Agents are all you need: Pioneering the use of agentic artificial intelligence to embrace large language models into...

Large language models (LLM) hold significant promise to transform dairy science by enhancing research interpretation, supporting decision making, and improving knowledge dissemination. However, without proper systematic design, LLM may generate irrelevant or factually inaccurate responses for domain-specific questions. Moreover, most existing LLM and related tools are not tailored to the needs of the dairy domain, limiting their practical application within the field. To demonstrate the feasibility and practical value of embracing LLM in dairy science, we developed a 2-component agentic system: (1) a decision-support chatbot grounded in the Journal of Dairy Science (JDS) for science-backed insights and (2) a natural language interface for interacting with academic models and visualizing prediction results. All publicly available JDS abstracts and associated metadata dating back to 1917 were compiled using the PubMed application programming interface, forming a scientific knowledge base that enables the chatbot to answer user questions. A retrieval-augmented generation framework was implemented to ensure that responses generated by LLaMA (a LLM developed by Meta) were well-grounded in peer-reviewed literature, with the 5 most relevant sources cited alongside each answer. To address questions beyond the coverage of JDS literature, a web search agent was incorporated into the system to retrieve supplementary information from external online sources. Grading agents, powered by DBRX (a LLM developed by Databricks), were incorporated to evaluate the credibility and relevance of LLM-generated content to mitigate the risk of misinformation or hallucinated responses. The second component of the system facilitates natural language interaction with MilkBot, a published Bayesian milk yield prediction model. After users submit questions in plain language, the system converts the question into model parameters for MilkBot, executes the model prediction, and uses the predicted output to generate visualizations. This work demonstrates the capability of LLM to serve as intuitive, user-friendly interfaces for dairy-specific models. To our knowledge, this is the first chatbot prototype that integrates large-scale information from scientific literature, web-based resources, and academic models, along with self-evaluation capability, to provide dairy-specific insights to scholars, consultants, and farmers. However, challenges remain to realize the full value of LLM-assisted decision making, such as the lack of region-specific data to tailor the answers to the local circumstances, the need for more robust measures to protect data security and privacy, and the need to integrate additional functions to enable more comprehensive decision support.