Agentic AI systems for scientific discovery

Abstract

Artificial intelligence (AI) agents are emerging in scientific research as systems that can transform the accumulated decisions of scientists into traceable workflows. In fact, modern scientific practice depends on continual coordination of knowledge dispersed across literature, data, theory and experiment, which, however, is often made through manual, case-by-case judgement. This Primer introduces agentic AI systems as a general method to structure this process through natural language interfaces, serving as a computational brain in the laboratory. Central to this framework are practical workflows for in-depth literature search and knowledge mining, orchestration of physics-based simulations, and integration with automated experimental platforms. We begin by describing the foundations of AI agents, including task decomposition, memory, tool use and communication between agents, and by distinguishing single-agent and multi-agent designs, as well as fully autonomous and human-in-the-loop configurations. Equally important are the means by which these systems are evaluated, documented and reported reproducibly. Finally, we examine major bottlenecks, including hallucination, tool misuse, limited interpretability and safety risks, and outline a future in which coordinated networks of agents, instruments and researchers transform scientific discovery from isolated tasks into continuous adaptive experimentation. Agentic artificial intelligence (AI) systems enhance scientific workflows by structuring knowledge coordination through natural language interfaces. In this Primer, Feng et al. examine the challenges and opportunities involved in integrating AI agents in the laboratory.