Network engineers at Microsoft rarely struggle because they lack the tools to do their work. The challenges appear when they need to access too many tools spread across too many workflows.
At Microsoft Digital, the company’s IT organization, we manage an enterprise network that supports employees, services, devices, and business-critical operations around the world. Over the years, we’ve invested heavily in automation, infrastructure as code, and self-service capabilities to improve consistency and reduce operational overhead.
But as our environment has grown, so has the number of systems, workflows, repositories, and tools that our engineers have to navigate. So we decided to take a different approach. We asked, “What if engineers only needed to describe the outcome they wanted, instead of manually figuring out which tools to use?”
That idea became the foundation for Autonet AI Agent, our natural language platform for network configuration management that is saving us time and driving greater efficiency for our engineering teams.
Creating a ‘conversational front door’
In the past, our network engineers often needed to know where information lived before they could solve a problem. Different workflows required different tools, and different tasks relied on familiarity with a variety of systems.
A single network request could start a complex workflow. where engineers needed to gather information from multiple tools and systems. They might use one tool to review existing configurations, another to validate the current state, and still others to identify affected devices, generate changes, prepare rollback plans, and route updates through established governance processes.
Autonet AI Agent simplifies that process by giving our engineers a single place to start.
When engineers access the portal, they use natural language to describe the outcome they’re looking for. We describe it as a “conversational front door.”
The agentic platform gathers the required information and context, then coordinates the appropriate actions. It also alerts a human engineer for approval before making any changes.
Specialized agents support specific network configuration tasks. For example, our IP Helper Agent retrieves IP helper data. Our Core WAN agent manages configuration templates.
“The goal was to create a conversational front door that could connect people with the right network data, workflows, and specialist capabilities without requiring them to understand everything happening behind the scenes.”
Sai Madhav Tiruvuri, senior product manager, Microsoft Digital
Agents also work together more broadly. For example, our IP Helper Agent can connect with our Intake Agent, which is responsible for handling incoming workflows. Autonet AI Agent coordinates these workflows, reducing the number of systems that network engineers manage directly.
“We wanted engineers to focus on outcomes rather than tooling,” says Sai Madhav Tiruvuri, a senior product manager in Microsoft Digital. “The goal was to create a conversational front door that could connect people with the right network data, workflows, and specialist capabilities without requiring them to understand everything happening behind the scenes.”
It wasn’t just up to our platform team to develop the agents that make up this solution. Because we’re committed to Frontier Transformation at Microsoft, we’re actively exploring ways for every team to put AI to work, build trust, and scale transformation across everyday workflows.
As a result, we’ve been empowering different network engineering teams to build their own agents. To support their work, we’ve implemented review and governance processes to ensure they meet our standards for security, privacy, and responsible AI.
As we created Autonet AI Agent, we collaborated with those teams to understand their needs. From there, we incorporated the agents they had already created into the platform by designing the portal, shell, components, and model context protocol (MCP) rules and servers that connect all of the pieces.
This approach capitalizes on the expertise of teams that know the work best while giving our engineers a simpler experience overall. It also allows the platform to accommodate new capabilities. We can add new agents for specialized tasks behind one consistent interface, and engineers will use the same entry point as new workflows and agents become available.
Building on a trusted source of truth
Before we introduced agentic AI into network engineering workflows, we focused on something more fundamental: trusted data.
We knew that any agentic experience would only be as useful as the information available to the agent in question. Rather than connecting AI to multiple disconnected systems, we grounded it in a single, governed source-of-truth repository that captures configuration data.
This repository provides a common foundation for engineers, APIs, partner tools, automation workflows, and AI-powered experiences. As a result, we can funnel trusted network data through a consistent interface.
The lesson applies beyond networking. Any organization that’s interested in agentic workflows should first focus on establishing authoritative operational data.
“The opportunity wasn’t simply to replace our existing automations. We wanted to reduce the effort required to prepare work for automation and help engineers move more quickly from intent to action.”
Faris Mango, principal software engineering manager, Microsoft Digital
The quality of the experience depends on the quality of the underlying source of truth. From there, AI can help users find, understand, and act on information.
Combining AI with deterministic automation
Many network operations processes are deterministic by design. Validation routines, deployment pipelines, approval workflows, and configuration distribution systems all depend on predictable and repeatable outcomes.
As part of our agentic workflows, existing automation continues to perform deterministic steps. But on top of those more rigid automations, our new agents support processes that require interpretation, coordination, and connections across systems.
This division of work became an important design principle. We use AI for interpretation and coordination, and deterministic automation for predictable execution.
“The opportunity wasn’t simply to replace our existing automations,” says Faris Mango, a principal software engineering manager in Microsoft Digital. “We wanted to reduce the effort required to prepare work for automation and help engineers move more quickly from intent to action.”
Making network engineering jobs easier
Network configuration rendering and validation was one of our first use cases.
Configurations rely on templates, business logic, and facts stored in source-of-truth systems. Small changes can affect how we render configurations across multiple device types and deployment scenarios.
Traditionally, testing those changes could require significant manual effort. Engineers might need to compare template versions, validate outputs, investigate unexpected behavior, and move information between systems.
With Autonet AI Agent, users initiate those workflows in one place using natural language. Engineers can retrieve network facts, render configurations using current source-of-truth information, compare results across versions, identify differences, and investigate issues in a process that looks more like a conversation than navigating a series of tools.
“The more conversational experience makes it easier to test changes, compare outputs, understand what changed, and continue iterating until the configuration behaves the way you expect.”
Pruthvish Panakanti, senior network engineer, Microsoft Digital
Imagine a network engineer who receives a ticket requesting a configuration update. Once they’ve accessed Autonet AI Agent, the platform would execute the following steps as part of an agentic workflow:
- Interpret the request’s intent
- Identify affected devices and locations
- Retrieve relevant configuration information
- Compare current and intended states
- Detect potential conflicts
- Generate proposed configuration updates.
- Create rollback guidance
- Produce reviewable artifacts
“Rendering and validating changes used to involve a lot of manual comparison,” says Pruthvish Panakanti, a senior network engineer in Microsoft Digital. “The more conversational experience makes it easier to test changes, compare outputs, understand what changed, and continue iterating until the configuration behaves the way you expect.”
The benefit extends beyond the configuration change itself. Before engineers can decide what to do, they need to assemble a reliable picture of the current environment and understand how a proposed update might affect it. That preparation can require gathering network facts, reviewing existing configurations, comparing intended and current states, and identifying potential conflicts.
“A significant amount of operational work happens before we ever make a configuration change. Engineers spend time gathering context, validating information, and making sure they have what they need. That’s where these conversational workflows remove friction.”
Christopher Kuilan, senior cloud network engineer, Microsoft Digital
Autonet AI Agent brings those steps into a connected workflow. Instead of manually moving information between systems, engineers can use a conversational experience to retrieve relevant context, examine differences, and develop a proposed change for review. The platform can also prepare supporting artifacts and rollback guidance, helping engineers evaluate the change before deciding whether it should proceed.
This allows engineers to spend less time assembling the inputs needed for a decision and more time applying their expertise. The platform does not remove that responsibility. It makes the information easier to access and review while keeping engineers in control of the final decision.
“A significant amount of operational work happens before we ever make a configuration change,” says Christopher Kuilan, a senior cloud network engineer in Microsoft Digital. “Engineers spend time gathering context, validating information, and making sure they have what they need. That’s where these conversational workflows remove friction.”
Preserving governance and human review
Introducing AI into infrastructure operations requires robust governance. Throughout development, we prioritized human-in-the-loop oversight as a requirement.
For us, trust depends on transparency. Engineers need visibility into how agents generate recommendations and the ability to inspect supporting information before taking action.
“We wanted to make it easier for engineers to get the information they need while keeping governance and review in place.”
Darron Sullivan, principal software engineer, Microsoft Digital
Autonet AI Agent can retrieve information, prepare recommendations, generate artifacts, and help users understand potential actions. But engineers remain responsible for reviewing recommendations and determining whether proposed changes should move forward.
When an agent proposes an action or change, the platform alerts an engineer and waits for approval.
“Agentic systems are most effective when they’re working in established operational controls,” says Darron Sullivan, a principal software engineer in Microsoft Digital. “We wanted to make it easier for engineers to get the information they need while keeping governance and review in place.”
Looking ahead
We’re currently piloting Autonet AI Agent across a relatively small set of processes, but we see it as the beginning of a broader platform approach rather than a standalone solution.
“When we can bring the right information, automation, and specialized capabilities together through one experience, our engineers can spend less time figuring out which system to use and more time applying their expertise to the network.”
Jason Thompson, principal group product manager, Microsoft Digital
And we’ve already seen impressive results. In some cases, IP helper data retrieval that used to take up to three weeks can now be accomplished within a day or less.
The conversational front door gives us a framework for introducing additional specialist capabilities over time while still maintaining a consistent user experience. As more workflows and domains become available, engineers can continue interacting through a familiar interface while the platform expands behind the scenes.
“When we can bring the right information, automation, and specialized capabilities together through one experience, our engineers can spend less time figuring out which system to use and more time applying their expertise to the network,” says Jason Thompson, a principal group product manager in Microsoft Digital. “That’s where we see the real value of this technology.”
Autonet AI Agent reflects how we’re applying agentic AI to our own network operations at Microsoft. Organizations interested in building similar agentic experiences can explore the Microsoft agent ecosystem to determine the right approach for their needs. Get started with Microsoft Agent 365 to manage and govern agents across your organization or explore our agent development tools to build your own solutions.
Key takeaways
Our experience building Autonet AI Agent reinforced a number of specific recommendations that can help your organization build a similar solution:
- Prioritize AI-ready data. Establish a governed source of truth before introducing AI into operational workflows.
- Focus on tasks and results. Design experiences around outcomes rather than tool selection.
- Choose the right automation approach. Discern where deterministic automation makes sense, and what agentic workflows can take over. Use AI where intent interpretation, context gathering, and validation add value.
- Keep humans in the loop. Maintain human review and existing governance controls. Prioritize transparency so users can understand and validate recommendations.
- Future-proof your platform. Build orchestration layers that allow specialized capabilities to evolve and can include new agents over time.
Try it out
- Get started with Microsoft Agent 365 at your company and read a guide to deploying Agent 365 based on our internal experience here at Microsoft.
Related links
- Discover how AI agents are reshaping network operations at Microsoft.
- Learn from our experience on how to choose the right Microsoft agent architecture.
- Explore ways Copilot Cowork is changing the way we work at Microsoft.
- Read our guide to governing AI agents at scale, based on our internal experience.
- Find out how we’re enhancing Microsoft network reliability with AIOps and Network Infrastructure Copilot.
