We propose the 2D Asymmetric Risk Theory (ART-2D), a framework for quantifying systemicfragility using Langevin dynamics. We propose the 2D Asymmetric Risk Theory (ART-2D), a rigorous framework for quantifying systemic fragility in complex adaptive systems. Breaking with the temporal prediction paradigm — precluded by the Efficient Market Hypothesis — we redefine risk monitoring as the detection of structural phase transitions, analogous to financial seismology. We derive a Universal State Vector u20d7Σ(t) from coupled Langevin dynamics between convex Principals and concave Agents, isolating two orthogonal order parameters: Structural Asymmetry (AS), derived via Itô calculus, and Informational Asymmetry (AI), quantified by Kullback-Leibler divergence under Girsanov’s Theorem. The master equation Σ = AS × (1 + λ · AI), calibrated with a universal coupling constant λ ≈ 8.0, produces a scalar metric of proximity to bifurcation. We identify a critical threshold Σcrit = 0.75 separating metastable regimes (Green) from unstable regimes (Red). Empirical validation covering the 2008 Global Financial Crisis, the 2022 Terra/Luna collapse, and COVID-19 hospital saturation reveals a Conditional Risk Amplification Factor (CRAF) exceeding 6.0x in endogenous systems. We extend the model to include spectral contagion in networks and stochastic optimal control, proposing the integration of ART-2D as a physics-based substitute for lagged Basel III macroprudential indicators.