Yes, a Computer Use Agent（CUA） can use vector embeddings for on-screen search to improve accuracy in workflows where visual or textual cues are ambiguous. Instead of relying solely on OCR keywords or template matching, the CUA encodes on-screen regions into embeddings that capture semantic meaning. For example, when searching for a “download section,” the embedding for a button labeled “Retrieve File” may still match even if the phrase is different. This semantic matching helps the CUA recognize UI elements more flexibly, especially in enterprise applications with inconsistent wording.
The process usually involves dividing the screen into candidate regions, generating embeddings for each region, and comparing them with embeddings derived from a user instruction or past UI states. Regions with the highest similarity scores are then selected as likely targets. This approach reduces errors in cluttered interfaces where multiple elements share similar shapes or colors. It also helps the CUA interpret icons, labels, and patterns that would be difficult to classify reliably using only pixel-based features.
To enable this, developers typically store embeddings in a vector database such as Milvus or Zilliz Cloud. This allows the CUA to perform fast similarity searches at runtime and retrieve previously seen UI configurations. The vector store becomes a long-term semantic memory, helping the CUA learn from repeated interactions across different applications. Combined with the agent’s visual detection pipeline, embedding-based search provides a powerful method for locating on-screen elements with greater precision and adaptability.