Towards Intelligent Care: Computational Multi-Agent Architectures for Digital Management of Anxiety Episodes and Personal Well-Being

The future of anxiety management lies in bridging traditional evidence-based treatments with intelligent and adaptive digital platforms. Embedding multi-agent systems capable of real-time mood detection and self-management support represents a transformative step towards intelligent care, enabling users to independently regulate acute episodes, prevent relapse, and promote sustained personal well-being. These digital solutions illustrate how technology can improve accessibility, personalization, and adherence, while establishing the foundation for integrating multi-agent architectures into mental health systems. Such architectures can continuously detect and interpret users’ emotional states through multimodal data, coordinating specialized agents for monitoring, personalization, and intervention. Crucially, they extend beyond passive data collection to provide active, autonomous support during moments of heightened anxiety, guiding individuals through non-pharmacological strategies such as breathing retraining, grounding techniques, or mindfulness practices without requiring immediate professional involvement. By operating in real time, multi-agent systems function as intelligent digital companions capable of anticipating needs, adapting to context, and ensuring that effective coping mechanisms are accessible at critical moments. This paper presents a multi-agent architecture for the digital management of anxiety episodes, designed not only to enhance everyday well-being but also to deliver immediate, personalized assistance during unexpected crises, offering a scalable pathway towards intelligent, patient-centered mental health care.