At Beijing Friendship Hospital, affiliated with Capital Medical University, Academician Wang Zhenchang's laboratory, in collaboration with several universities and research teams, has identified and addressed the 'scalability paradox' in medical AI. This paradox arises when models perform well in testing environments but fail to translate that success into meaningful clinical outcomes. The team has thoroughly examined the four primary causes of this issue and introduced an innovative development paradigm: 'governed agent-based clinical intelligence.' This paradigm features a three-tier architectural framework—comprising the 'data layer, engine layer, and application layer'—alongside a comprehensive governance security mechanism that oversees the entire process. It categorizes agent autonomy into three distinct levels (A to C) according to action attributes, firmly establishes that the core responsibilities for diagnosis and treatment rest with physicians, and delineates a clear, phased roadmap for implementation. This approach offers a robust theoretical foundation, technical architecture, and practical industry solutions to facilitate the standardized, secure deployment, and widespread adoption of medical AI agents.