Pharmacogenomic guidelines are essential for tailoring drug therapy to individual genetic profiles, but current curation workflows are manual, resource‑intensive, time‑bound, and limited in coverage. We introduce an agentic AI system for automated, scalable generation of CPIC-style recommendations using large language models (LLMs) guided by structured evidence. Our modular pipeline retrieves and processes full-text biomedical literature and FDA drug labels, extracts clinically relevant entities with high accuracy (91.9% across 22 articles), aggregates findings across studies, and generates phenotype-specific dosing recommendations for gene-drug pairs. In expert evaluations of 24 random recommendations, our system significantly outperformed leading LLM baselines (GPT-5, Claude, Grok) in clinical clarity and guideline concordance. These results demonstrate the feasibility of using evidence-grounded, agentic AI for end-to-end pharmacogenomic evidence synthesis, offering a path toward broader population coverage, faster updates, and more consistent and explainable decision support.
Zack et al. (Wed,) studied this question.