Agentic AI and the rise of in silico team science in biomedical research.

Agentic artificial intelligence (AI) systems are emerging as teams of intelligent computational experts capable of rivaling human performance in labor-intensive tasks, including literature review, hypothesis formulation, data analysis and model interpretation. These systems are poised to accelerate labor-intensive biomedical research by making autonomous decisions based on contextual information and expert feedback. Agentic AI systems have been developed for various applications, including drug discovery, data analysis and biomarker identification; however, several distinct challenges remain for making these systems broadly deployable in biomedical research. Here we discuss three key algorithms and seven foundational building-block characteristics that contribute to the development of agentic AI systems. We highlight their biomedical applications, design considerations and the challenges and opportunities associated with deploying agentic AI systems to advance collaborative scientific research.