Published February 25, 2026

Cursor AI Agents Now Interact with Software Visually

Cursor Taught Its AI Agents to Use a Computer

Cursor has released an update where AI agents can now independently run and test code directly on a virtual machine.

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Event Source: Cursor AI Reading Time: 4 – 5 minutes

There's a difference between writing code and making sure it works. Previously, AI assistants in Cursor could do the former, but the latter was left to the user: run the code, see what happens, go back, and fix it. Now, the agent can handle this entire cycle on its own.

New AI Agent Computer Control Capabilities

What's New

Cursor has given its cloud agents the ability to control a virtual computer. Simply put, the agent no longer just writes and delivers code – it can now open a browser, launch an application, click around the interface, and see what's happening on the screen. All of this occurs in an isolated environment, without access to your local machine.

This is what the industry calls computer use – the ability for an AI to interact with programs just as a human does: through a visual interface, not just through code or commands.

Why AI Agents Need Visual Interface Interaction

Why Does an Agent Need to See the Screen?

When a developer codes a feature – say, a registration form or a button animation – they don't just look at the code. They open a browser and check: is everything displaying correctly, does the transition work, are there any visual bugs? This is what's known as “eyeballing it”.

Until now, an AI agent was deprived of this ability. It could write the logic but couldn't see the result. Now, it can. The agent runs what it has created and visually inspects its work, just as a human would at their monitor.

This is especially important for frontend development – the part of software engineering responsible for the user interface. Many issues there are hard to check without “eyes”: layouts can break, elements can overlap, and buttons might not respond to clicks. The agent is now capable of detecting all of this on its own.

AI Agent Demonstrates Work Visually

Show, Don't Just Tell

Another point worth noting: the agent can not only test the code but also demonstrate the result. In other words, it can record or play back what it has created – a sort of “demo” of the work it has completed.

This changes the interaction model. Instead of receiving a code file and having to figure out how everything fits together, you can see it working in action. For those who assign tasks to the agent, this significantly lowers the barrier to entry: there's no need to set up an environment and run everything manually just to confirm the task is done.

Cloud-Based AI Features Explained

This is a Cloud-Based Feature – And Here's Why That Matters

An important detail: this applies to Cursor's cloud agents, not the local AI assistant built into the editor. The cloud agent runs on a remote server in an isolated virtual machine. It doesn't touch your system and has no access to your files outside the project.

This is crucial from a security standpoint. The ability to control a computer is a powerful thing, and the fact that it's implemented in an isolated environment, rather than on the user's machine, mitigates most of the obvious risks. The agent lives in its own “bubble” and only works with what it's been given.

Future of AI Agents and Software Development

Where This is All Headed

Looking at the bigger picture, this is part of a broader trend: AI agents are gradually getting more “hands”. First, they could only answer questions. Then, they learned to write and edit code. After that, they could execute commands in a terminal. Now, they can control a user interface and see the results of their work.

Each step like this increases their autonomy. The agent needs a human less and less as an intermediary between “write” and “test”. This doesn't mean developers are becoming obsolete; rather, the nature of their critical involvement is changing. The agent takes on the routine cycle of “write – run – spot error – fix”. What's left for the human is to formulate tasks, evaluate the results, and make decisions where true expertise is required.

How well these agents handle this cycle in practice, only time and real-world usage will tell. But the direction is clear: development tools are becoming not just smarter, but more autonomous. 🖥️

Original Title: Cursor agents can now control their own computers
Publication Date: Feb 24, 2026
Cursor AI cursor.com A U.S.-based AI-powered code editor assisting developers with writing and analyzing code.
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This material is not a direct retelling of the original publication. First, the news item itself was selected as an event important for understanding AI development. Then a processing framework was set: what needs clarification, what context to add, and where to place emphasis. This allowed us to turn a single announcement or update into a coherent and meaningful analysis.

Neural Networks Involved in the Process

We openly show which models were used at different stages of processing. Each performed its own role — analyzing the source, rewriting, fact-checking, and visual interpretation. This approach maintains transparency and clearly demonstrates how technologies participated in creating the material.

1.
Claude Sonnet 4.6 Anthropic Analyzing the Original Publication and Writing the Text The neural network studies the original material and generates a coherent text

1. Analyzing the Original Publication and Writing the Text

The neural network studies the original material and generates a coherent text

Claude Sonnet 4.6 Anthropic
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Gemini 2.5 Pro Google DeepMind step.translate-en.title

2. step.translate-en.title

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Gemini 2.5 Flash Google DeepMind Text Review and Editing Correction of errors, inaccuracies, and ambiguous phrasing

3. Text Review and Editing

Correction of errors, inaccuracies, and ambiguous phrasing

Gemini 2.5 Flash Google DeepMind
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DeepSeek-V3.2 DeepSeek Preparing the Illustration Description Generating a textual prompt for the visual model

4. Preparing the Illustration Description

Generating a textual prompt for the visual model

DeepSeek-V3.2 DeepSeek
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FLUX.2 Pro Black Forest Labs Creating the Illustration Generating an image based on the prepared prompt

5. Creating the Illustration

Generating an image based on the prepared prompt

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