Abstract: To address challenges in health monitoring of data center chiller units, including inflexible diagnostic models, poor cross-scenario transferability, and the disconnect between algorithmic models and operation-and-maintenance knowledge, this paper proposes an AI Agent-based health monitoring method. Inspired by the coordinated strategy of preliminary screening and comprehensive diagnosis in clinical practice, the proposed method establishes a diagnostic framework integrating univariate threshold monitoring with multivariate kernel principal component analysis (KPCA). Based on the DeepSeek-V3 large language model, the AI Agent can automatically plan monitoring strategies, invoke algorithmic tools, and generate executable code. Experiments on the public ASHRAE RP-1043 dataset show that the proposed AI Agent can accurately reproduce the diagnostic logic for seven typical faults, including insufficient condenser water flow, insufficient chilled water flow, and refrigerant leakage. Through a standardized function-calling mechanism, the method improves the transferability and deployment flexibility of diagnostic models across different chiller units. The results further demonstrate that the integration of univariate threshold monitoring and multivariate KPCA can effectively reduce false alarms under varying operating conditions and across different units. Meanwhile, while maintaining diagnostic accuracy, the monitoring code generated by the AI Agent reduces the number of lines of code by approximately 8.6% compared with manual coding. This study provides a scalable and easy-to-deploy automated solution for fault prediction and health management of critical data center infrastructure.