{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T14:06:40Z","timestamp":1790172400469,"version":"4.1.0"},"reference-count":74,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T00:00:00Z","timestamp":1790121600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Vet. Sci."],"abstract":"<jats:p>Animal diseases continuously threaten livestock production and public health, yet current surveillance approaches remain largely passive and fragmented. Conventional artificial intelligence models typically function as open-loop predictors, delivering static outputs that fail to accommodate the dynamic operational needs of veterinarians in real-world farm settings. This review explores an emerging paradigm of AI agents for animal disease monitoring, emphasizing how such agents can transition from passive observation to active intervention through a closed-loop perception\u2013decision\u2013feedback architecture. We first classify multi-scale sensing technologies: at the micro-scale, automated molecular diagnostics and biosensors; at the macro-scale, computer-vision-based phenotyping. These technologies collectively provide the data streams required for continuous surveillance. The review then discusses how multi-modal inputs, time-series forecasting, resource-aware decision-making, and reinforcement learning can be integrated into an agent-based workflow. Importantly, most current veterinary research evidence supports only the individual components of this framework, rather than the deployment of fully autonomous, iteratively evolving closed-loop agents in actual field environments. Translating closed-loop AI from a conceptual framework into a reliable veterinary decision-support tool will therefore require further advances in validation, standardization, governance, and human oversight.<\/jats:p>","DOI":"10.3389\/fvets.2026.1968632","type":"journal-article","created":{"date-parts":[[2026,9,23]],"date-time":"2026-09-23T13:31:55Z","timestamp":1790170315000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Closed-loop artificial intelligence agents for animal epidemic prediction and decision support in livestock farming: a review"],"prefix":"10.3389","volume":"13","author":[{"given":"Yuzhi","family":"Wang","sequence":"first","affiliation":[{"name":"Department of Veterinary Medicine, College of Animal Sciences, Beijing Life Science and Technology Research Institute-Zhejiang University Joint R&D Center, Zhejiang University","place":["Hangzhou, Zhejiang, 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