Science advances by formulating and testing hypotheses, collecting and analyzing data, and drawing conclusions—yet much of a scientist’s time is spent in tasks such as coding analyses, writing and revising text, reviewing the literature, and learning new concepts. Can recent advances in AI help reclaim some of that time? In this talk, I will show how large language models and AI agents may help scientists with these tasks. I will present Denario, a complex, publicly available, multi-AI-agent system designed to function as a research assistant. Developed and evaluated by a diverse team of scientists, mathematicians, and philosophers, Denario is an interdisciplinary tool capable of generating ideas, searching the literature, developing research plans, writing and executing code, crafting plots, drafting and reviewing scientific papers. I will present and discuss some of the documents generated by Denario in disciplines such as astrophysics, biology, biophysics, biomedical informatics, chemistry, machine learning, material science, mathematical physics, medicine, neuroscience, planetary physics, and quantum physics. I'll finalize discussing the benefits and perils of automating (parts) of science.