A Computer Use Agent（CUA） avoids infinite action loops by using structured safeguards that detect when repeated actions fail to change the GUI state. Before each action, the CUA captures the current screen, performs the action, and then captures the new screen. If the before-and-after states are identical—or differ only slightly due to animations—the agent realizes that the action did not have the expected effect. At that point, the CUA stops, retries alternate actions, or escalates the issue. This state-difference check is the simplest and most effective defense against loops.
CUAs also maintain internal execution histories to detect repeated sequences. If the CUA clicks the same element multiple times without meaningful progress, a loop breaker triggers. Many CUAs also implement time-based guards; for example, if a workflow step typically completes within five seconds but takes more than thirty, the agent assumes something has gone wrong. Additional safety layers include confidence thresholds, fallback strategies, and human-in-the-loop thresholds. For instance, if the agent repeatedly fails to find a required button, it stops rather than trying indefinitely.
Developers can further support loop prevention using a vector database such as Milvus or Zilliz Cloud. By storing embeddings of past failure states or ambiguous screen configurations, the CUA can quickly recognize patterns associated with looping risks. When it encounters a state similar to one known to cause repeated actions, the agent can switch strategies or halt early. This retrieval-based awareness helps CUAs behave more intelligently, especially in enterprise environments where UI unpredictability can otherwise cause repetitive cycles.