Design is the process by which solutions get developed to solve social challenges, and its products can be seen across our world from toothbrushes to computers to spaceships. Conceptual design is an early phase of design where an initial candidate solution gets developed. Analogical design is a form of design where knowledge from some known source case is transferred into the design solution, leveraging knowledge not directly related to solving the problem at hand. Biologically inspired design is a real-world form of analogical design where source cases come from nature. In all of this, there is a need for design revision. The output (the candidate design solution) likely begins in an imperfect state and needs to be revised to ensure it properly and completely solves the design problem. Additionally, the designer's understanding of any source cases or prior solutions used could also exist in an imperfect state and should too be revised since they are used in reasoning. Thus, any flaws in this knowledge could negatively impact the process and/or outcome. This is especially important in an interdisciplinary context like biologically inspired design where a given design practitioner may have expertise in only some of the involved domains. To support design revision, this dissertation presents an AI agent that can help a design practitioner engage with a process of incremental design revision. In this hypothesized, iterative process, a practitioner externalizes their knowledge of a design concept in a functional model, gives this model to the AI agent that evaluates the model, and then receives feedback from the agent. The practitioner can then decide to revise their model based on this feedback, and then can restart the cycle or conclude it. Specifically, this dissertation addresses the task of model evaluation to support incremental design revision in the context of the conceptual phase of biologically inspired design. It presents the Design Evaluation through Simulation and Comparison (DESC) AI agent, a ...