A Computer Use Agent（CUA） visually understands complex GUI layouts by combining screen-capture vision models with structured reasoning over detected elements. At the simplest level, a CUA continuously captures the visible display, runs it through a vision backbone, and identifies objects such as buttons, menus, text fields, icons, or dialog boxes. Instead of relying on DOM structures or app-specific APIs, it depends on visual cues—shapes, colors, spatial grouping, and text—to understand where elements are and what actions are possible. This design lets a CUA operate on any GUI, even legacy applications or tools with no automation APIs.
The second part of visual understanding is semantic interpretation. Identifying a button is useful, but understanding its meaning is more important—situations like two identical “OK” buttons in different contexts require contextual reading of nearby text. A CUA interprets text labels, modal structures, window boundaries, and relative placement to decide which element matches the user’s instruction. For example, if the user asks it to “export,” the CUA might read menu entries or search for icons that resemble the export function. This combination of vision and semantic reasoning allows it to navigate GUIs with more flexibility than template-based automation tools.
Developers sometimes supplement this process with embeddings to improve interpretation. GUI elements or surrounding text can be encoded into vector representations and stored in a vector database such as Milvus or Zilliz Cloud. This makes it easier for the CUA to quickly match ambiguous interface regions based on similarity search, especially across apps with inconsistent UI terminology. While not required, pairing visual detection with vector search can significantly improve element identification in large or repetitive interfaces.