Agentic AI for Autonomous Cascade Failure Detection and Response in Critical Energy Infrastructure - Proof-of-Concept Implementation and Validation Framework for Multi-Agent Decision Systems

This paper presents a novel agentic AI framework for autonomous cascade failure detection and response in critical energy infrastructure. Building upon our previously validated physics-informed graph neural network (PI-GNN) architecture, we extend the system with autonomous decision-making capabilities through a multi-agent architecture that enables real-time grid stabilization, threat response, and operational optimization. Our proof-ofconcept validation on the IEEE 118-bus test system demonstrates exceptional performance metrics: 100% cascade-level detection accuracy, 97% recall at the node level, 0.43 precision score, and 22.8-minute average prediction lead time. The agentic extension introduces three specialized autonomous agents-Grid Stabilization Agent, Threat Response Agent, and Sustainability Agent-operating within a hierarchical safety framework that maintains human oversight while enabling sub-minute autonomous response times. Experimental validation across 30-step simulation scenarios confirms the system's ability to detect critical cascade conditions in real-time, with the agent architecture providing an additional 15-20% improvement in response effectiveness compared to prediction-only systems. The multi-modal data fusion layer integrates SCADA telemetry, environmental sensors, and physics-based constraints into a unified decision framework, validated through comprehensive dashboard visualization showing grid frequency deviations (70.76/60.0 Hz), confidence metrics (39.4%), and node-level probability assessments. This work establishes a foundational framework for deploying autonomous AI systems in mission-critical energy infrastructure environments.