Agentic artificial intelligence in mechanics of materials: A review of building tailored models on demand

Abstract In mechanics of materials, many powerful methods are accessible only to experts. Constitutive models, for example, describe the relation between stress and strain in materials and are a core ingredient of simulations from structural engineering to biomechanics. Yet building and using such models requires specialized expertise—a barrier for the many potential users trained in other fields. Artificial intelligence based on foundation models, increasingly organized as agents, is beginning to lower this barrier by allowing users to generate tailored models on demand. The same development is underway in many other fields where specialized tools have long been hand‐crafted anew for each task. We call this emerging paradigm the on‐the‐fly generation of physical models : from a plain request, an agent creates a physical model on demand—from scratch and tailored to the problem. Its appeal is accessibility and flexibility. As a consequence, the lasting artifact is no longer a fixed model but the orchestration and prompting of the agents that produce it. This review surveys the concepts behind the paradigm, within mechanics of materials and in neighboring fields, and examines them through two lenses: effectiveness (the accuracy of the generated models) and trustworthiness (their physical admissibility). Trustworthiness is pursued along two routes: deepening the agent's integration under explicit validation, or restraining its autonomy in favor of trusted components. Given the rapid progress of foundation models, and the bitter lesson that general methods leveraging computation tend to prevail, we call for paradigms designed to scale with this progress rather than to compete with it.