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This paper presents a framework for integrating Agentic AI into strategic foresight. Traditional scenario planning is constrained by cognitive biases and linear extrapolation, while standard LLMs introduce hallucination and lack causal rigor. We propose a recursive Human-AI Teaming methodology that integrates System 2 Reasoning for causal verification, Multi-Agent Systems for adversarial debate, and Structural Causal Models to stress-test escalation pathways — illustrated through an Iranian nuclear escalation case study. We address classified-environment realities through Sovereign Edge Deployment and Federated Learning, with protocols to counter adversarial data poisoning. The result is a mathematically grounded methodology for anticipating non-linear geopolitical shifts.