We explore the potential to include automatic learning in a design agent by implementing a simple distillation sequencing system, CPD-Soar, within Soar. Soar is an integrated software architecture with a build-in set of mechanisms for exhibiting intelligent behavior, including problem-solving, learning and interaction with the environment. Soar has a number of scientific uses : computer scientists build artificially intelligent agents with Soar as a foundation, and cognitive psychologists use Soar to model human cognition. CPD-Soar illustrates how design-related tasks can be cast within Soar’s framework, hence demonstrating the functioning and potential of its problem-solving and learning mechanisms. This simple example system, which involves computations with real numbers, automatically learns things which are too specific, leading to the hypothesis that the generalization an agent infers from specific examples is strongly dependent upon the model the agent brings to the learning process.