As we move from AI experimentation to enterprise-scale deployment, a new challenge is emerging: How do we govern thousands of AI agents, workflows, and copilots across Microsoft without slowing innovation?
We’ve learned through our Customer Zero journey—where we use our technology and services first and communicate our learnings back to our product groups—that responsible AI (RAI) is an essential scaling function. It turns AI experimentation into AI systems that can operate safely across enterprise functions.
From supply chain operations, to legal workflows, to customer support, and countless other essential business processes, we are learning that scaling AI requires operating discipline:
- Being intentional about the purpose of any AI solution
- Making its decisions observable and auditable
- Empowering teams by ensuring they have the expertise and processes necessary to make responsible AI decisions themselves
Responsible AI as a scaling function
Our Office of Responsible AI creates and implements standardized RAI policies and processes, which speed up reviews and allow for consistency across products. The goal is to build responsible AI into the design and development of agentic AI systems, rather than treat it as something added at the end.
“If you start with responsible AI principles by design, privacy by design, and security by design, the impact in terms of resources is extremely low. And the benefit will be projected across the entire lifecycle of your solution.”
Alberto Carrozzo, senior technical program manager, Microsoft Europe, the Middle East, and Africa (EMEA)
That approach is reflected in the work of Alberto Carrozzo and his team, who are experts at helping apply responsible AI practices across internal AI scenarios.
“If you start with responsible AI principles by design, privacy by design, and security by design, the impact in terms of resources is extremely low,” says Carrozzo, a senior technical program manager and an RAI leader for Microsoft Europe, the Middle East, and Africa (EMEA). “And the benefit will be projected across the entire lifecycle of your solution.”
Our responsible AI standard
Learn about responsible AI at Microsoft, including tools, practices, and policies.
As our teams across Microsoft apply more and more AI tools in their work every day, distinct risk profiles appear. Supply chain agents use business-confidential information and supplier data. Legal systems analyze sensitive records and must make defensible decisions about their relevance. Customer support tools put AI directly into conversations with external customers.
Carrozzo’s team has completed more than 2,000 internal-tool reviews so far, and they’re using their learnings to help prioritize and process a pipeline of thousands more.
Patterns from earlier work can direct reviewers toward cases that warrant closer attention, which gives our team a way to handle that volume consistently and efficiently.
“When you have thousands of agents, you need to use what you learned from the past,” Carrozzo says. “The more we learn, the more we need to use this knowledge to follow the risk and address it as fast as possible.”
Supply chain: Making agent behavior more predictable
Across our Customer Zero experience, several practices have emerged that can help teams with adoption and scaling work. The first may seem counterintuitive: Sometimes scaling AI means intentionally limiting what an AI system can do.
Our cloud supply chain organization—which sources, delivers, maintains, and ultimately decommissions the hardware behind all our company services—had grown its volume of business fivefold in recent years, while staffing remained flat. They needed to innovate.
The organization now has more than 100 enterprise-grade AI agents in production. Analysis that previously took several people days to assemble can now be automatically prepared and updated continuously, giving planners a real-time starting point to begin their work.
“If I build a planning agent, it shouldn’t be able to answer a question about the weather. It needs to be grounded in the fact that it was created to do a certain job.”
Jeevan Pathuri, vice president of software engineering, Cloud Supply Chain, Azure Hardware Systems and Infrastructure
As our teams put more agents into production, testing revealed a practical issue common with AI tools: An application sometimes allows through more of the model’s general capability than its business purpose requires. At enterprise scale, a broader range of capabilities outside the agent’s intended scope can lead to uncertainty with its potential behavior, making decisions less predictable and outcomes harder to anticipate.
Our Cloud Supply Chain group uses OneRAI to register AI capabilities, document models and evaluations, track approvals, and test those boundaries as part of its responsible AI process.
“With RAI, I get the benefit of running the Red Team Agent to make sure a tool is not doing things that are outside of its boundary,” says Jeevan Pathuri, a vice president of software engineering in Cloud Supply Chain, part of Azure Hardware Systems and Infrastructure at Microsoft. “If I build a planning agent, it shouldn’t be able to answer a question about the weather. It needs to be grounded in the fact that it was created to do a certain job.”
Defining what an agent should and shouldn’t do up front reduces ambiguity, minimizes rework, and accelerates deployment by making system behavior more predictable.
A narrow scope also helps employees see what the system was built to handle and where their own judgment should be applied. AI-generated recommendations remain simply inputs for a decision—the person using the agent still owns the outcome.
Legal review: Making outputs verifiable
Litigation and regulatory investigations often require our legal department to review large volumes of data for relevance, privilege, personally identifiable information, security risks, and confidentiality concerns.
After search and machine-learning filters reduce the total volume of material, attorneys from our Corporate, External and Legal Affairs (CELA) department may still need to read tens of thousands of documents individually.
“The AI-generated reasoning needed to be reproducible and explainable. We had to make sure that the insights were verifiable—that it was not a hallucination and was accurately tied to the document itself.”
EJ Bastien, general manager of discovery programs, Microsoft Corporate, External and Legal Affairs (CELA)
Generative AI creates an opportunity to apply case-specific instructions across a large collection of documents, classify and summarize content, and preserve a written reason for each decision. But in highly sensitive legal work, getting an answer isn’t enough. Attorneys need to understand why the AI reached its conclusion and be able to verify it against the underlying evidence.
“The AI-generated reasoning needs to be reproducible and explainable,” says EJ Bastien, a general manager of Discovery Programs in Microsoft CELA. “We had to make sure that the insights were verifiable—that it was not a hallucination and was accurately tied to the document itself. This is so that any time an attorney looks at the output, they have a written explanation for why a given document would be considered relevant or not.”
CELA’s agentic system was built in Azure using Azure OpenAI so that internal documents would not be exposed to a public model. The agent is restricted to a curated document set, rather than searching broadly. Attorneys can inspect the explanation, find the supporting material, and test whether the interpretation holds up before they make a legal decision.
Bastien describes the RAI operating principle as “trust but verify.” Citations help users confirm what a source says and can reveal other repositories that may need to be preserved or collected. A strictly scoped corpus of documents and durable outputs support a review method that legal teams can explain.
Customer support: Keeping responsible AI current as technology evolves
In customer support, the challenges come in real time as our teams handle customer calls from nearly 200 countries in more than 60 languages. Some of these cases can be highly specialized, and only a limited number of support engineers have the expertise to handle them.
“There may be five experts in the world on a particular issue, and they all speak English,” says Bill Fillmore, a senior technical program manager in Customer Service and Support. “But we now have an AI-powered translation platform that enables us to communicate with that customer in their chosen language.”
However, putting AI directly into customer conversations creates responsible AI challenges. The system interacts with customers and customer data while technology and its security, privacy, and governance requirements continue to evolve. Addressing the RAI requirements only once, before deployment, isn’t enough.
“RAI is a priority for us. We make sure that the guardrails are there first, as far as security, privacy, and governance go.”
Josh Wong, senior product manager, Customer Zero Engineering
Ensuring that customer support use cases like front-door problem solving and partially autonomous support are grounded in responsible AI principles has been a game changer for the team.
“RAI is a priority for us,” says Josh Wong, a senior product manager in Customer Zero Engineering. “We make sure that the guardrails are there first, as far as security, privacy, and governance go. Whenever there are updates to those things, we prioritize making sure we incorporate them into our AI solutions. That’s non-negotiable at Microsoft.”
That practice gives the team a way to keep the solution aligned with our requirements as both the technology and its use case evolve. Rather than treating responsible AI as a one-time approval, we continually incorporate updated requirements into the AI solution.
Looking ahead to the Frontier future
With the lightning pace of change that comes with agentic development, we’re not standing still at Microsoft when it comes to responsible AI work.
Pathuri’s Cloud Supply Chain group is progressing from individual agents to end-to-end supply chain workflows driven by AI. Bastien sees Microsoft’s legal teams prototyping more case-specific AI tools while preserving the same obligations around evidence and human judgment. Wong’s and Fillmore’s teams are exploring AI for case routing, front-door problem solving, and autonomous support functions.
“These guardrails solve the challenge of scaling AI safely without slowing teams down. Our goal is not to block innovation, but to make safe AI adoption the default path.”
Alberto Carrozzo, senior technical program manager, Microsoft Europe, the Middle East, and Africa (EMEA)
As AI takes on a larger role in the enterprise, the lessons from our Customer Zero experience become more important. Our teams cannot eliminate every risk when developing AI-powered solutions, but they can establish evidence, ownership, and feedback mechanisms that reinforce responsible AI principles to support stronger data governance without losing sight of the larger goals.
This is where responsible AI becomes a scaling function.
“Things like mandatory risk assessments before deployment, permission-based data access, continuous telemetry and monitoring, and automated detection of security and responsible AI risks are key to effective AI deployment,” Carrozzo says. “These guardrails address the challenge of scaling AI safely without slowing teams down. Our goal is not to block innovation, but to make safe AI adoption the default path.”
Key takeaways
Here are some lessons that can aid your own efforts to turn responsible AI into an engineering discipline that supports safe, regular deployment:
- Responsible AI works best when it’s designed in, not added on. Teams that set scope, data limits, and human checkpoints early in the process spend far less time on rework later.
- Standardized reviews let governance scale with AI adoption. Shared policies and lessons from past reviews help our teams keep up with tens of thousands of agents.
- A narrow scope is a safety feature. Agents built to do one job well are easier to test, easier to trust, and easier for employees to work alongside.
- Verifiable AI output is the standard in high-stakes work. Our legal teams require every AI conclusion to come with a reproducible, evidence-backed explanation.
- Guardrails speed teams up rather than slowing them down. Addressing security, privacy, and governance first makes safe AI adoption the default path.
