Over the past few years, Case-Based Reasoning (CBR) has grown from a Yalecentric view of cognition to a solid sub-area that is supported by wide-spread academic research and industrial development. Unfortunately, the definition of what is and is not CBR remains seriously ambiguous. In this talk, I will look at different takes on CBR and suggest a definition that, oddly enough, doesn’t include the use of "cases ". In particular I will look at: CBR as nothing new. CBR as an alternative cognitive model. CBR as an approach to knowledge engineering. CBR as a new set of assumptions. CBR as a new set of modeling goals. In the end, I argue that the CBR is part of a larger model of long-term agency. This model is distinguished by the view of agents and environments as dynamic entities that change to fit each other over time. Along with arguing for a new view of autonomous agents, this model supports the main tenant of CBR, that reasoning and learning must be linked within any intelligent system.