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This paper introduces Copewell, a multi-agent swarm architecture aimed at enhancing mental wellness support by integrating diverse data sources and emotional mapping to tailor interventions. By addressing the limitations of traditional AI-powered wellness solutions, Copewell employs a multi-source assessment framework and dual-mode intervention delivery to provide immediate, personalized support. The findings highlight how this innovative approach can significantly improve access to mental health resources, particularly in underserved regions, while embedding ethical considerations throughout its design.
Copewell's multi-agent architecture not only personalizes mental wellness support but also operationalizes equity and safety principles from the ground up.
Mental health disorders affect nearly one billion people globally, yet 75% of individuals in low- and middle-income countries receive no treatment due to workforce shortages, cost barriers, and stigma. Current AI-powered wellness solutions predominantly rely on single-mode conversational interfaces that suffer high abandonment rates and fail to provide measurable, immediate relief calibrated to users'dynamic emotional states. This paper presents Copewell, a novel multi-agent swarm system designed to expand access to mental wellness support through human-centered AI principles. Our architecture introduces three technical innovations: (1) a multi-source assessment framework integrating self-reported, physiological, and contextual data to mitigate algorithmic bias; (2) valence-arousal emotion mapping using Russell's Circumplex Model of Affect to route users to specialized AI agents; and (3) dual-mode intervention delivery combining conversational support with evidence-based sensory wellness protocols. We examine the sociotechnical design considerations underlying Copewell's development, including a privacy-first architecture, embedded ethical oversight through a dedicated Ethics Supervisor agent, and participatory design informed by mental health practitioners. Early practitioner engagement and beta deployment inform design decisions and identify directions for future empirical evaluation. This work contributes to responsible AI discourse by demonstrating how technical architecture can operationalize equity and safety principles from inception.
