Computer Use
Computer Use is the capability of AI systems to see and interact with computers through screenshots and simulated input, enabling them to navigate websites and use software tools directly.
Definition
Rather than relying on structured APIs or text extraction, computer-use AI systems take screenshots of applications and websites, understand the visual interface, and interact by simulating mouse clicks and keyboard input. This enables them to use any web or desktop application without specialized integrations.
Computer use represents a new frontier for agents because they can navigate websites visually the way humans do, discover interfaces, interact with interactive elements, and handle unexpected layouts. This works for any website without requiring API documentation or special integration.
Why it matters for AI visibility
Computer-use agents can navigate your website directly, discover content visually, and use your interfaces. Your brand's online presence benefits from clear visual design, intuitive navigation, and fast-loading pages. Agents will rate your site partly on how easy it is to visually parse and navigate through user interface design.
Related terms
AI Agents
AI Agents are autonomous systems that use LLMs to perceive their environment, make decisions, and take actions to accomplish goals. They can use tools, run code, and execute multi-step workflows without human intervention.
AIAgentic Workflows
Agentic Workflows are automated processes orchestrated by AI agents that execute multi-step tasks, make decisions between steps, and adapt to outcomes. They combine language understanding with action execution.
AIFunction Calling (Tool Use)
Function Calling is the capability of LLMs to call external functions or APIs as part of generating responses. It enables agents to take actions, query databases, or access real-time information beyond the model's training.
AIAI Inference
AI Inference is the process of running a trained model on new input to generate output. It is the runtime execution phase, as opposed to training, where the model learns patterns from data.