Agentic Artificial Intelligence for Reproducible Human-in-the-Loop Environmental Health Research

Abstract

Agentic artificial intelligence (AI) systems that are capable of planning and executing multi-step analytical tasks are increasingly available to environmental health researchers, but their reliability in real-world practice has not been fully explored. This paper describes a human-in-the-loop agentic framework for environmental health research, involving the review, verification, and correction of AI-generated data analysis code and results at each step - a process that mirrors the mentorship structure of traditional research teams. This approach offers a path toward rigorous, reproducible use of agentic AI in routine data-rich environmental health research. We illustrate this framework through a case study analyzing nitrogenous organic contaminants at U.S. Superfund sites, a chemical family linked to the emerging tire-derived contaminants 6PPD and 6PPD-quinone. Using an agentic large language model to generate R code for data filtering, spatial mapping, and cluster analysis, we document instances where initial agentic AI outputs benefited from a human-in-the-loop process to produce more rigorous and reproducible results. We conclude that effective use of agentic AI requires both domain expertise to frame questions and evaluate outputs, and coding literacy to guide the AI's approach, while outlining future opportunities for agentic AI workflow to advance the environmental health sciences.