Scaling Agentic AI in Healthcare: Challenges, Design Principles, and Deployment Strategies

Abstract

Autonomous agent AI systems, which are agentic AI systems that have the ability to operate autonomously, are transforming healthcare in streamlining clinical and administrative operations. Yet, scaling such systems to real healthcare setting comes with problems concerning data heterogeneity, safety, regulation, and trust. As this paper analyses these barriers, it also suggests explanation-based design principles, modularity-based design principles, and human-based oversight-based design principles. we explore approach of deployment (via hybrid RAG models, real-time orchestration and compliance layer). We use case study analysis to assess system performance: in triage, radiology, ADE detection and in dementia-care. Our results provide practical implications and a bespoke platform through which agentic AI may be securely and saleable implemented into significant healthcare processes.