Agentic AI for Mission Adaptation: A Distributed Cognition Framework for Air Force Operations

Modern air combat operations demand rapid, decentralized decision-making under uncertainty and communication constraints. This paper proposes an agentic AI framework for combat triage and mission adaptation, grounded in distributed cognition. The architecture fuses multimodal data from drones, wearables, and battlefield sensors to assess injury severity, threat levels, and mission status in real time. We implement survival regression models and triage consistency checks trained on large-scale EMS data (NEMSIS), alongside reinforcement learning agents that simulate UAV coordination, casualty evacuation timing, and dynamic route adaptation in adversarial conditions. Human-machine interfaces-such as AR displays and triage conflict alerts-provide explainable, missioncritical recommendations to medics, pilots, and commanders. This research advances a scalable, deployable model of AI-human teaming for the Air Force, supporting distributed mission command and medical autonomy in contested environments.