The data-driven AI catalysis agent (FIG. ), integrating "big data platforms + AI methods + precise theoretical modeling," is revolutionizing catalyst development. This innovative framework accelerates the discovery of high-performance, cost-effective catalysts by optimizing the entire process from material design to performance evaluation, significantly reducing costs and time. The DigCat (digital catalysis platform) can serve as the "brain" of a modern AI catalysis lab. Integrating big data and AI, DigCat encompasses more than 400,000 experimental performance data for electro-, thermo-, and photocatalysts, as well as over 300,000 catalyst lution reaction (HER), ammonia synthesis, carbon monoxide/dioxide reduction reaction (CORR/CO 2 RR), hydrogen peroxide synthesis, ozone synthesis, hydrogen and ammonia oxidation, nitrogen and ammonia oxidation, electrocatalytic hydrogenation, epoxide synthesis, urea synthesis, methanol/ethanol reforming for hydrogen production, and a wide variety of organic catalytic reactions. These data encompass materials such as transition metals and their alloys, metal X-ides (e.g., metal oxides, nitrides, carbides, sulfides, and phosphides), single-/multi-atom catalysts, carbon-based materials, 2D materials, and perovskites.