The objective of the proposed research is to develop hardware and systems for autonomous machines and cognitive intelligence, aiming to improve their efficiency, reliability, and trustworthiness. The future of computing can be embodied autonomy — robots seamlessly interacting with humans, agents making trustworthy decisions, and pervasive intelligence advancing toward AGI. However, the growing complexity of autonomous agents imposes significant computing challenges, including increased latency, memory demands, energy consumption, safety concerns, and higher design costs, all of which hinder the widespread adoption of embodied intelligence. My research presents systematic techniques for software-system-hardware cross-layer co-optimization for autonomous agents, highlighting the integration of application discovery, systems thinking, and co-design intelligence. Through case studies in neuro-symbolic intelligence, autonomous navigation, and cooperative planning, I will demonstrate how our tailored computing methodology identifies critical domain characteristics, generates optimized architectures and system designs, and achieves improvements in performance, adaptability, efficiency, and reliability, all while sustainably shaping our digital future and unlocking the potential of embodied autonomous systems.