What is Computer Use? A Complete Guide to AI Agents That Control Computers

🎯 TL;DR

Computer use refers to AI agents that can control computers like humans do — by seeing the screen, moving the mouse, typing on the keyboard, and clicking buttons. Unlike traditional automation that requires pre-programmed scripts, computer use agents understand visual interfaces and can navigate software through natural language instructions. Orgo provides the desktop infrastructure that makes computer use possible at scale.

What is Computer Use?

Computer use is a capability that allows AI models to interact with computer interfaces the same way humans do. Instead of accessing software through APIs or code, computer use agents observe the screen visually, interpret what they see, and execute actions like clicking, typing, scrolling, and navigating menus.

This represents a fundamental shift in how AI systems interact with digital tools. Rather than requiring custom integrations for each application, computer use agents can theoretically work with any software that has a graphical user interface (GUI).

The term "computer use" emerged in 2024 when Anthropic released Claude Computer Use, the first AI model specifically trained to control desktop environments. Since then, multiple research teams and companies have developed similar capabilities, making computer use one of the fastest-evolving areas in AI development.

How Computer Use Agents Work

Computer use agents operate through a continuous perception-action loop. The agent captures a screenshot of the desktop, analyzes the visual information to understand the current state, decides what action to take next, executes that action through simulated mouse and keyboard inputs, and then repeats the cycle.

The core technical components include:

Vision Models process screenshots to identify UI elements like buttons, text fields, menus, and icons. Modern computer use agents use multimodal large language models (LLMs) that can understand both images and text, allowing them to interpret complex interfaces.

Grounding Models translate high-level intentions into precise coordinates. When an agent decides to "click the submit button," the grounding model identifies the exact pixel location of that button on screen.

Action Executors simulate human input by controlling the mouse cursor, generating keyboard events, and managing window focus. These components interact with the operating system's accessibility APIs to perform actions.

Planning Systems maintain context across multiple steps. Advanced agents like Agent S2 use proactive planning, continuously updating their strategy based on new observations after each action. This allows recovery from errors and adaptation to unexpected interface changes.

Why Computer Use Matters

Computer use agents unlock automation for tasks that were previously impossible to automate. Traditional robotic process automation (RPA) tools break when interfaces change, require extensive configuration, and struggle with dynamic content. Computer use agents adapt to interface variations because they understand what they're looking at.

For developers, computer use eliminates the integration tax. Instead of building and maintaining API connections for dozens of tools, teams can deploy agents that simply use the software's existing interface. This dramatically reduces development time and ongoing maintenance costs.

Computer use also democratizes automation. Non-technical users can instruct agents in plain English rather than writing scripts or configuring complex automation workflows. This makes powerful automation accessible to a much broader audience.

The potential impact extends beyond simple task automation. Computer use agents can serve as QA testers that actually use your application, accessibility auditors that verify interfaces work correctly, research assistants that gather information from multiple sources, and development aids that write and test code in real development environments.

Computer Use vs Traditional Automation

Key Technologies Enabling Computer Use

Several foundational technologies have converged to make computer use possible:

Multimodal Language Models combine visual and linguistic understanding in a single model. Models like Claude 3.7 Sonnet, GPT-4V, and Gemini 2.5 can analyze screenshots and generate action plans based on what they see. This eliminates the need for separate vision and reasoning systems.

Vision-Language Models (VLMs) specialized for UI understanding can identify clickable elements, read text from screenshots, understand spatial relationships between interface components, and recognize common UI patterns across different applications.

Screen Capture APIs provide real-time desktop access. Operating systems expose accessibility interfaces that computer use agents leverage to observe screen content and inject input events without requiring direct hardware access.

Virtual Desktop Infrastructure (VDI) enables scalable agent deployment. Platforms like Orgo provide on-demand desktop environments where agents can operate safely, isolated from production systems, with consistent configuration and fast boot times (sub-500ms).

Grounding Models bridge the gap between abstract intentions and concrete actions. When an agent decides to "click the search icon," grounding models identify which pixels on screen correspond to that icon. State-of-the-art systems use mixture-of-experts architectures with specialized models for visual grounding, text selection, and structural element handling.

Real-World Applications

Computer use agents are already being deployed across multiple domains:

Software Testing — Agents execute test cases by actually using applications like human QA testers would, identifying bugs, verifying workflows, and testing edge cases across different screen resolutions and configurations.

Data Collection and Research — Agents navigate multiple websites, extract information, compile data from various sources, and maintain context across long research sessions that might take hours.

Administrative Task Automation — Agents handle form filling, data entry across multiple systems, report generation from dashboard interfaces, and email management that requires reading and responding based on content.

Development Assistance — Agents write code in actual IDEs, run tests and debug failures, deploy applications through web interfaces, and monitor dashboards for errors.

Customer Support — Agents access customer data across multiple tools, execute account changes through admin interfaces, and gather troubleshooting information from internal systems.

Getting Started with Computer Use

To start building with computer use agents, developers need three components: an AI model with computer use capabilities (such as Claude 3.7 Sonnet, GPT-4o, or open-source alternatives like Agent S2), a desktop environment where the agent can operate, and a way to send instructions and receive results.

Orgo simplifies this setup by providing instant desktop environments with pre-configured computer use tools. The basic workflow involves creating a virtual computer, sending natural language prompts or direct API calls, and monitoring agent actions in real-time through a dashboard.

Here's a minimal Python example using Orgo:

from orgo import Computer
 
computer = Computer()
computer.prompt("Open Firefox and search for weather in Seattle")

This code creates a virtual desktop, instructs Claude to execute the task, and handles all the screenshot-action loop complexity behind the scenes.

For developers building custom agents, Orgo also exposes lower-level APIs for direct control:

computer.screenshot()           # Capture current state
computer.left_click(450, 320)   # Click at coordinates
computer.type("search query")   # Type text
computer.key("ctrl+enter")      # Press key combination
computer.bash("ls -la")         # Execute shell command

These primitives give you full control to implement custom agent logic while Orgo handles the desktop infrastructure.

The Future of Computer Use Agents

Computer use agent performance has improved rapidly. In early 2024, the best models achieved around 15% success rates on complex tasks measured by the OSWorld benchmark. By late 2025, state-of-the-art agents like Simular's Agent S2 reached 34.5% success rates, with human-level performance (approximately 72%) now seeming achievable in the near term.

Several developments will accelerate this progress:

Multimodal models continue improving at vision tasks, understanding increasingly subtle UI elements and maintaining better context over longer interaction sessions.

Specialized grounding models are getting faster and more accurate, reducing the latency between seeing an element and clicking it correctly.

Proactive planning architectures enable agents to reason about multi-step tasks, recover from failures gracefully, and optimize action sequences for efficiency.

Infrastructure platforms like Orgo are making computer use more accessible by providing scalable, fast, and affordable desktop environments optimized for agent workloads.

The implications are significant. As computer use agents approach human-level performance, entire categories of knowledge work that currently require human-in-the-loop interaction with software interfaces may become automatable. The computer use paradigm means AI can leverage the existing software ecosystem without requiring every application to expose AI-friendly APIs.

Summary

Computer use represents a new category of AI capability where models interact with computers through visual interfaces rather than code. By combining vision understanding, reasoning, and action execution, computer use agents can automate tasks across any software with a GUI.

The technology has matured rapidly from experimental demos to production-ready tools. Platforms like Orgo provide the infrastructure developers need to deploy computer use agents at scale, with instant desktop environments, sub-second boot times, and flexible APIs for both prompt-based and programmatic control.

For developers building AI applications, computer use eliminates integration complexity and unlocks automation opportunities that were previously impractical. As the technology continues improving toward human-level performance, computer use agents will become fundamental primitives in the AI development toolkit.

Related Resources

• Anthropic Claude Computer Use Documentation
• OSWorld Benchmark Leaderboard
• Agent S2 Research Paper
• Orgo Computer Use Guide

Glossary

Computer Use Agent — An AI system that controls computers by observing screens and simulating human input (mouse and keyboard actions), rather than using APIs or code.

Grounding Model — A specialized AI model that translates high-level intentions into precise screen coordinates, identifying where to click or type on a visual interface.

Multimodal LLM — A large language model capable of processing both text and images, enabling it to analyze screenshots and generate action plans.

Perception-Action Loop — The continuous cycle where an agent observes the screen state, decides on an action, executes it, and then observes the result to inform the next action.

Virtual Desktop Infrastructure (VDI) — Technology that provides on-demand, isolated desktop environments where computer use agents can operate safely without affecting production systems.

GUI (Graphical User Interface) — The visual interface of software applications that humans interact with through mouse and keyboard, as opposed to command-line or API interfaces.

Vision-Language Model (VLM) — An AI model trained to understand both images and text, essential for computer use agents to interpret what they see on screen and relate it to user instructions.

Proactive Planning — An agent architecture that continuously updates strategy based on new observations after each action, allowing recovery from errors and adaptation to changing conditions.