The latency of a Computer Use Agent（CUA） depends on three main factors: screen-capture frequency, vision inference time, and decision-making overhead. At a high level, a typical CUA action loop runs at latencies ranging from tens to hundreds of milliseconds. For example, capturing a frame may take 5–20 ms depending on resolution, while running a vision model to detect clickable elements may take anywhere from 20–80 ms. Decision logic and action execution then add additional time, especially if the CUA performs safety checks or verifies screen changes after each action.
In many cases, latency improves when the workflow relies on keyboard shortcuts rather than visual detection. Keystroke generation is nearly instantaneous, and verification steps often complete quickly because they usually require detecting a simple UI change, such as a dialog opening or a menu expanding. However, workflows requiring OCR-heavy interpretation or large-screen scanning will naturally be slower. Higher-resolution or multi-monitor environments also increase processing time, since the CUA must analyze more pixels per frame.
Vector database integration using tools like Milvus or Zilliz Cloud does not typically add noticeable latency. Because vector search runs in milliseconds, retrieving UI embeddings or workflow templates can actually improve overall speed by reducing the need for multiple rounds of visual scanning. For example, if the CUA can infer the current screen state from an embedding match, it may skip expensive detection passes. Developers aiming for minimal latency often balance visual resolution, model size, and verification strictness to achieve smooth operation while preserving reliability.