Agensuse

Agensuse is a conceptual term used in discussions of autonomous software agents and human–AI collaboration to describe the extent to which an agent's behavior reflects the user's goals and preferences in practice. It treats agency not only as the capacity to act but as the alignment between actions and the user’s intended outcome within a given context.

The term appears in informal AI ethics and HCI literature and online discourse in the late 2010s and beyond. It is not part of a formally standardized vocabulary, and its definition varies across authors. In general, agensuse is invoked to distinguish between merely capable autonomous action and action that remains coherent with what users want or expect.

Agensuse is often contrasted with related concepts such as agency, which emphasizes capability, and alignment, which emphasizes goal prescription. It also relates to predictability and controllability, as high agensuse implies actions that users can anticipate and influence without constant supervision.

Measurement and evaluation of agensuse are debated. Proposed approaches include calculating an alignment score from user feedback, tracking the frequency of overrides, assessing user satisfaction, and evaluating the transparency of decisions. Data sources can include interaction logs, explanation quality, and outcome stability over time.

Applications of the concept focus on designing interfaces and systems where autonomous agents operate with transparent intent and user control. Proponents argue that higher agensuse can improve trust, reduce cognitive load, and mitigate risks in domains such as smart assistants, robotics, and automated workflows. Critics warn that the term may be ill-defined across contexts and susceptible to overinterpretation.

See also: agency, autonomy, alignment, human-in-the-loop, explainable AI.