Systematic anatomy, as the first core course for medical students, has a direct impact on the learning outcomes of subsequent courses and the formation of clinical thinking ability. Within the intricate anatomy system, the cardiac conduction system features microscopically concealed structures and abstract, dynamic functions, making it challenging for the traditional “classroom lecture + specimen observation” mode to effectively link anatomical structures with physiological functions and clinical issues. This study takes the teaching reform practice at Hebei Medical University as an example, systematically elaborating on the construction background, core content, and implementation path of the AI intelligent agent teaching system. The course construction encompasses integrating high-resolution three dimentional(3D) visualization and dynamic process simulation to intelligently upgrade teaching resources, using a dedicated knowledge graph of the cardiac conduction system as the local knowledge base. It designs intelligent interactive functions such as tiered question generation, real-time error analysis, socratic questioning, and personalized path recommendation. During the course implementation, a three-stage progressive teaching process is constructed: “pre-class exploration, in-class internalization, and post-class expansion,” innovatively applying a “teacher-student-machine” ternary collaborative precision teaching path. Teaching practice shows that artificial intelligent agent-assisted blended teaching can effectively enhance academic performance, increase learning engagement, and promote the transformation of the teacher’s role from knowledge impartation to high-order thinking guidance, providing a scalable practical path for the digital and intelligent transformation of systematic anatomy.