Robots have limited computational resources, often due to size, weight, and power (SWaP) constraints, and these resources must perform the reasoning and computation tasks for sensor management, image processing, mapping, planning, control, collaboration, and communications. In a dynamic environment, as the robot executes different reasoning tasks that compete for resources, the constraints on processors, memory, communication bandwidth, and power can interact in unpredictable ways to degrade the robot’s performance. Moreover, in certain situations, the reasoning processes might perform poorly. To avoid these problems, the robot needs a way to monitor and control its reasoning processes. An intelligent robot can use metareasoning (reasoning about reasoning) to improve its reasoning and decision-making processes by adapting them in response to changes in the environment or the system. This makes it safer and more resilient as well. This chapter introduces some key concepts related to robots and autonomous systems. It then discusses the costs and benefits of metareasoning, which is a branch of artificial intelligence (AI). It presents a list of key sources that one should read for more information about metareasoning. Finally, it discusses the systems engineering approach that informs the structure and contents of this book.