What Is Computer Use? How AI Models Control Your Screen

"Computer use" has become one of the most talked-about capabilities in AI over the past year. But the term gets thrown around loosely, and it covers several very different approaches. If you are trying to understand what computer use actually means, how it works, and which approach is best for your needs, this guide will cut through the confusion.

At its core, computer use means an AI model that can operate a computer the way a human does - clicking buttons, typing text, navigating between applications, reading what is on screen, and making decisions about what to do next. Instead of just generating text or images, the AI takes actions in the real world through your computer's interface.

This is fundamentally different from traditional AI tools. A chatbot gives you text. An AI image generator gives you pictures. A computer use agent does things - it fills out forms, sends emails, organizes files, and completes workflows across multiple applications without you touching the keyboard.

The Three Approaches to Computer Use

Not all computer use is created equal. There are three main technical approaches, and each comes with very different tradeoffs in terms of speed, accuracy, and capability.

1. Screenshot Analysis (Vision-Based)

This is the approach that got the most attention when Anthropic introduced Claude Computer Use in late 2024. The basic idea is simple: take a screenshot of the screen, send it to a multimodal AI model, and let the model decide what to click or type based on what it sees.

How it works:

1. The agent captures a screenshot of the current screen
2. The screenshot is sent to a vision-capable AI model (like Claude or GPT-4o)
3. The model analyzes the image and identifies UI elements - buttons, text fields, menus, links
4. The model decides what action to take and specifies pixel coordinates
5. The agent moves the mouse to those coordinates and clicks (or types)
6. A new screenshot is taken, and the cycle repeats

Pros:

• Works with literally any application, since it only needs to see what is on screen
• No special integrations or APIs required
• Can handle visual elements like charts, images, and custom UI components

Cons:

• Slow. Each action requires taking a screenshot, sending it to the cloud, waiting for analysis, and executing the action. A single click can take 3 to 10 seconds.
• Error-prone. Pixel coordinates are imprecise. The model might click slightly off-target, especially with small buttons or densely packed interfaces.
• Expensive. Every screenshot sent to a vision model costs API tokens. A complex workflow involving 50 actions could cost several dollars in API calls.
• Resolution-dependent. If your screen resolution changes or windows resize, the coordinates change and the agent can misclick.

Screenshot-based computer use works, but it is the slowest and most fragile approach. It is like asking someone to operate your computer while looking at it through a security camera - they can do it, but it is not efficient.

2. Accessibility API

This approach uses the operating system's built-in accessibility framework to understand and interact with the user interface. Operating systems like macOS, Windows, and Linux all provide accessibility APIs originally designed to support screen readers and assistive technology. These APIs expose the structure of the UI - every button, text field, menu, and label - in a programmatic format.

How it works:

1. The agent queries the accessibility API to get a structured tree of all UI elements on screen
2. Each element has properties: its role (button, text field, menu), its label, its position, its state (enabled, focused, selected)
3. The agent reads this tree to understand what is on screen
4. It identifies the element it needs to interact with by name or role
5. It performs the action using the accessibility API itself - clicking buttons, entering text, selecting menu items

Pros:

• Fast. No screenshots or cloud processing needed for UI understanding. The agent knows immediately what elements are available.
• Accurate. Instead of guessing pixel coordinates, the agent interacts with specific, named UI elements.
• Works across all native applications on the operating system
• Does not depend on screen resolution or window positioning

Cons:

• Not all applications expose their UI well through accessibility APIs. Web applications running in browsers are partially exposed, but custom widgets and canvas-rendered elements may not be.
• Platform-specific. Accessibility APIs differ between macOS, Windows, and Linux, so agents need platform-specific implementations.
• Some applications have poor accessibility support, which means the agent has limited visibility into their interface.

The accessibility API approach is what Fazm uses for desktop application control. It is faster and more reliable than screenshot analysis because the agent has direct knowledge of UI structure rather than trying to interpret images. You can read more about this approach in our post on full desktop control with accessibility APIs.

3. DOM Control (Browser-Specific)

For web applications specifically, there is a third approach: directly accessing the Document Object Model (DOM) of the browser. The DOM is the underlying structure of a web page - every element, its properties, its styles, and its content.

How it works:

1. The agent connects to the browser through a debugging protocol (like Chrome DevTools Protocol)
2. It reads the DOM to understand the page structure - forms, buttons, links, text content
3. It interacts with elements directly through the DOM - clicking buttons, filling inputs, extracting text
4. It can also execute JavaScript to handle complex interactions

Pros:

• Fastest approach for web applications. DOM interactions happen at native browser speed.
• Most accurate. Elements are identified by their CSS selectors, IDs, or other unique identifiers - no guessing.
• Can access data that is not even visible on screen (hidden elements, data attributes, API responses)
• Works consistently regardless of screen size, resolution, or window position

Cons:

• Only works for web applications. Cannot control desktop applications.
• Requires browser integration, which means the agent needs to run in or connect to the browser
• Some websites actively prevent automated interaction (anti-bot measures, CAPTCHAs)

DOM control is the gold standard for browser automation. It is what tools like Playwright and Puppeteer have used for years. Fazm uses DOM control for browser-based tasks, combined with accessibility API control for desktop applications. This hybrid approach gives you the best of both worlds - fast, accurate browser automation and reliable desktop application control.

Who Offers Computer Use Today

Several major players are building computer use capabilities, each with a different approach and different limitations.

Anthropic Claude Computer Use

Anthropic introduced computer use with Claude 3.5 Sonnet in late 2024 and has continued developing it through 2025 and into 2026. Their approach is primarily screenshot-based - Claude takes screenshots, analyzes them, and outputs mouse and keyboard actions.

Claude Computer Use is impressive in its ability to understand complex visual interfaces, but it inherits all the limitations of the screenshot-based approach. It is slow, expensive, and sometimes inaccurate with pixel targeting. It also requires cloud processing, which means your screen content is sent to Anthropic's servers.

For a detailed comparison, see our Claude Computer Use comparison page.

OpenAI CUA (Computer-Using Agent)

OpenAI's Computer-Using Agent, which powers tools like Operator, takes a similar screenshot-based approach. GPT-4o analyzes screen captures and determines actions.

OpenAI's implementation focuses heavily on web browsing use cases and includes some browser-level integration for improved accuracy in web contexts. But for desktop applications, it falls back to the screenshot approach with the same speed and accuracy limitations.

You can see how it compares on our OpenAI Operator comparison page.

Fazm

Fazm takes the hybrid approach described above - accessibility API for desktop applications and DOM control for browsers. This makes it significantly faster than screenshot-based agents for most tasks.

Because Fazm runs locally on your Mac, there is no cloud processing of your screen content. The AI model runs on your machine or connects to an API of your choice (bring your own key), but the screen understanding happens entirely locally through the accessibility API and browser protocols.

The tradeoff is that Fazm is currently macOS-only, while screenshot-based approaches are theoretically platform-agnostic (though implementations are often platform-specific in practice).

Others in the Space

Several other companies and projects are working on computer use:

• Google Project Mariner focuses on browser-based computer use with Chrome integration
• Simular AI is building desktop agents with vision-based approaches
• Manus AI offers cloud-based computer use in virtual environments
• Various open-source projects are building on top of Claude and GPT-4o's vision capabilities

The space is moving fast, with new entrants and capability improvements appearing regularly.

Speed Comparison - Why Approach Matters

To make the difference concrete, let's compare how long a simple task takes with each approach. The task: open a browser, navigate to a website, fill in a contact form with 5 fields, and click submit.

| Approach | Time per Action | Total Time (approx. 15 actions) | API Cost | |----------|----------------|--------------------------------|----------| | Screenshot-based | 3-10 seconds | 45-150 seconds | $0.50-$2.00 | | Accessibility API | 0.1-1 second | 1.5-15 seconds | $0.01-$0.10 | | DOM control | 0.05-0.5 seconds | 0.75-7.5 seconds | $0.01-$0.10 |

The difference is dramatic. A task that takes two minutes with screenshot analysis takes under 10 seconds with DOM control. Over the course of a workday with dozens of automated tasks, this adds up to a massive difference in productivity.

Cost is also significant. Screenshot-based approaches send large image files to cloud APIs for every single action. Accessibility API and DOM approaches send small text queries, which cost a fraction as much.

Accuracy and Reliability

Speed is important, but reliability matters more. An agent that is fast but makes mistakes is worse than one that is slow but accurate.

Screenshot-based approaches have inherent accuracy limitations. The model needs to identify the correct pixel coordinates for a button that might be 50 pixels wide. If it is off by 30 pixels, it clicks the wrong thing. If the window has moved or resized since the screenshot was taken, coordinates may be wrong. If there are similar-looking buttons near each other, the model might click the wrong one.

Accessibility API approaches identify elements by their semantic properties - "the button labeled Submit" rather than "the clickable area at coordinates 450, 320." This means they are inherently more accurate for native applications.

DOM control is the most accurate for web content, because elements are identified by unique selectors rather than visual appearance or coordinates.

In practice, the reliability difference compounds. A 95% accuracy rate per action sounds good until you realize that a 20-step workflow has only a 36% chance of completing without any errors (0.95^20 = 0.36). A 99% accuracy rate per action gives you an 82% success rate for the same workflow (0.99^20 = 0.82). And 99.9% gives you 98%.

The Future of Computer Use

Computer use is still in its early stages, and the technology is improving rapidly. Here is what to expect in the near term.

Hybrid Approaches Will Win

The future is not one approach versus another - it is intelligent combination. Use DOM control for web apps because it is the fastest and most accurate. Use accessibility APIs for native desktop apps. Fall back to screenshot analysis for the rare cases where neither API is available (like controlling a remote desktop session or a game).

Speed Will Keep Improving

As models get smaller, faster, and cheaper, even screenshot-based approaches will become more practical. But the fundamental advantage of structured approaches (accessibility API and DOM) will remain - they simply have less work to do per action.

More Applications Will Support Agents

As computer use becomes more common, application developers will increasingly build agent-friendly interfaces. Better accessibility support, standardized automation APIs, and explicit agent integration points will make all approaches more reliable.

Privacy Will Become a Differentiator

As more people realize that screenshot-based cloud agents are sending images of their screen to remote servers, privacy-conscious users and enterprises will gravitate toward local-first approaches. Your screen content is highly sensitive - it contains emails, financial data, passwords, and personal information. Keeping that data local is not just a nice-to-have; for many use cases, it is a requirement.

Choosing the Right Approach

If you are evaluating computer use agents, here is a simple framework:

• If you primarily need browser automation, look for agents with DOM control capabilities. They will be faster and more accurate than anything screenshot-based.
• If you need desktop application control on macOS, look for agents using the accessibility API, like Fazm.
• If you need cross-platform desktop control and speed is not critical, screenshot-based approaches offer the broadest compatibility.
• If privacy matters (it should), prefer local-first agents that do not send your screen content to cloud servers.
• If you need enterprise deployment, look for agents with human-in-the-loop controls, audit logging, and configurable security policies.

Computer use is one of the most significant shifts in how people interact with AI. It takes AI from "tool that gives advice" to "tool that takes action." Understanding the technical approaches - and their tradeoffs - is essential for making a smart choice about which agent to trust with your work.