Adaptive k-Truss-Constrained Agentic AI Framework for Resilient Multi-Agent UAV Swarm Coordination in Dynamic Disaster Environments

Coordinating multi-agent unmanned aerial vehicle (UAV) swarms is challenging in disaster scenarios where communications are dynamic, unreliable, and subject to UAV losses. While distributed artificial intelligence has enabled unprecedented levels of autonomous multi-agent coordination, most methods implicitly take communication topology as a given, not accounting for its limited maintenance in such scenarios. As a result, communication fragmentation can undermine autonomous mission progress during highly dynamic communications conditions. This paper proposes the Adaptive k-Truss-Constrained Agentic AI Framework (ATAC), an AI-based decentralized coordination framework for multi-agent UAV systems, which explicitly factors in graph-theoretic structural considerations during decision-making. The swarm is modeled as a graph, where an adaptive k-truss backbone is maintained during dynamic communication conditions to preserve triangle-based redundancy. Each agent acts as a graph-aware AI entity which bases its decentralized decisions on local information and descriptors of the communication backbone. A closed-loop evolutionary process is used to rebuild the backbone after significant communication link losses while UAVs make mission progress decisions based on information from the current backbone, enabling continuous adaption of the swarm structure to the communication state. The efficacy of the proposed framework is demonstrated through a comprehensive simulation campaign which includes communication link losses, UAV failures, adaptive truss selection, ablation studies, reward sensitivity analysis, and computational performance assessments. ATAC is compared to alternative graph-aware coordination approaches, showing consistent improvements in maintaining communication, preserving backbone structure, enabling triangle-based connectivity, and overall structural recovery while still achieving high-levels of mission progress during dynamic disaster response scenarios. The results highlight the effectiveness of explicitly tying AI-driven decentralized decision-making to maintenance of a graph-theoretic backbone structure for resilient UAV swarm coordination.