Yang, LiuyangShan, LiyuCao, XiaolinCui, JinzhaoTong, MichaelNiu, YanZhang, Ting2026-06-112026-06-111439-4456PubMed:41813259ORCID:/0000-0002-9694-9207/work/217150198https://hdl.handle.net/1885/733810661Traditional epidemic intelligence relies heavily on human epidemiologists for data interpretation and reporting, which makes it resource intensive, slow to respond, and vulnerable to variability in professional expertise. To overcome these limitations, we propose an expanded conceptual epidemic intelligence quadripartite framework that extends the classical trinity of (1) surveillance, (2) risk evaluation, and (3) early warning with a fourth pillar, (4) decision support and intervention optimization through AI agents. Acting as 24/7 digital epidemiologists, multiagent systems can integrate heterogeneous signals from multisource surveillance systems, conduct contextual risk evaluation and adaptive forecasting, generate tailored early warnings, and provide actionable recommendations for targeted control—closing the loop between detection and response. Embedding interpretability and mandatory human-in-the-loop oversight enhances trust and accountability. Nonetheless, real-world deployment requires addressing context-specific challenges of data quality, interoperability, robustness, governance, circular reporting, and equity. If designed with transparency, inclusiveness, and resilience, AI agents have the potential to transform epidemic intelligence into a continuously adaptive and globally connected system.This research was funded by the National Key Research and Development Program of China (grant 2023YFC2308701) and Yunnan Fundamental Research Projects (grant 202501AU070024).enPublisher Copyright: © Liuyang Yang, Liyu Shan, Xiaolin Cao, Jinzhao Cui, Michael Tong, Yan Niu, Ting Zhang.artificial intelligence agentsdecision supportearly warningrespiratory infectious diseasesrisk evaluationsurveillance202610.2196/86936105032557363