Deploying learned robot manipulation policies in industrial settings requires rigorous pre-deployment validation, yet exhaustive testing across high-dimensional parameter spaces is intractable. We present ROBOGATE, a deployment risk management framework that combines physics-based simulation with a two-stage adaptive sampling strategy to efficiently discover failure boundaries in the operational parameter space. Stage 1 employs Latin Hypercube Sampling (LHS) across an 8-dimensional parameter space; Stage 2 applies boundary-focused sampling concentrated in the 30-70% success rate transition zone. Using NVIDIA Isaac Sim with Newton physics, we evaluate a scripted pick-and-place controller across four robot embodiments -- Franka Panda (7-DOF), UR3e (6-DOF), UR5e (6-DOF), and UR10e (6-DOF) -- totaling over 50,000 experiments. Our logistic regression risk model achieves AUC 0.780 and identifies a closed-form failure boundary equation. We further benchmark eight VLA (Vision-Language-Action) policies, including a fine-tuned NVIDIA GR00T N1.6 (3B) trained on LIBERO-Spatial for 20K steps. The same checkpoint achieves 97.65% success rate on LIBERO (MuJoCo) but 0% on RoboGate's 68 industrial scenarios in NVIDIA Isaac Sim -- a 97.65 percentage point cross-simulator gap on a single model that underscores the deployment validation challenge. Inspired by the validation-layer paradigm NVIDIA codified for quantum computing with Ising, ROBOGATE provides this validation layer for Physical AI. Open-source.