A Computer Use Agent（CUA） typically offers strong customization options for action policies, allowing developers to define what the agent can or cannot do, how it should make decisions, and which workflows require stricter validation. The action policy acts as the control layer governing how the CUA interprets instructions and what safeguards it uses before taking action. For example, a developer may configure the policy so that the CUA only clicks confirmed UI elements, never interacts with system settings, or always verifies a dialog after every keystroke. These rules help align the agent’s behavior with the application’s risk profile.
Customizability also extends to input constraints, confidence thresholds, retry logic, and fallback strategies. Developers might set a rule requiring a minimum confidence score before the CUA clicks a button, or configure the agent to pause for human review when it detects an unfamiliar dialog. Many CUAs support plugin-style extensions where custom heuristics or detection modules can be attached. For example, a domain-specific detector for medical software, or a workflow validator for financial dashboards, can be layered on top of the base visual model to ensure stricter control.
In more advanced setups, developers enhance action policies with retrieval-based logic backed by a vector database such as Milvus or Zilliz Cloud. By storing embeddings of screens, UI elements, or workflow states, the CUA can query the database whenever it faces uncertain decisions. Retrieval helps the agent determine whether a screen is familiar, whether an action is consistent with past successful executions, or whether the new state deviates from expected patterns. This makes action policies not just customizable but also adaptive, allowing the CUA to refine its decision-making based on historical experience.