Control systems embodying artificial intelligence (AI) techniques tend to be “reactive” rather than “deliberative” in many application areas. There arises a need for systems that can sense, interpret and deliberate with their actions and goals to be achieved, taking into consideration continuous changes in state, required service level and environmental constraints. The requirement of such systems is that they can plan and act effectively after such deliberation, so that behaviourally they appear self-aware. In this paper, we focus on designing a generic architecture for autonomic systems which is inspired by the Human Autonomic Nervous System. Our architecture consists of four main components which are discussed in the context of the Urban Traffic Control Domain. We also highlight the role of AI planning in enabling self-management property of autonomic systems. We believe that creating a generic architecture that enables control systems to automatically reason with knowledge of their environment and their controls, in order to generate plans and schedules to manage themselves, would be a significant step forward in the field of autonomic systems.