We present a multi-agent framework based on the ReAct (Reasoning and Acting) paradigm that autonomously derives and robustifies constitutive laws using Regularized Nash Dynamics (R-NaD), the algorithmic core of DeepNash. The approach orchestrates a zero-sum game between a Modeling Agent and a squad of Adversarial Agents, all integrated with sub-scale Representative Volume Element (RVE) simulations. The Modeling Agent interleaves reasoning and acting to formulate symbolic constitutive laws (elasticity, plasticity, damage) within a decision tree structure. Simultaneously, Adversarial Agents use counter-reasoning to identify validity limits, thereby exposing hidden weaknesses by subjecting the RVEs to critical boundary conditions \cite{Paper1, Paper2}. Crucially, we employ R-NaD to stabilize this non-cooperative game. Unlike standard adversarial reinforcement learning, which often suffers from cyclic non-convergence (where model corrections merely shift weaknesses to new areas), R-NaD guides the agents toward a Nash Equilibrium. This guarantees convergence to a constitutive law that is not merely fitted to past data, but is also theoretically robust against the worst-case loading scenarios that adversaries can discover. Numerical examples demonstrate how this regularized dynamic automates the discovery of "unexploitable" material models. Furthermore, we analyze the Pareto front resulting from the AI's conflicting objectives, revealing the intrinsic trade-off between model fidelity and generalization robustness.