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The intellectual bottlenecks of science are growing with exponential growth in research paper counts, complexity of papers, and a concurrent decline in scientific productivity. The next major breakthroughs will increasingly rely on automation of the stages of scientific discovery. We have made progress on this grand challenge using large language models augmented with access to tools, called scientific agents. We have used these agents for automating literature research, designing molecules, engineering enzymes, and bioinformatics analysis. A major component of this is evaluation against expert researchers in fair comparisons. We have succeeded in training these agents and exceeding human performance across multiple tasks. Finally, I will discuss preliminary work on scientific reasoning models - a new direction of artificial intelligence in the scientific domains.