Train the Trainers -- An Agentic AI Framework for Peer-Based Mental Health Support in Battlefield Environments

TL;DR

This paper introduces a novel agentic AI-enabled peer support framework for military personnel, enhancing mental health care in resource-constrained battlefield environments through scalable, supervised AI agents.

Contribution

It proposes a new Train-the-Trainers model combined with an agentic AI platform to provide scalable, supervised psychological support in austere military settings.

Findings

Developed a functional prototype with the U.S. Army Health Center.

Demonstrated AI agents can assist in symptom triage and support interventions.

Aimed to reduce evacuations and improve mental health continuity.

Abstract

Modern military operations expose soldiers to sustained psychological stress, leading to acute reactions, post-traumatic stress symptoms, and other mental health issues. Although the U.S. Department of Defense offers evidence-based therapies, access to trained professionals in forward-deployed and contested environments is limited. As a result, soldiers with early-stage distress are often evacuated to rear medical facilities, delaying care, reducing readiness, and increasing long-term risks. This paper proposes a Train-the-Trainers framework in which soldiers who have completed therapy and returned to duty are trained as peer facilitators to provide first-line psychological support in operational settings. To scale and standardize this model under severe resource and connectivity constraints, we introduce an agentic AI-enabled platform that augments these recovered soldiers with specialized AI agents. The recovered soldier acts as a human supervisor, coordinating agents for symptom triage, guided peer-support interventions, operational constraint reasoning, training and simulation, and structured documentation for clinical escalation when needed. The AI agents use consensus-driven decision support in high-stakes environments. The architecture functions in air-gapped and low-connectivity settings, maintaining human oversight and ethical safeguards. A functional prototype was developed with the McDonald U.S. Army Health Center, Newport News, VA, USA. By combining peer-based intervention with consensus-driven agentic AI decision support, the framework seeks to cut response times, prevent symptom escalation, reduce unnecessary evacuations, and improve continuity of care. This work shows how agentic AI can serve as a force multiplier for mental health support in austere environments and identifies pathways for broader evaluation and deployment across defense and humanitarian operations.