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This paper introduces a neuro-agentic control framework that couples an LLM-based planner (Gemini 2.5 Flash-Lite) with a pre-trained Time-Series Foundation Model (TimesFM) for physics-grounded autonomous defense in industrial IoT. A Counterfactual Physics Injection mechanism ensures only safe, non-hallucinated actions are executed, achieving zero invalid actions and better breach prevention than LSTM and TCN baselines on the SWaT dataset.