CAB) intake process, and driving configuration updates for security and first-party product deployments.

“There’s a lot of diligence required to see the edge cases happening, to pay attention to them, and to watch out for potential problems. Tunde stops rollouts regularly to flag potential defects or risks, which prevents issues from interrupting our work and reducing productivity.”

Jeff Duncan, principal service engineering manager, Microsoft Digital

Makinde learned how to assess change requests and understand risk profiles, as well as enforce best practices for managing change within the security environment. Within about a year, he was able to take the lead in the space and own the deployment process.

A single misconfigured policy can cause major disruption. Makinde’s role puts him in position to be the checkpoint that prevents incidents before they happen.

“There’s a lot of diligence required to see the edge cases happening, to pay attention to them, and to watch out for potential problems,” says Jeff Duncan, principal service engineering manager in Microsoft Digital and Makinde’s manager. “Tunde stops rollouts regularly to flag potential defects or risks, which prevents issues from interrupting our work and reducing productivity.”

Softer skills like transparency, collaboration, and clear communication across levels and teams are key aspects of Makinde’s work as well.

“Tunde is thoughtful and detail-oriented, and he’s very good at explaining the decision-making process when he provides overviews for leadership,” Duncan says. “There’s rational, logical reasoning behind the decisions he makes.”

Makinde has implemented new efficiencies in how he manages the CAB and deployment service using AI. This includes CABBIE—an AI-powered agent that automates CAB communications. For Intune deployments, he uses AI to streamline deployment coordination and package reviews. These innovations reflect our Customer Zero approach to AI adoption here in Microsoft Digital.

“We run weekly CAB meetings to review change requests. That comes with a lot of communication work — status updates, follow-ups, coordination with stakeholders. It was all manual,” Makinde says. “CABBIE pulls the data from Azure DevOps, generates the emails, updates requests, and logs approvals automatically. It saves time and reduces errors.”

Success at Microsoft Digital: Aptitude and curiosity

As the organization at the center of the company’s own digital transformation, we in Microsoft Digital function as a living showcase of what’s possible with Microsoft technology. Our team tests new capabilities at enterprise scale as Customer Zero for Microsoft, identifying gaps and providing insights to ensure our customers benefit from what we’ve learned.

Because the impact of Microsoft Digital extends far beyond internal systems, team members have to set the standard for digital excellence. They must demonstrate what enterprise transformation looks like in practice and empower customers with the confidence to pursue their own modernization journeys.

 Hiring talented people like Makinde is essential to this mission.

“There are three core traits I look for when hiring—aptitude, attitude, and curiosity,” Korsky says. “Aptitude is not only what you currently know, but your propensity and desire to learn and grow those skills. Attitude goes hand in hand with that—are you willing to demonstrate grit and perseverance? And then curiosity, because so much of what we do from an innovation perspective requires a willingness to challenge assumptions and think of completely new ways of doing things.”

Makinde’s journey here at Microsoft Digital embodies and illustrates the company’s larger story: how technical expertise, innovative thinking, and a commitment to continuous learning combine to deliver world-class results.

“I’m now up to 25 certifications, and I continue to learn how to do more at Microsoft to positively impact the organization and protect our employees’ experience across applications and devices.”

Olutunde Makinde, senior service engineer, Microsoft Digital

That attitude of persistent curiosity and the willingness to keep learning continue to fuel Makinde’s experience at Microsoft. 

“Self-improvement is a way of life for me that has driven my career forward,” Makinde says. “At an early stage in my career, I did a lot of self-training—from learning how to configure Cisco routers and switches, to migrating on-premises workloads to Azure and managing cloud resources. I’m now up to 25 certifications, and I continue to learn how to do more at Microsoft to positively impact the organization and protect our employees’ experience across applications and devices.”

Key takeaways

Olutunde Makinde’s career experience here in Microsoft Digital offers some important insights that you can apply to your own organizational development:

• AI adoption starts with practical problems. Makinde’s use of AI to streamline CAB communications and deployment coordination shows how Customer Zero teams find real-world applications for emerging technology.
• Different experiences and perspectives contribute to business success. Achieving ambitious goals as an organization is dependent upon attracting talented people like Makinde from a range of backgrounds, disciplines, and lived experiences.
• Strong technical skills paired with innovative thinking drives value. Makinde’s contributions to flexible cloud deployment workflows are an example of how this combination pays dividends.
• Proactive risk management and attention to detail can prevent large-scale disruptions. By being willing to stop rollouts and flag risks before they become problems, Makinde’s approach to his work exemplifies how thoughtful decision-making safeguards productivity and security.
• Persistence, curiosity, and continuous learning are critical career accelerators. Having a long and successful career at a company like Microsoft goes beyond just technical aptitude; it also requires perseverance and a passion for learning. Makinde’s self-driven training efforts and his refusal to give up have enabled him to achieve what once seemed impossible.

Try it out

Learn more about working in IT at Microsoft by reading our Inside Track blog,

Related links

• Considering a career at Microsoft? See current openings at the Microsoft Careers portal.
• Read about a day in the life of a Microsoft employee using Copilot.
• Discover how Microsoft employees use the Total Rewards Portal to understand their value.
• Learn how Microsoft HR is using Viva and Microsoft 365 Copilot to empower our employees.
• Find out how the Microsoft Power Platform at Microsoft empowers employees.
• Connect with regional employees more effectively at Microsoft with Viva Amplify.

The post Olutunde Makinde: From Lagos to Redmond, a Microsoft IT engineer’s journey appeared first on Inside Track.

There was excitement, curiosity, skepticism, and no shortage of questions about what this technology meant for the future of IT.

At Microsoft Digital—the company’s IT organization—we didn’t start with a grand transformation plan. Instead, we started with a realization: AI wasn’t just another tool to roll out. It was a fundamental shift in how engineering work could happen.

For years, our IT teams have been focused on scale, reliability, and operational excellence. Those priorities didn’t change. What changed were the possibilities.

Suddenly, engineers could draft code in seconds, summarize complex systems instantly, or automate work that had once consumed hours or days. It was an opportunity to take the skills and capabilities of our people and amplify them with AI.

That realization forced us to step back and ask harder questions.

How do you help thousands of engineers understand what AI can actually do to impact their day-to-day work? How do you move from experimentation to trust? And how do you adopt AI in a way that strengthens engineering fundamentals instead of eroding them?

The answer came in the form of a phased journey grounded in people, culture, and continuous learning.

Phase 1: Awareness and access

It might sound surprising when speaking about engineering processes, but our first challenge wasn’t technology; it was understanding.

When generative AI entered the conversation, most engineers saw the headlines and dabbled in various tools, but few understood fully what it meant for their work. Some were excited, others were wary. Many simply didn’t know where to start. That gap between awareness and practical value was the first barrier we had to address.

We realized early that top-down mandates wouldn’t work. Telling engineers to “use AI” without context or relevance would only deepen skepticism. Instead, we focused on something both simpler and more difficult: Exposure.

We started by making AI visible and accessible in the tools engineers already used. GitHub Copilot. Microsoft 365 Copilot. Early copilots embedded directly into engineering workflows. The goal wasn’t immediate productivity gains. It was familiarity. Letting engineers see, firsthand, what AI could and couldn’t do.

“We encouraged tool usage and adoption so people would at least play around with AI. And once they did, they started seeing the value. That’s when the mindset shifted from ‘AI might replace me’ to ‘AI can be my companion.’”

Mukul Singhal, partner group engineering manager, Microsoft Digital

Just as important, we talked openly about limitations.

AI wasn’t perfect. It hallucinated. It made confident mistakes. And that honesty mattered. By framing AI as an assistant, we reinforced the role of engineering judgment. Engineers didn’t need to fear losing control. They needed to understand how to stay in control.

We also made experimentation safe.

No quotas. No forced adoption metrics. Engineers were encouraged to try AI on low‑risk tasks: summarizing documentation, generating test cases, or exploring unfamiliar codebases. Small wins built confidence, confidence built curiosity, and curiosity drove organic adoption.

As that experimentation took hold, the mindset began to shift.

“We encouraged tool usage and adoption so people would at least play around with AI,” says Mukul Singhal, a partner group engineering manager in Microsoft Digital. “And once they did, they started seeing the value. That’s when the mindset shifted from ‘AI might replace me’ to ‘AI can be my companion.’”

Over time, conversations changed from ‘Should we use AI?’ to ‘Where does AI help most?’

Engineers began sharing prompts, tips, and lessons learned with one another. What started as individual exploration turned into community learning. Awareness gave way to momentum.

Phase one was about providing access to explore, to question, and to learn. And that foundation made everything that followed possible.

Phase 2: Culture shift

Access created awareness and awareness created curiosity.

As more engineers began experimenting with AI, we noticed a pattern. Some teams were moving faster, learning faster, and reducing friction in their day‑to‑day work. Others stalled after initial trials. The difference wasn’t technical skill or capability, it was mindset.

“People started shifting from the mindset of ‘Will AI work?’ to ‘AI is working for me.’ I think that was a very transformational shift, to where I believe a lot of engineers in the organization started believing in AI.”

Veera Mamilla, principal group engineering manager, Microsoft Digital

To move forward, we had to shift how AI was perceived from something optional or experimental to something that was simply part of how modern engineering gets done.

That meant normalizing AI as a trusted partner in the engineering process.

Leaders played a critical role in that shift. Rather than positioning AI as a productivity shortcut, they framed it as a way to strengthen engineering fundamentals: clearer design discussions, better documentation, faster feedback loops, and more time for deep problem‑solving. The message was intentional and consistent. Using AI wasn’t about cutting corners, it was about reimagining how work gets done.

We also had to address a fear that surfaced early: that AI adoption was a signal of replacement rather than empowerment.

“People started shifting from the mindset of ‘Will AI work?’ to ‘AI is working for me,’” says Veera Mamilla, a principal group engineering manager in Microsoft Digital. “I think that was a very transformational shift, to where I believe a lot of engineers in the organization started believing in AI.”

That framing mattered.

As engineers incorporated AI into their workflows, success stopped being measured by output alone. The focus shifted to outcomes. Did AI help you understand a system faster? Did it surface risks earlier? Did it free up time to focus on higher‑value work?

Over time, AI stopped feeling like a novelty. It became part of the engineering fabric. We reinforced it through leadership modeling, peer learning, and shared success stories. Teams no longer asked whether AI belonged in their workflows. They asked how to use it responsibly and effectively.

Phase 3: Upskilling and role evolution

Once AI moved from curiosity to expectation, the challenge of skill building became unavoidable.

From the start, we made a deliberate choice: This would be an upskilling and reskilling journey, not a wholesale replacement of roles. The goal wasn’t a new workforce. It was an investment in the one we had.

That decision shaped everything that followed.

Early upskilling efforts focused on practical entry points. Prompt engineering. Tool literacy. Understanding how copilots and early agents behaved in real engineering workflows. We treated these as something every engineer needed to experiment with, regardless of discipline.

But it quickly became clear that skills alone weren’t the full story. Roles themselves were starting to evolve.

“Your title might still be software engineer or principal engineer. But if you’re acting like an AI engineer, what does that actually mean? That question helped us start defining how these roles were evolving.”

Ragini Singh, partner group engineering manager, Microsoft Digital

Across software development, service engineering, and cloud network engineering, the work was shifting from manual execution toward orchestration and oversight. Engineers were no longer expected to do every task end‑to‑end by hand. Instead, they were learning how to guide AI, review its output, and decide where automation made sense and where it didn’t.

As part of this shift, we began researching how the industry itself was redefining engineering roles. Leaders examined emerging job descriptions from across the market and compared them with Microsoft’s own role frameworks. At the time, there was no formal “AI engineer” role in the internal job library. Rather than creating a new title, the focus stayed on evolving expectations within existing roles.

The idea of an “AI‑native engineer” emerged not as a job description, but as a mindset.

An AI‑native engineer still understands systems, architecture, and risk. What’s different is how that expertise gets applied. Routine tasks are delegated to AI. Judgment, design, and accountability stay with the human. Engineers move from doing all the work themselves to supervising work done in partnership with AI.

“Your title might still be software engineer or principal engineer,” says Ragini Singh, a partner group engineering manager in Microsoft Digital. “But if you’re acting like an AI engineer, what does that actually mean? That question helped us start defining how these roles were evolving.”

This evolution looked different across disciplines. Software engineers focused on AI‑assisted coding, test generation, and spec‑driven development. Service engineers leaned into AI for incident response, knowledge capture, and operational decision support. Cloud network engineers began moving from manual intervention toward intelligent orchestration and agent‑assisted troubleshooting. The common thread wasn’t identical tooling, it was a shared shift toward higher‑order work and reduced toil.

Phase 4: Embedding AI across the engineering lifecycle

By this phase, we knew individual productivity gains were simply the starting point for larger and broader benefits.

Early on, most AI usage showed up in familiar places: Code suggestions, documentation summaries, quick answers. Useful, but fragmented. The bigger opportunity emerged when we stepped back and asked a harder question: What would it look like if AI were embedded across the entire engineering lifecycle, not just used at isolated moments?

We stopped thinking in terms of tools and started thinking in terms of flow. Design. Build. Test. Deploy. Operate. Improve. AI needed to show up across all of it, in ways that reinforced how engineers already worked.

“If AI is only showing up at one step, you don’t get the full value. The real impact comes when it’s integrated across the lifecycle, where engineers can design, build, operate, and learn faster as a system.”

Sudhakar Sadasivuni, principal group engineering manager, Microsoft Digital

In software engineering, that meant pulling AI earlier into the process. We began using it to help draft requirements, reason through design options, and review code with broader system context to accelerate how quickly we could get to informed decisions. Coding assistance mattered, but it was no longer the center of gravity.

Testing and quality followed a similar pattern. AI supported test generation, defect analysis, and code review, reducing repetitive effort and helping issues surface sooner. That gave engineers more time to focus on quality and architecture instead of cleanup.

In service engineering, we embedded AI into incident management and operational workflows. Engineers used it to summarize incidents, surface relevant knowledge, and analyze signals across systems. In cloud network engineering, AI helped shift work away from manual intervention toward orchestration and intelligent troubleshooting. Across disciplines, the principle stayed the same: AI should reduce friction, not introduce it.

As we scaled this approach, one thing became clear. Embedding AI wasn’t just a technical exercise. It was a systems change.

“If AI is only showing up at one step, you don’t get the full value,” says Sudhakar Sadasivuni, a principal group engineering manager in Microsoft Digital. “The real impact comes when it’s integrated across the lifecycle, where engineers can design, build, operate, and learn faster as a system.”

As AI became part of core workflows, engineers remained accountable for outcomes. AI output was reviewed, tested, and validated like any other engineering input. Embedding AI didn’t lower the bar for rigor. It raised expectations around judgment, oversight, and data quality. We became more deliberate about responsibility and governance.

Over time, these integrations created compound benefits.

Faster design cycles reduced downstream rework. Better testing lowered operational noise. Improved operational insight shortened recovery times. AI stopped being something we used occasionally and became something the engineering system itself was built around.

Phase 5: Eliminating toil and accelerating outcomes

At some point, every AI story hits the same test. Does it actually make engineers’ days better? For us, that proof showed up fastest in elimination of toil.

Across Microsoft Digital, engineers have always spent time on work that was necessary but draining. It included tasks such as manual troubleshooting, repetitive diagnostics, log analysis, and routine operational tasks that kept systems running but didn’t move the organization forward.

AI gave us a chance to change that.

“Toil reduction is the biggest thing. That’s where engineers’ eyes light up. If we can eliminate toil, people engineers will flock to use AI. I really believe it.”

Beth Garrison, principal cloud network engineer, Microsoft Digital

In cloud network engineering, for example, troubleshooting used to require manually reconstructing what happened, such as logging into devices, chasing configurations, and piecing together context after the fact. As we began introducing agents and machine learning into these workflows, that work shifted. Instead of spending time assembling the picture, engineers could generate the views they needed faster and focus on resolving issues.

The same shift showed up in how we used operational data.

Rather than reacting to incidents after impact, we started using machine learning to analyze logs, identify patterns, and surface anomalies earlier. That moved teams from reactive response toward proactive monitoring and prevention.

One thing became clear very quickly: Toil reduction wasn’t just a benefit; it was the catalyst for adoption.

“Toil reduction is the biggest thing. That’s where engineers’ eyes light up,” says Beth Garrison, a principal cloud network engineer at Microsoft Digital. “If we can eliminate toil, people engineers will flock to use AI. I really believe it.”

Service engineering followed a similar arc.

Across governance, operations, productivity, and cost management, we began applying agents and automation to simplify complex work and reduce manual review cycles. Governance and compliance workflows became faster and more consistent. Operational processes benefited from guided remediation and earlier insight. Knowledge capture improved as documentation and remediation guidance could be generated and updated automatically.

When we removed repetitive work such as manual triage, rote diagnostics, endless documentation cleanup, we transformed how engineers spent their time. More focus on design. More proactive problem‑solving. More energy directed toward improving systems instead of just maintaining them.

Toil reduction made the value of AI tangible. It’s the moment AI stopped being interesting and became indispensable, and our engineering teams started asking where else we can apply it next.

Measuring what matters

By the time AI was embedded across our engineering lifecycle, a new question came into focus: “How do we know it’s working?”

In the early days, we paid close attention to usage. Which tools engineers were trying, where adoption was growing, or where it stalled. Those signals mattered and adoption was the leading indicator that people were getting comfortable and starting to integrate AI into real work.

“Adoption was always the starting point. But we were clear from the beginning that usage isn’t the destination. The real goal is impact; more time for engineers to focus on the work that truly matters.”

Ullas Kumble, principal group software engineering manager, Microsoft Digital

But using AI doesn’t automatically mean better outcomes. So, we shifted the conversation and started asking, “What’s different now that our engineers are using AI?”

That change reframed how we thought about measurement. We began looking beyond tool activity to understand impact across the engineering system. Faster design cycles. Earlier defect detection. Reduced time spent on repetitive operational work. Shorter incident resolution. Clearer documentation. Fewer handoffs. Less rework.

These weren’t abstract metrics. They showed up in the flow of work.

We were intentional about not forcing a single definition of value across every role. Software engineers, service engineers, and cloud network engineers experience impact differently. What mattered was that each team could point to tangible improvements in how work moved through the system.

That perspective shaped how leadership talked about success.

“Adoption was always the starting point,” says Ullas Kumble, a principal group software engineering manager at Microsoft Digital. “But we were clear from the beginning that usage isn’t the destination. The real goal is impact; more time for engineers to focus on the work that truly matters.”

Over time, this approach changed the quality of our conversations. Instead of debating whether AI was worth the investment, teams talked about where it was removing friction and where it still wasn’t delivering enough value. Measurement became a tool for learning and prioritization.

Moving forward

Looking ahead, one lesson stands out: this journey isn’t complete.

AI tools will continue to evolve. Agents will become more capable. Roles will keep shifting. What it means to be an engineer will continue to change. And that means our approach must stay grounded in the same principles that guided us from the start: invest in people, reinforce fundamentals, embed AI into real workflows, and stay honest about what’s working and what isn’t.

We didn’t set out to build an AI‑driven engineering organization overnight, we built it phase by phase.

By meeting engineers where they were
By reshaping culture before redefining roles.
By embedding AI across the lifecycle, not bolting it on.
By reducing toil and measuring impact where it mattered most.

The result is better engineering: powered by AI, guided by human judgment, and built to keep evolving.

Key takeaways

Here’s a set of approaches you can take to establish AI-led engineering for your organization:

• Start with access and understanding. Give engineers safe, easy access to AI in the tools they already use so curiosity and confidence can develop organically before you push for outcomes.
• Frame AI as a partner, not a replacement. Position AI as an assistant that strengthens engineering judgment and fundamentals rather than a shortcut or a threat to roles.
• Normalize experimentation without pressure. Encourage low‑risk experimentation and peer sharing instead of mandates, allowing adoption to grow through visible, practical wins.
• Invest in upskilling. Focus on evolving skills and expectations within existing roles so engineers learn how to guide, review, and stay accountable for AI‑assisted work.
• Embed AI across the full engineering lifecycle. Look beyond isolated productivity gains and integrate AI into design, build, test, operate, and improve workflows to unlock system‑level impact.
• Measure impact where engineers feel it. Move past usage metrics and track outcomes like reduced toil, faster feedback, and improved flow so teams can see where AI is truly making work better.

Try it out

Try GitHub Copilot.

Related links

• Learn how an AI-led SDLC builds an end-to-end agentic software development lifecycle with Azure and GitHub.
• Discover why developers will lead AI transformation across the enterprise.

The post Powering the new age of AI-led engineering in IT at Microsoft appeared first on Inside Track.

Maybe you’re a product manager with a backlog that you can’t ever get to. Perhaps you’re a designer who can never seem to get engineering resources assigned to you. Or maybe you’re a program manager who routinely gets stuck copying data between systems by hand.

These are common challenges knowledge workers face everywhere, including here at Microsoft. A year ago, AI enthusiasts knew agents with tools could fix these problems—they just didn’t know where to start.

Some of our employees in Microsoft Digital, the company’s IT organization and Customer Zero for the company, took a grassroots approach to solving this problem. They built something called the Frontier Forge, our pro‑code “harness” that enables our less-technical employees to get work done with agents. They use it to quickly build agentic instructions and instantly share their solutions with peers, which accelerates our productivity across the company.

The Frontier Forge represents a cultural shift in how our product managers, designers, program managers and other “I’m not an engineer but I want to build stuff” employees now apply AI tools directly to their work.

What first began as a hackathon experiment has evolved into a thriving Microsoft-internal community with nearly 100 engaged contributors, an active Teams channel, and a GitHub repository filled with templates, learning modules, and ready-to-use AI agents. The impact is measurable: Forecasting, backlog grooming and communication tasks that collectively took weeks now take hours or minutes.

“I saw myself and others spending too much of our time on data wrangling and admin tasks when we wanted to be strategizing. Nobody was building what felt truly agentic. So, we did it ourselves.”

Brett Reifers, senior product manager, Microsoft Digital

Employees who never saw themselves as technical are now building sophisticated data visualizations, automating workflows, creating prototypes, and generating learning modules. These were capabilities previously reserved for specialized engineering teams.

The “Forge” is where it’s all happening now.

From a hackathon to a movement

In early 2025, Brett Reifers, a senior product manager in Microsoft Digital, spotted a problem he couldn’t ignore. His peers, smart and driven product managers, kept asking the same question: “How do I use agents for my actual work?”

Beginner tutorials about prompt engineering felt trivial. Advanced agents with tools assumed engineering expertise. The middle ground, where AI meets real jobs, didn’t exist.

“I saw myself and others spending too much of our time on data wrangling and admin tasks when we wanted to be strategizing,” Reifers says. “Nobody was building what felt truly agentic. So, we did it ourselves.”

So, Reifers partnered with colleague Humberto Arias, a senior product manager in Microsoft Digital whose work explores the intersection of AI and productivity. Arias had been independently researching agentic solutions that could click through interfaces, open applications, and complete tasks autonomously.

The insight that unlocked everything came from a deceptively simple observation:

“Everything on the internet is a form—every site, mobile app, every click,” Reifers says. “If agents could fill out my forms in Azure DevOps, they could handle any web-based task.”

They pitched the concept of Copilot fulfilling form-based processes as an entry for Microsoft’s annual hackathon to Sean MacDonald, partner director of product management in Microsoft Employee Experience. MacDonald immediately recognized its potential.

“My reaction was simply, ‘This sounds amazing,’” MacDonald says. “This solution was exactly what we needed.”

The event proved agents could automate PM workflows: managing Azure DevOps items, generating summaries, and querying data systems. After the hackathon validated the concept, Arias suggested pushing the project to GitHub for wider exposure. Reifers then used GitHub Copilot itself, recursively using the very tools they were building, to open source the first Frontier Forge repository in 15 minutes.

A pro-code environment with natural language accessibility

The Forge combines GitHub Copilot, Visual Studio Code (VS Code), and MCPs into a framework that makes professional development tools easily accessible to non-engineers.

“The Frontier Forge is a place where you can learn regardless of your skill level. You can adopt what’s out there, even if you don’t know where to start.”

Sean MacDonald, partner director of product management, Microsoft Employee Experience

The core idea: Give employees a workspace seeded with community-created templates, learning modules, and custom agents tailored to Microsoft Digital contexts. Then let them build from there.

For MacDonald, the Forge has proven to be an accessible entry point for almost anyone, regardless of experience.

“The Frontier Forge is a place where you can learn regardless of your skill level,” MacDonald says. “You can adopt what’s out there, even if you don’t know where to start.”

An architecture for context-first AI

The technical architecture of The Frontier Forge leverages three layers simultaneously:

• VS Code provides the enterprise managed workspace where everything happens.
• GitHub Copilot offers chat functionality and AI assistance, with access to multiple models including Claude, GPT, and Gemini.
• Tools like Model Context Protocols (MCPs) act as standardized connectors that let agents access tools, data, and services locally. This unlocked what Copilot could decide and do with user approval.

“With GitHub Copilot and MCPs, there are literally no boundaries. It’s hard to explain just how transformational this can be for a product manager. Whatever you ask is transformed into code with a purpose, allowing you to do something you couldn’t before.”

Humberto Arias, senior product manager, Microsoft Digital

The MCPs connect to services like Azure DevOps (for roadmap planning and backlog management), Microsoft Documentation, Figma (for design work), and dozens of other platforms that are essential to product manager workflows. New MCPs appear daily, expanding capabilities organically as the community builds them.

Employees can even ask GitHub Copilot to build custom MCPs for services lacking official integrations. When Arias needed a PowerPoint creator that didn’t exist, he asked GitHub Copilot to create one.

“With GitHub Copilot and MCPs, there are literally no boundaries,” Arias says. “It’s hard to explain just how transformational this can be for a product manager. Whatever you ask is transformed into code with a purpose, allowing you to do something you couldn’t before.”

The shift from prompt engineering towards context engineering is another reason why the Forge works. Its workspace settings, agent instructions, skills and hooks provide a harness with guardrails that help colleagues adopt and use this.

The Forge provides a curated starting point: Microsoft Digital-specific templates, governance frameworks, security guidelines grounded in Microsoft’s Responsible AI framework, and working examples employees can immediately use and modify.

Transformational impact

The productivity gains generated by The Frontier Forge are very real. Our employees report saving weeks or even months on certain projects, especially those that previously required extensive manual work or specialized technical skills.

Case in point: Laura Oxford, a senior content program manager in Microsoft Digital, had four years’ worth of Excel files and communication metrics reports. She had always intended to use the data to create marketing forecasts, but she could never find the necessary time or resources to perform the analysis.

“The key to creating the agent was going deep into the context. It was an iterative conversation, going back and forth to fine-tune the agent until I was consistently getting the output I wanted. But it truly was just a conversation—no tech skills needed.”

Laura Oxford, senior content program manager, Microsoft Digital

Through iterative, conversation-based prompting, Oxford’s agent analyzed patterns, created projections, and produced visualizations. Oxford now has a robust historical analysis that enables prediction of future campaign performance.

“The key to creating the agent was going deep into the context,” Oxford says. “It was an iterative conversation, going back and forth to fine-tune the agent until I was consistently getting the output I wanted. But it truly was just a conversation—no tech skills needed.”

Drafting clear, executive-ready communications for complex initiatives was what brought Mark Stratford, a senior product manager with the email and calendaring service team in Microsoft Digital, to the Forge.

Before the Forge, communicating status updates to leadership meant he had to manually synthesize data from CSVs, track several approval chains at once—often in messy emails—and iterate on visualizations for what seemed like days and days.

Put more succinctly, these tasks are time-consuming chores that are perfect for AI.

“The Forge’s architecture changes how you think about the problem,” Stratford says. “Instead of iterating on prompts, you declare intent and desired outcome. The Forge’s architecture handles the rest.”

Using this pattern, Stratford created:

• Over a dozen interactive dashboards for portfolio roadmaps, migration tracking, and service health monitoring.
• Approval matrix visualizations mapping multi-stakeholder sign-off dependencies.
• Data analysis pipelines transforming raw telemetry into executive-ready narratives.

“I didn’t need to fight ambiguity or handhold the model. The architecture gave the agent a stable, skills-driven foundation from the start, which dramatically accelerated development time and improved clarity.”

Mark Stratford, senior product manager, Microsoft Digital

The Forge’s clean separation between intent, constraints, tools, and data inputs eliminated the prompt-tuning loop. Stratford mapped his objectives into the agent framework once, relying on built-in structure and guardrails.

His analysis and drafting time dropped from days to minutes. Outputs like roadmaps and data visualizations went directly into decision workflows with no manual cleanup required.

“I didn’t need to fight ambiguity or handhold the model,” Stratford says. “The architecture gave the agent a stable, skills-driven foundation from the start, which dramatically accelerated development time and improved clarity.”

Building community and sharing knowledge

A simple continuously improving repository has grown into something larger: a community of nearly 100 enthusiasts. Contributors are building templates, learning modules, and specialized MCPs tailored to their job functions. Teams are sharing wins and unlocked achievements.

“At its core, The Frontier Forge is an open-source, community‑driven experience. It’s a safer environment that will help people learn and apply Microsoft’s AI at work.”

Brett Reifers, senior product manager, Microsoft Digital

The Forge succeeds because of its emphasis on community and knowledge sharing. Its GitHub repository serves as collaborative workspace where employees contribute agents, templates, and learning resources.

This sharing culture creates a compounding cycle. One employee’s outcome becomes another’s starting point. Contributors share useful agents immediately, without lengthy approvals. This grassroots approach lets innovation spread at the pace of curiosity.

“At its core, The Frontier Forge is an open-source, community‑driven experience,” Reifers says. “The Forge is a safer environment that will help people learn and apply Microsoft’s AI at work.”

Building a safe-to-fail path

For IT leaders looking to replicate something like the Forge, MacDonald’s guidance starts with reframing the challenge.

“Find the people who are super curious and who want to learn. They will be the ones who drive innovation with AI agents and other newly developed tools.”

Sean MacDonald, partner director of product management, Microsoft Employee Experience

The barrier to agent adoption for non-engineering roles isn’t access to tools. It’s all about giving them the confidence needed to build them and then put them to work. Providing a safe, hands-on environment where people can learn at their own pace, regardless of skill level, has been an essential key to success.

Another key has been to empower the people in your organization who are eager to innovate and try new things. The Forge began with two curious product managers who decided to experiment and then shared their idea with peers.

“Find the people who are super curious and who want to learn,” MacDonald says. “They will be the ones who drive innovation with AI agents and other newly developed tools.”

For IT leaders currently trying to prepare their organizations for an AI-driven future, the story shows that the answer isn’t to wait around for perfect tools or comprehensive employee training.

“The leaders that create safe spaces for non-engineers to build with AI now will compound that advantage for years,” Reifers says. “The ones that wait will spend 2027 trying to catch-up.”

Our knowledge workers don’t need to wait for help any longer, now they can forge their own path with an agent or other AI tool they build themselves.

Key takeaways

Here are some insights your leaders can use to build grassroots-led, AI-forward communities in your organization:

• Start with volunteers, not mandates. The Forge grew to 100 contributors with zero top-down requirements. Organic growth from curious employees creates sustainable adoption.
• Highlight your quick wins. Reifers’ and Arias’ live demos of MCPs, Oxford’s 90-minute forecast and Stratford’s 20-minute drafts became the recruiting pitch for the next wave of adopters. Show your people results like these, then hand them the tools.
• Lower barriers without lowering standards. Accessibility and quality aren’t mutually exclusive. Governance and security are non-negotiable. Configure it all into the harness.
• Prioritize knowledge sharing and attribution. When one person solves a problem and shares it, dozens benefit immediately. Reward provenance.
• Ship fast, improve later. The Forge repo was built in 15 minutes. Four months later, it contained 50+ templates and agents. As much of 80% what is produced in the Forge is rewritten every other week as tools evolve. Ship MVPs and evolve based on real usage.
• Reframe outcomes > tools. Shifting from “developer tool” to “Copilot workspace” helps knowledge workers see they belong.

Try it out

Start building and sharing AI agents today with GitHub Copilot.

Related links

• See how we enable Microsoft employees to build AI agents that help them complete important tasks.
• Learn more about GitHub Copilot for Azure.
• Discover how we’re transforming our employee experience with AI. 
• Take a course on building AI agents for beginners in GitHub Copilot.
• Follow our step-by-step guide to building API-powered agents. 
• Find out how continuous improvement helps us identify agentic opportunities, and how it can help your company, too. 

The post The Frontier Firm: How knowledge workers are forging their own AI tools at Microsoft appeared first on Inside Track.

IT journey at Microsoft

The rapid advance of AI is enabling us to rethink every dimension of IT. From the apps, workflows, and services that power our employee experience, to the network, infrastructure and devices that power our employee productivity, everything is evolving quickly.

“The potential for transformation through AI is nearly limitless,” says Nathalie D’Hers, corporate vice president of Microsoft Employee Experience. “We’re evaluating every service in our portfolio to consider how AI can improve outcomes, lower costs, and create a sustained competitive advantage for Microsoft and for our customers.”

In this article, we describe some of the ways we’re already using AI, as well as new investments we’re making to accelerate our own AI-powered transformation—and to inspire yours.

{Learn how we’re reinventing the employee experience for a hybrid world here at Microsoft.Discover how we’re improving our Employee Experience as Microsoft’s Customer Zero.}

Resilient and secure infrastructure powered by AI

Reliable and secure access to corporate resources is paramount—especially at a company that has fully embraced flexible work. Our employees depend on a foundation of network connectivity to seamlessly access the tools and services they rely on every day. Harnessing the power of AI to ensure our employees stay productive and our network remains resilient and secure is one of our top investment areas in Microsoft Digital.

“AI offers a once-in-a-generation opportunity to rethink our enterprise infrastructure,” says Heather Pfluger, general manager of Infrastructure and Engineering Services in Microsoft Digital. “It’s an exciting time to be an IT leader at Microsoft as we consider all the ways IT can make our services more efficient, secure, and reliable.”

One way we’ll do that is by infusing data-driven intelligence into every part of our infrastructure, engineering, and operations to eliminate configuration drift, comply with standards and security policy, reduce operator effort and errors, and efficiently respond to rapidly changing business needs.

Investments in software defined networking and infrastructure-as-code are already improving reliability and network adaptability. AI is helping us automate workflows that deliver network services, detect anomalies, and manage compliance. We’re using real-time streaming telemetry from network devices to drive continuous operational improvements. We’ve used data from past incidents to train an AI model that will help our network engineers to reduce the time needed to mitigate infrastructure incidents with the goal of reducing or eliminating network outages and increasing employee productivity.

Securing the Microsoft network and other endpoints is critical to AI and Machine Learning (ML) are among our best tools to enhance the security and compliance of our cloud and on-premises network and hosting environments.

While Microsoft already benefits from a Zero Trust security posture, we’ll use AI and ML to further strengthen our network by automatically assigning devices to the right network, removing the burden on IT admins to classify and assign networks manually. We’ve implemented consistent access controls for wireless and wired networks to further improve security and reduce legacy VPN usage. We’re also building AI and ML capabilities to automate our security workflows, including analyzing device vulnerabilities, detecting anomalous firewall traffic flows, and managing incident diagnosis and remediation.

A vast network of interconnected devices running many different operating systems relies on our network for seamless connectivity. Managing these devices requires significant time and resources. To better manage them, we’re investing in a range of new AI-powered device capabilities spanning the entire device lifecycle.

AI-powered predictive maintenance and intelligent troubleshooting is a key investment area. We’ll use AI and ML to schedule essential maintenance tasks and autonomously fix errors and performance issues. This will reduce downtime, prolong device lifespans, and ensure employees have a consistent and productive experience by avoiding problems and errors. We’ll also use AI to analyze device settings, network activity, vulnerabilities, and user behavior, enhanced with demographic data and location metadata to offer relevant solutions for common and emerging device problems. We aim to help IT administrators be more productive through quicker decisions about device replacement, software updates, capacity increases, and other common support scenarios.

{Learn how we’re moving Microsoft’s global network to the cloud with Microsoft Azure. Discover How AI will impact the future of security.}

Employee experiences enhanced with AI

AI enables possibilities to simplify the employee experience in unprecedented ways. Our vision in Microsoft Digital is to use AI to simplify the employee experience by providing a single endpoint for most common tasks. We envision a workplace where AI seamlessly integrates into our employees’ workflows and simplifies the number of systems they must learn and navigate. We’re working to deliver a unified, connected, and personalized experience where our employees can access critical data, tools, and insights, all from one place.

“We see AI as the key to unlocking the full potential of our employees,” says Sean MacDonald, partner director of Employee Productivity in Microsoft Digital. “AI enables us to deliver personalized experiences that empower our employees to work smarter, faster and happier.”

With Microsoft 365 Copilot Chat as a central engagement hub, we’ll redesign the employee experience to no longer be app centric, but rather employee centric, to meet people in the flow of their work.

Navigating with Microsoft 365 Copilot Chat

Our employees will discover information and services through natural language Q&A to complete tasks more smoothly. Copilot Chat will support a range of different employee experiences like diving into employee benefits, facilitating career planning, processing expenses, finding information, exploring workplace amenities, purchasing devices, or upskilling for a new role. We’ll connect different Copilot features and our internal data to Copilot Chat and reduce the time to get answers or insights by more than half. Our employees can still use web or app experiences if they prefer, but Copilot Chat will become a primary entry point for many workflows.

Generative AI will also change how our employees experience support services. We aim to increase employee productivity by addressing IT issues automatically or remotely through AI-powered chats that often don’t require an agent.

{Learn more about how we’re deploying Microsoft 365 Copilot at Microsoft. Discover how Microsoft Digital is reinventing employee productivity in the hybrid workplace.}

AI-powered transformation powered by good governance

We’re working to strike the right balance with AI—we want to use it to transform the way we work and to empower our employees while also protecting the company. To get this right, we’re laser focused on adopting the right governance measures.

“We’re going to be one of the first organizations to really get our hands on the whole breadth of AI capabilities,” says Matt Hempey, a principal group product manager in Microsoft Digital. “It will be our job to ensure we have good, sensible policies for eliminating unnecessary risks and compliance issues.”

Our team in Microsoft Digital manages some of the most complex Microsoft productivity tenants in the world, and the governance of those tenants can be a challenge. Unmanaged assets like Power BI workspaces, Power Apps, or even Microsoft Teams groups increase the risk of oversharing sensitive data and can compromise the health and security of the environment. AI will be instrumental in helping us manage our tenants more effectively by helping us to detect issues and by automating time-consuming compliance tasks.

High-quality enterprise data with strong data governance is another key to our AI-led transformation. We’re establishing processes and frameworks to ensure data quality, with assigned data owners to supervise them. We’re using tools to measure data quality and produce a quality score, enhancing confidence and trust among users so they can be confident using the data in AI scenarios.

However, high-quality data is useless without ongoing governance.We have years of experience managing Microsoft’s vital tenants, and we’ve experienced the complexities of governance at global enterprise scale. AI raises issues around privacy, reputation, and copyright. Responsible and ethical applications of AI, powered by effective data governance, are our priority, so that we as a company can keep innovating without risking security or reputational damage. Using tools like Microsoft Purview, we’ll pay attention to the source, sensitivity, and lifecycle of data for AI, focusing on discovery, classification, and protection. Our goal is to safely handle sensitive data, ensuring ethical uses to achieve the right business outcomes.

{Discover how Microsoft Digital is governing its key productivity tenants to support AI. Find out how data and AI are driving our transformation.}

Transforming our approach to IT

In Microsoft Digital, we’re committed to innovating with AI to revolutionize our employee experience and to rethink IT management, work that we’ll power through consistent governance practices and high-quality enterprise data.

“From making our employees more productive to improving their experience in the hybrid workplace, to preemptively anticipating issues with our services, infrastructure, and devices, AI is fundamentally changing how we manage information technology at Microsoft,” D’Hers says.

As we’ve done since the early days of IT at Microsoft, we’ll push boundaries to become a catalyst for AI innovation for our employees and for our customers. Just as we helped shape the past three decades of enterprise IT innovation, the next steps we take on our IT journey here at Microsoft will help shape this new era of AI. We’re excited to continue sharing our insights from our journey of AI-powered transformation with our customers and partners.

Here are some learnings that we hope will help you in your journey to transform your approach to IT with AI:

• AI has the potential to revolutionize every facet of IT administration and the employee experience. Microsoft 365 Copilot Chat can increase employee productivity by making information easier to find and by reducing or eliminating repetitive and mundane tasks, which will enable your employees to focus on higher value work.
• Enterprise data is your most valuable asset in the era of AI. Invest in data and tenant governance so you can use your data to support AI-powered productivity and collaboration workloads using Azure OpenAI and other services. Microsoft Purview offers a great solution to protect your sensitive enterprise data.
• We’ll be using the power of AI to rethink our entire IT portfolio here in Microsoft Digital. As Customer Zero for some of Microsoft’s most important products for IT professionals, our insights and product enhancements will improve the experience for our customers as well.

Get started with Azure AI.

• What is Azure OpenAI Service?
• Learn about Azure OpenAI Service.
• Get started with Microsoft 365 Copilot Chat.
• Learn how we’re reinventing our employee experience for a hybrid world here at Microsoft.
• Discover how we’re improving our employee experience as the company’s Customer Zero.
• Find out how data and AI are driving our transformation.
• See how we’re getting the most out of generative AI at Microsoft with good governance.

The post Powering a generational shift in IT at Microsoft with AI appeared first on Inside Track.

We periodically update our stories, but we can’t verify that they represent the full picture of our current situation at Microsoft. We leave them on the site so you can see what our thinking and experience was at the time.

Microsoft runs on Microsoft technology. We are the proving ground for our products and when we say that software is enterprise-ready this means that we have already built it for and run it at scale in our own enterprise.

We are in the business of building the future of technology. And more often than not, our software is built using Microsoft Azure DevOps.

Microsoft is undergoing a mission to transform the way we work. There are three key pillars to this strategy: tools, processes, and people.

—Heather Pfluger, general manager of Infrastructure & Engineering Services, Microsoft Digital

Microsoft Azure DevOps was designed to support enterprise teams who need a collaboration and product management tool with organizational structures and robust security controls that meet the real world of how teams are actually run. With Microsoft Azure DevOps we can smartly plan our projects, improve collaboration, and ship our products faster with increased visibility, security, and efficiency.

“Microsoft is undergoing a mission to transform the way we work. There are three key pillars to this strategy: tools, processes, and people,” says Heather Pfluger, general manager of Infrastructure & Engineering Services in Microsoft Digital, the company’s IT organization. “But the operative change is to our culture.”

We take pride in developing our software through the real-world use of our global teams. We refer to ourselves in these cases as “Customer Zero,” where we effectively are the launch customer for our product engineering teams. This allows our employees to use leading-edge solutions before our customers to improve our products based on our real-world usage.

Shifting left: Building a tool for the modern engineering environment

This story begins with the launch of Windows Azure in 2008, which became Microsoft Azure in 2010 and really started to come of age by 2014. That’s when Microsoft Digital, and nearly every other team at Microsoft, began migrating their legacy workloads to Azure. The team that became Microsoft Digital was faced with a momentous leap forward due to the cloud, enabling an opportunity to revolutionize our engineering processes.

One way that we describe this culture shift internally is “shifting left.” We are moving our engineering focus closer to our dev teams by giving them more tools and more power to efficiently drive their progress right at the early stage of development.

They have what they need to do their job at hand while at the same time introducing efficiencies in team structure, organization, and security. What used to take a large team of engineers and testers to accomplish is now taken care of by leaner, more agile developer teams themselves with the aid of automations and Microsoft Azure’s inherent security features.

Microsoft Azure DevOps is all about productivity for developers, and over many years of refining our processes we’ve increased both the quality and velocity of our output. We have the entire Microsoft Digital organization running on a single Azure DevOps instance, which gives unprecedented visibility and accountability for our processes.

In an organization our size, which has been creating software for as long as we have, a recurring concern is the long-term traceability and maintenance of our code. Today, we have new processes in place to better organize our output and make it easier for future Microsoft engineers to understand what we’ve built.

“Using area paths, we mapped out the entire organization and created a hard chain of custody for every line of code, in every repo,” says Martin O’Flaherty, principal PM manager of the Microsoft Digital Engineering Systems team. “If you create something, it will be tied to a repo, which will be tied to a team. No longer will there be code that can’t be accounted for – it’s all hard-wired in the backend. If something goes wrong, we immediately have a point of contact for the person who is accountable to remediate the issue.”

However, the journey never ends, as technology is always evolving.

—Martin O’Flaherty, principal PM manager of the Microsoft Digital Engineering Systems team

On our single Microsoft Azure DevOps instance, we have thousands of daily active users, thousands of repos, and more than 20,000 build and release pipelines. We’ve shown that Azure DevOps, right out of the box, can not only handle our scale but it excels at it. Azure DevOps is propelling us forward and accelerating our progress.

Get clean and stay clean

A significant opportunity we had with moving our entire engineering team to a common deployment of Microsoft Azure DevOps was cataloging and consolidating all our services. This process, which started five years ago, led to the retirement of nearly 30 percent of our legacy applications, while enabling us to deploy what remained rest to the cloud. By carefully selecting the applications and processes to continue and others to sunset, we quickly improved our security posture. We refer to this era as “getting clean.”

“However, the journey never ends, as technology is always evolving,” O’Flaherty says. “What we considered secure in 2017 is so rudimentary to how we approach things now. This is why we must ‘stay clean’ by continually monitoring the guardrails we put in place for our developers.”

Pursuing the mission of maintaining a strong security posture throughout our Microsoft Azure DevOps instance supports a simple imperative: if our primary tool for developing code isn’t secure, nothing we produce will be secure.

To accomplish “staying clean,” we have designed, enacted, and maintained a clear security and compliance framework within Microsoft Azure DevOps. We’ve streamlined our pipelines and deployed common protocols to all our teams, which ensures all our releases are held to the same high security standards.

Security, across the board

We have also “shifted left” our application security posture. We’ve moved our security focus closer to the developer by utilizing breakthroughs in technology and strategy like GitHub Advanced Security for Microsoft Azure DevOps. This new tool, currently in public preview, automatically scans new code to ensure there are no secret leaks or exposures in your Microsoft Azure repos.

This is a powerful advance in security technology that pushes the boundary of our security posture to the code itself, right as it is being written. It alerts the developer in real time to potential errors or security concerns. By moving security and testing earlier in the development process we further enhance security during product development and reduce the risk of errors being released.

The security revolution powered by Microsoft Azure DevOps and running on a single instance is paying dividends for Microsoft Digital. Now, we universally apply and monitor security policies rather than relying on each team to set their own parameters. By utilizing common guardrails, we are able to monitor and apply policies across the board. We’ve baked in security early in the development cycle, and it’s done automatically and consistently.

Mature software that is enterprise ready

New customers to Microsoft Azure DevOps gain from all of the efficiencies and learnings Microsoft Digital has pioneered as Customer Zero. It’s now a mature product with a lengthy track record, and it works right out of the box.

“If I was advising a new enterprise just starting out with Azure DevOps, I would tell them to not just copy our way of doing things,” says Damon Gray, principal group engineering manager for Optimization, Engineering & Networking Services in Microsoft Digital. “They can smartly set up their instance themselves and add the guardrails that fit their organization over time. Within the day, right out of the box, they’ll be securely submitting and releasing code to the cloud.”

Companies of our scale require robust and customizable solutions to allow teams to build with the freedom to push the envelope of what’s possible. Microsoft Azure DevOps was designed, built, tested, and optimized to make our teams as efficient and secure as they need to be. We build the future of software at Microsoft, and this software is built with Azure DevOps.

“Azure DevOps is the tool that we utilize company-wide to allow our teams to build the future, wherever in the world they are working,” Pfluger says.

Here are some tips you can use to help you get started with Microsoft Azure DevOps:

• Azure DevOps is a powerful productivity and security tool right out of the box. You can release code the same day you set up your instance and you will be able to dial in your security guardrails over time.
• Azure DevOps scales with you, whether you’re a small team or a large enterprise, or a small team with dreams of becoming much larger. Build with confidence.
• “Get Clean/Stay Clean” is an operative philosophy that produced immediate security gains for our team.

Try Microsoft Azure DevOps by signing up for a Microsoft or GitHub account.

• See how we’re optimizing Azure resources at Microsoft with AI and Managed Cloud Labs.
• Discover five principles that guided our network journey to Azure and the cloud.
• Learn how we implemented a Zerto Trust security model at Microsoft.

The post Streamlining engineering at Microsoft with Azure DevOps appeared first on Inside Track.