Utility-scale photovoltaic (PV) plants combine multiple cyber-physical systems. SCADA networks continuously collect a vast amount of telemetry from multiple devices such as inverters, weather stations, PV arrays, and auxiliary equipment. While SCADA platforms are widely deployed, fault diagnosis and maintenance decisions are mainly based on an alarm-driven system and manual operations. This paper presents the Reliable Agentic AI Framework for SCADA Network Orchestration and Explainable Fault Diagnosis in Utility-Scale Solar Plants, where multiple, specialized AI agents work in collaboration to analyze SCADA telemetry and provide operational intelligence in a reliable, explainable, and actionable manner. The framework is evaluated in a simulated environment of a 500 MW utilityscale solar plant, which includes realistic weather, operational and solar irradiance variability as well as representative fault scenarios. The results show an approximate 40% to 60% reduction in detection-to-action time and an approximate 20% increase in fault-triage efficiency. The results also show an improvement of inverter availability from approximately 96% to 99.5%, an average normalized energy-yield advantage of approximately 1.8%, and a decrease in total operation and maintenance (O&M) costs of approximately 17.5%. It is proven that reliable agent orchestration and explainable fault diagnosis improve operational reliability, maintenance efficiency, and transparency of decisions, which opens a new frontier for agentic AI in intelligent operations
for renewable energy.

Reliable AI, Agentic AI, SCADA Networks, Multi-Agent Systems, Explainable AI, Solar PV, Fault Diagnosis.