Co-designing AI agents for agricultural policy
Large language models (LLMs)—artificial intelligence systems trained on vast collections of text and other data that can interpret user prompts, summarize information, and generate natural-language outputs—hold the potential to give policymakers site-specific, up-to-date agricultural knowledge and insights.
- Artificial Intelligence
- agricultural policies
By Kristin Davis, Eliot Jones-Garcia, Hlamalani Ngwenya, Arielle Rosenthal, Amanda Grossi, and Mia SpeierMarch 4, 2026
Large language models (LLMs)—artificial intelligence systems trained on vast collections of text and other data that can interpret user prompts, summarize information, and generate natural-language outputs—hold the potential to give policymakers site-specific, up-to-date agricultural knowledge and insights. Yet LLMs have many applications beyond retrieving and summarizing knowledge. They are at the core of AI agents: programs or systems that autonomously perform diverse tasks, engaging with an array of online tools and data. AI agents are extraordinarily flexible; they have been used to design (and in some cases replace) office workflows, to diagnose medical conditions, and to carry out research, among other things.
AI agents have the potential to be important tools in policy development, and in agrifood policy in particular. Figure 1 gives an overview of how this works in concept: the policy agent sends prompts to the LLM; the LLM determines what information is needed; and the agent then carries out the required tool calls—retrieving documents, querying external APIs, or running simulation models—before feeding the results back into the LLM for interpretation. Through this iterative loop, LLM-powered agents can help policymakers explore scenarios, test assumptions, and generate insights in real time.