A Computer Use Agent（CUA） stores on-screen context by capturing screenshots and extracting structured information from them—such as detected UI elements, recognized text, bounding boxes, and inferred intent. These pieces of structured data form the agent’s short-term memory, allowing it to understand what has changed between frames. For example, if the CUA opens a menu, it records the new elements that appear and uses that knowledge to choose the next action. This information is often stored temporarily in memory for the duration of the session.
For longer-term recall, a CUA may store embeddings of screen states, workflows, or UI components. These embeddings encode the meaning and structure of what the CUA perceived, allowing it to later compare new screens to past ones. This is especially helpful when operating across multiple applications or complex enterprise tools that frequently change layout or themes. If the CUA encounters a screen that looks similar to one it has seen before, it can retrieve the associated context—such as which buttons were safe to press or which actions succeeded last time—making its behavior more stable and predictable.
This longer-term recall is often implemented using a vector database such as Milvus or Zilliz Cloud. The CUA stores embeddings representing important screen states or UI features, then performs similarity search whenever it needs to recognize a familiar context. For example, if the agent sees a new error dialog, it can compare it to past dialogs stored in the database to determine whether it is new or a known issue. This approach allows CUAs to accumulate practical experience across sessions and improves reliability without requiring manual rules or hard-coded workflows.