Governed proactive agency enables smarter ai assistants to act appropriately
When Intelligence Becomes Agency: A Theory of Governed, Proactive Agency for Symbiotic AI Systems
Artificial Intelligence
Summary
AI assistants that are always present need to decide for themselves when and how to act instead of just waiting for commands. The authors propose a way to let these assistants decide when it’s okay and useful to step in, while respecting the human user’s control and authority. They introduce a framework that links the assistant’s behavior choices to ongoing authorization and accountability. This helps make sure the assistant acts only when it should and can explain its actions afterward.
What this means in practice
- •For personal assistant developers: Design AI assistants that decide autonomously when and how to help users while maintaining user control and clear accountability.
- •For robotic system integrators: Integrate mechanisms ensuring autonomous robots act only within authorized limits and remain accountable through traceable actions.
A theory result. No direct application yet.
Authors
João Dias Ferreira
Abstract
Persistent AI assistants are intended to extend human attention, memory, and coordination across changing digital and physical environments. To be truly useful they must do more than just act when asked. They must decide on their own whether a situation warrants behavior at all, when it does and in what mode, whether to act, ask, monitor, defer or deliberately refrain. We call this the activation problem. Research on commitment, appraisal, mixed-initiative interaction and delegation each illuminates part of it, but none ties situated activation to continuing authorization and accountability. This paper develops a conceptual and formal framework for governed proactive agency, organizing behavior across time through perception, intent, affective-conative appraisal, constraint, and feedback. It distinguishes autonomous and delegated agency and defines symbiotic agency as delegation under a standing, revocable mandate, with continuing coupling to the principal's situation, calibrated inference of their condition, and bounded personalization. The distinctive contribution is an integrated account linking activation decisions to authorized perception, behavior selection, authority containment, traceable restraint, and constrained adaptation, with behavioral episodes as the unit of analysis. Through an agency classification method, an evaluation framework, proposed benchmark scenarios, and a reference architecture, the account provides a basis for specifying and assessing whether assistance is warranted, timely, authorized, and answerable beyond task completion alone. It is intended to guide the development and evaluation of always-present personal assistants and embodied support systems that augment human capabilities while preserving the principal's authority and judgment.