Cursor Cloud Agents: AI That Uses a Computer Like a Developer

On February 24, 2026, Cursor shipped the most significant update in its history: Cloud Agents with Computer Use. The feature gives AI agents their own isolated virtual machines where they can write code, open a browser, test what they built, record a video demo of the result, and submit a pull request—all without touching your laptop.

This is not autocomplete with extra steps. This is an AI agent that operates a computer the way a developer does: it clicks through a web app to verify a feature works, scrolls a spreadsheet to check data, opens dev tools to debug a layout issue, and captures the evidence on video before handing you a merge-ready PR. Cursor is betting that the next era of software development looks less like pair programming and more like managing a team of autonomous contributors.

Here is what Cloud Agents actually do, how Computer Use works under the hood, what it means for your workflow, and how it stacks up against the competition.

What Are Cursor Cloud Agents?

Cloud Agents are autonomous AI coding agents that run on Cursor’s infrastructure in isolated virtual machines. Each agent gets its own full development environment—operating system, terminal, browser, file system, display—completely separate from your local machine. You describe a task (or assign a GitHub issue), and the agent works independently in its VM until it delivers a finished pull request.

The key difference from previous Cursor agent features is the Computer Use capability. Earlier agents could edit files, run terminal commands, and iterate on code. Cloud Agents can do all of that plus interact with graphical interfaces: browsers, desktop applications, and anything else that runs on a screen.

Jonas Nelle, co-head of engineering for asynchronous agents at Cursor, put it bluntly: “They’re not just writing software, writing code—they’re sort of becoming full software developers.”

How Computer Use Works

Computer Use means the agent can see and interact with the display of its virtual machine. It is not a metaphor. The agent literally takes screenshots of its own screen, processes them, decides what to click or type, and executes those actions. Think of it as a developer sitting at a remote desktop, except the developer is an AI model running in a loop.

Here is what that looks like in practice:

1. You describe a task. “Add a dark mode toggle to the settings page and make sure it persists across page reloads.”
2. The agent onboards onto your codebase. It clones your repo, installs dependencies, and understands your project structure inside its isolated VM.
3. It writes the code. Standard agentic coding—edits files, runs linters, fixes errors in a loop.
4. It launches the application. The agent starts your dev server, opens a browser inside the VM, and navigates to the settings page.
5. It tests its own work visually. It clicks the dark mode toggle, verifies the UI changes, refreshes the page, confirms persistence. If something looks wrong, it goes back to the code and fixes it.
6. It records a video demo. The agent captures a screen recording of itself using the feature it just built—clicks, transitions, and all.
7. It submits a PR with artifacts. You receive a pull request containing the code changes, the video demo, screenshots, and logs. You review the PR and the video side by side.

This is the critical innovation. Before Computer Use, AI agents could only verify their work by running tests or reading terminal output. Now they can verify the same way a human QA engineer would: by actually using the software.

What the Agent Can Do on Its VM

• Browse the web — Navigate pages, fill forms, click buttons, read rendered content
• Run and test applications — Start dev servers, interact with the running app in a real browser
• Manipulate desktop tools — Open spreadsheets, use GUI-based applications, interact with any windowed software
• Record video demos — Capture screen recordings showing the feature in action
• Take screenshots — Snapshot specific states for PR artifacts
• Generate logs — Capture terminal output, browser console logs, and test results
• Submit pull requests — Push code and open merge-ready PRs with all the artifacts attached

You can also connect to the agent’s remote desktop yourself to use the modified software or make edits directly—without checking out the branch locally. This is useful for quick manual verification or steering the agent mid-task.

Parallel Agents: 10–20 Tasks at Once

Because each agent runs on its own VM in the cloud, you are no longer limited by your local machine’s resources. Alexi Robbins, co-head of engineering for asynchronous agents at Cursor, described the shift: “Instead of having one to three things that you’re doing at once that are running at the same time, you can have 10 or 20 of these things running. You can have really high throughput with this.”

This changes the unit economics of a developer’s time. Instead of working on one feature while an agent handles another in the background, you can dispatch a batch of tasks across a fleet of agents and spend your time reviewing their output. Monday morning standup becomes: “I assigned 15 issues to agents over the weekend. Here are the 12 PRs that are ready for review.”

Cloud Agents are accessible from anywhere you use Cursor—the desktop app, web interface, mobile, Slack, and GitHub. That means you can kick off agent tasks from your phone during a commute and review the results when you sit down at your desk.

Real-World Adoption: 30%+ of PRs at Cursor Are Agent-Generated

Cursor is not just selling this feature—they are using it internally. More than 30% of the pull requests merged at Cursor are now created by agents operating autonomously in cloud sandboxes. That number, cited in their February 2026 blog post, is a striking data point. It means a meaningful fraction of Cursor’s own product development is being done by the same agents they are shipping to customers.

This kind of internal dogfooding is a strong signal. When a company builds its own product using the tool it sells, the feedback loop is tight and the incentive to fix problems is immediate.

Cursor by the Numbers

The Cloud Agents launch comes at a moment when Cursor’s trajectory is hard to ignore:

• $29.3 billion valuation after a $2.3 billion Series D round (investors include Thrive, a16z, Accel, NVIDIA, Google, and Coatue)
• $1 billion+ annualized revenue—crossed in under 24 months, making it one of the fastest-scaling SaaS companies in history
• Enterprise revenue grew 100x in 2025
• 30%+ of internal PRs generated by autonomous agents

These are not vanity metrics. A billion dollars in ARR means millions of developers are paying for Cursor every month and finding it valuable enough to keep paying. The $29.3B valuation reflects investor confidence that Cloud Agents and Computer Use represent the next major platform shift in developer tools.

Pricing and Access

Cloud Agents are available across Cursor’s paid tiers, though the practical limits differ:

Agent tasks consume credits from your plan’s pool. Each model call within an agent run costs roughly $0.04, and a complex task might involve dozens of calls. If you are running 10–20 parallel agents on the Ultra plan, the $200/month price becomes the cost of what would otherwise be hours of manual development time. For teams and enterprises, the ROI math is straightforward.

How This Compares to the Competition

Cloud Agents with Computer Use does not exist in a vacuum. Every major player in AI coding tools is pushing toward agentic autonomy, but they are taking different approaches. If you want the full landscape, check out our best AI coding tools guide.

Claude Code

Anthropic’s Claude Code is Cursor’s most direct competitor in the agentic space. It runs as a CLI tool in your terminal with a massive 200K-token context window and achieves a 77.2% solve rate on SWE-bench. Claude Code excels at large-scale refactors, multi-file changes, and reasoning through complex codebases.

Anthropic recently launched Claude Code’s Remote Control, which lets you manage terminal-based coding sessions from your phone or any browser. It also supports background agents via --worktree mode for isolated git worktrees.

The key difference: Claude Code is a terminal-first tool that operates through text commands and file edits. It does not have Computer Use—it cannot open a browser, interact with a GUI, or record a video demo. Its strength is deep codebase reasoning and autonomous coding loops. Cursor’s strength is visual verification and end-to-end feature delivery with artifacts. For a deeper dive, see our Claude Code vs Cursor comparison.

GitHub Copilot

GitHub Copilot now has its own coding agent that works asynchronously via GitHub Actions. You assign it an issue, it works in a sandboxed environment, and it opens a draft PR. Copilot also has agent mode inside VS Code for real-time multi-step tasks.

Copilot’s coding agent is conceptually similar to Cursor Cloud Agents but more limited. It does not have Computer Use—it cannot visually test its own work or record demos. It also runs within the GitHub Actions ecosystem, which means you need GitHub repos and Actions configured. Cursor Cloud Agents are platform-agnostic and produce richer artifacts.

Where Copilot wins: broadest IDE support (VS Code, JetBrains, Neovim, Eclipse, Xcode), tightest GitHub ecosystem integration, and enterprise compliance features. At $10/month for Pro, it remains the best value for pure code completion.

OpenClaw

OpenClaw takes a fundamentally different approach. It is an open-source personal AI agent (68,000+ GitHub stars) created by Peter Steinberger that integrates with messaging platforms like Signal, Telegram, Discord, and WhatsApp. Rather than being a coding-specific tool, OpenClaw is a general-purpose agent that can autonomously write code to create new skills for itself.

OpenClaw is free (bring your own API key) and self-improving, but it is not a dedicated development environment. It does not have Cloud Agent infrastructure, Computer Use, or IDE integration. It occupies a different niche: a personal automation agent that happens to be able to code, rather than a coding tool that automates development. Think of it as a Swiss Army knife versus Cursor’s specialized power tool.

Quick Comparison

What This Means for Developers

Cloud Agents with Computer Use represents a shift in how we think about what an AI coding tool does. The progression over the past few years has been clear:

1. 2021–2023: Autocomplete. AI suggests the next line of code as you type. You are the driver; the AI is a smart clipboard.
2. 2024–2025: Agent mode. AI edits multiple files, runs tests, and iterates in a loop. You are the architect; the AI is a junior developer sitting next to you.
3. 2026: Autonomous agents with Computer Use. AI operates its own computer, tests its work visually, and delivers finished features with proof. You are the engineering manager; the AI is a contributor on your team.

This trajectory is what some developers call vibe coding—spending more time describing intent and reviewing results than writing code character by character. Cloud Agents push that concept further by making the review process richer: instead of reading a diff and hoping it works, you watch a video of the agent using the feature it built.

Practical Use Cases

Here are the scenarios where Cloud Agents with Computer Use make the most difference:

• Frontend feature development. The agent builds a component, launches the app, visually verifies the layout, and records a demo. No more “looks good in the diff but breaks on mobile.”
• Bug reproduction and fixing. Assign a bug report, and the agent reproduces the issue in its browser, fixes the code, verifies the fix visually, and submits the PR with before/after screenshots.
• Batch issue processing. Spin up 10–15 agents on a Monday morning, each tackling a different GitHub issue. By lunchtime, you have a stack of PRs with video demos to review.
• End-to-end testing. The agent does not just write tests—it runs the application and interacts with it like a user, catching visual regressions that unit tests miss.
• Documentation and demos. Need a screen recording for your changelog? The agent can record itself walking through the new feature.
• Data pipeline verification. The agent can open spreadsheets, database GUIs, or dashboards to verify that a data transformation produced the correct output.

Limitations and Open Questions

Cloud Agents are impressive, but they are not magic. Some honest caveats:

• Computer Use is slow. Taking screenshots, processing them through a vision model, deciding on actions, and executing them is inherently slower than text-based file editing. Complex visual verification loops add minutes to tasks that would take seconds for a human.
• Cost scales with complexity. Running 20 parallel agents on multi-step tasks with Computer Use will burn through credits quickly. The Ultra plan at $200/month is priced for this, but teams need to model their expected usage carefully.
• Visual understanding has limits. The agent can misinterpret subtle UI states—hover effects, animation timing, responsive breakpoints near boundaries. It is good enough for functional verification, not pixel-perfect QA.
• Security and access control. Agents running in isolated VMs need access to your codebase and potentially to staging environments or APIs. Cursor isolates each VM, but teams need to think about what credentials and access the agents should have.
• Review burden shifts. You spend less time writing code but potentially more time reviewing PRs and watching demo videos. This is a good tradeoff for most teams, but it is a different skill set.

The Bigger Picture

Cursor’s vision, articulated by Nelle, is a future of “self-driving codebases” where agents merge PRs, manage rollouts, and monitor production. Cloud Agents with Computer Use is the first concrete step toward that vision. The agent is no longer just a code generator—it is a software developer that can use a computer.

Whether Cursor, Claude Code, GitHub Copilot, or some combination becomes the default development stack of the next decade is still an open question. What is no longer a question is whether autonomous agents will be a core part of how software gets built. They already are. More than 30% of PRs at Cursor prove it.

The $29.3 billion valuation and $1 billion in annual revenue suggest the market agrees. For developers, the practical takeaway is simple: if you are not experimenting with agentic coding tools yet, you are falling behind. Start with one. See what it can do. Then scale from there.

Last updated: February 2026. Facts and figures verified against Cursor’s official blog, changelog, and CNBC reporting.