Automation and artificial intelligence are transforming society, increasing productivity by improving the speed, efficiency, and reliability of conducting complex tasks. Despite these advances, current approaches to autonomous decision-making require enormous computational resources, driving the expansion of data centers and placing growing strain on national electricity and water infrastructure. These high computational demands also limit where autonomous systems can be deployed, preventing low-cost or resource-limited applications from benefiting. This Faculty Early Career Development Program (CAREER) grant supports research that will create novel algorithms to dramatically reduce the computational demands of autonomous decision-making. The research will exploit symmetries, which are repeated patterns that frequently occur in large-scale human-engineered systems built from many similar components, including energy storage systems and logistics networks. By identifying and leveraging these patterns, this project will develop computational tools that enable symmetry-aware algorithms for extreme-scale autonomous decision-making, reducing both computing and memory requirements. Applications such as active battery balancing and resilient manufacturing logistics will strengthen national infrastructure by reducing the economic and environmental costs of computation. All algorithms and tools will be released as open-source software, broadening access and fostering innovation. Educational and outreach activities will promote interdisciplinary collaboration by bringing concepts from autonomy and artificial intelligence into curricula for students in majors outside control systems engineering. Project-based educational materials will be drawn from a broad range of real-world applications to engage students and demonstrate how these emerging tools are reshaping control and automation. This project will exploit symmetry to reduce the computational demands of extreme-scale a