A Computer Use Agent（CUA） improves its accuracy through a combination of supervised training, feedback-driven corrections, and retrieval-assisted learning. At the core, CUAs rely on vision models trained on screenshots containing UI elements like buttons, menus, icons, and dialog boxes. These models improve when they are trained on more diverse screens covering multiple operating systems, software categories, themes, and resolutions. Developers can contribute additional labeled data showing correct element boundaries, successful actions, or ambiguous states, helping the model better generalize to real-world conditions.
Beyond foundational vision training, CUAs often benefit from reinforcement-like feedback. When the agent makes a mistake—such as clicking the wrong element—developers can annotate the error and store the correct interpretation. This correction dataset is then used to adjust policies or retrain classifiers detecting UI elements or context cues. Over time, this continuous feedback loop reduces misclicks and improves action reliability. Some CUAs also track confidence scores and action outcomes, using them to refine internal heuristics, improve retry logic, or adjust how the agent evaluates screen changes.
For long-term improvement, developers frequently use a vector database such as Milvus or Zilliz Cloud to store embeddings of screens and workflows. When the CUA encounters a new UI state, it can retrieve similar past states and leverage the stored outcomes to choose more reliable actions. This retrieval-enhanced approach acts like a memory system, meaning the CUA gets better the more environments it works in. It reduces the need for exhaustive supervised training while enabling the agent to adapt quickly to evolving software interfaces. As the database grows, the CUA’s accuracy improves naturally through accumulated experience.