The objective of the proposed research is to enable agents to make decisions in real-time using optimization in the loop by utilizing observations of their own states and their environments subject to communication and computational constraints. Time-varying optimization is a natural and effective framework to enable agents to make economical decisions in dynamic settings and under computational constraints. The proposed research will serve to extend the field by (1) enabling agents to track minima of time-varying optimization problems subject to asynchrony in agents' operations and (2) applying time-varying optimization to relevant problems in autonomous control of space vehicles under computational time constraints. Continuing to investigate the time-varying optimization framework will enable us to control systems in real-time to achieve goals that are otherwise infeasible or perform poorly using offline optimization solutions and control laws.