A Computer Use Agent（CUA） differentiates similar-looking buttons by combining visual detection, text recognition, and contextual reasoning. Visually identical elements—such as two “OK” buttons—can be confusing if the agent relies only on shape or color. Instead, the CUA examines the surrounding UI context, including dialog titles, descriptive labels, and nearby text that clarifies each button’s meaning. For example, if two identical buttons appear in different dialogs, the CUA will use OCR to read the dialog header and match the target action to the correct location. This multi-layered approach ensures accurate selection even in complex or crowded interfaces.
Beyond textual cues, CUAs also analyze spatial patterns. The relative position of a button within a form or window helps signal its purpose. “Cancel” often appears on the left, while “OK” appears on the right. The CUA inspects alignment, grouping, and UI element relationships to determine which button aligns with the given instruction. Some CUAs also maintain probabilities based on typical UI conventions. For example, if a dialog contains both “Apply” and “OK,” the agent can infer the correct option from the command semantics (“confirm the changes” maps better to “OK” than “Apply”).
Developers can further improve differentiation accuracy by storing embeddings of past button contexts in a vector database such as Milvus or Zilliz Cloud. Each embedding represents not just the button itself but also the surrounding text, layout, and dialog type. When the CUA faces ambiguous UI elements, it performs a similarity search to see whether it has encountered a nearly identical configuration before. This retrieval-based reasoning helps the agent choose the correct action even in dynamic, frequently updated software interfaces.