For years, IT and DevOps teams have wrestled with the same stubborn problem: how do you automate workflows in systems that were never built for automation? Legacy apps, vendor portals, and proprietary line-of-business platforms rarely offer APIs. That means someone, usually a human, ends up clicking through screens, entering data, and completing transactions by hand.
Microsoft has a direct answer to that problem. Computer use in Microsoft Copilot Studio is now generally available, with expanded availability rolling out to all commercial geographies in Microsoft Power Platform.
What Computer-Using Agents Actually Do
The simplest way to think about it: computer use gives an agent the same tools a person has — a browser, a screen, a keyboard, and the ability to read what’s on the page and take the next logical step.
That’s a meaningful shift. Most automation tools rely on brittle, selector-based scripts that break the moment a UI changes. Instead, the computer uses a tool that relies on vision and reasoning to navigate live UIs — adapting when layouts shift, fields move, or workflows branch.
In practical terms, this means agents can now handle workflows that previously required manual workarounds or expensive integration projects. For organizations with deep investments in proprietary platforms or third-party portals, workflows that previously required either a multi-quarter integration project or an army of contractors clicking through screens can now be handed to an agent. For enterprise IT teams, this can also reduce pressure to modernize or rebuild legacy systems before automation can begin.
A Real-World Example
Microsoft points to Graebel, a global mobility and relocation services company, as an early example of what this looks like in production.
Working with GET AI and Microsoft, Graebel built and deployed the Graebel Service Order Agent in Microsoft Copilot Studio. The agent monitors designated mailboxes and interprets unstructured service-order emails using Azure Content Understanding, extracting key data into a structured form with confidence scoring. It validates each request against Graebel’s business rules, service logic, and compliance requirements before any action is taken. The agent then operates Global Connect directly through its UI — navigating screens, entering data, and completing transactions exactly as a trained human operator would, without APIs or platform redevelopment. Exceptions and low-confidence cases are escalated through human-in-the-loop workflows, preserving governance and service quality.
That last point matters. Keeping humans in the loop for exceptions isn’t a limitation — it’s the right design for enterprise workflows where compliance and accuracy aren’t optional.
Security, Governance, and Model Choice
One concern that often follows news like this: what about security? Microsoft is keenly aware of that question. The platform is built within the existing Power Platform governance framework, so agents inherit the same security controls, compliance policies, and audit capabilities that organizations already have.
Microsoft is actively investing in advanced governance, operations, and scale for computer-using agents, with customer feedback directly informing the roadmap.
Copilot Studio now also supports model selection for agents, with models from both OpenAI and Anthropic available. That flexibility matters for teams that want to match the right model to the right task — whether they’re optimizing for reasoning depth, latency, or cost.
What DevOps Teams Should Pay Attention To
Computer-using agents aren’t a replacement for well-designed APIs or modern integrations. But they do fill a long-standing gap in enterprise environments. Not every system gets modernized on schedule. Not every vendor exposes the data access your team needs. And not every business process maps neatly to an existing automation workflow.
Getting started is straightforward. Teams can create or open an agent in Microsoft Copilot Studio, navigate to Tools, select Add tool, and then add a new computer-use capability. From there, the task can be described in natural language.
“Computer-using agents extend automation from API-reachable systems to anything with a UI, the larger surface in most enterprises. General availability in Copilot Studio repositions the conversation from integration backlog to action governance, and model choice across OpenAI and Anthropic confirms that competitive value sits at the operating layer,” according to Mitch Ashley, VP and Practice Lead, Software Lifecycle Engineering, The Futurum Group.
“Procurement should treat this as a control plane question. UI-driven agents inherit human-level reach across legacy and SaaS workflows, so authorization, audit, and exception handling must operate at the action level. Without that visibility, autonomy stalls short of what the technology enables.”
The broader trend here is clear. Agentic AI is moving beyond conversation and into action. For DevOps teams managing a mix of modern and legacy environments, that’s worth paying close attention to.
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