In agent systems, meta-level reasoning is commonly used in enforcing rationality in the choice of goals and actions performed by an agent, ensuring that an agent behaves as effectively and efficiently as possible. Through meta-reasoning an agent is able to explicitly consider goals before committing to them, and consider courses of action before executing plans. In this paper, we argue that although seldom considered, a flexible meta-level reasoning component is a valuable addition to any agent architecture. We describe such a component for use in BDI architectures, underpinned by a model of motivation and a motivation-based description language, and demonstrate its effectiveness empirically.