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[2511.14478] Agentic AI Systems in Electrical Power Systems Engineering: Current State-of-the-Art and Challenges
Summary
This article reviews the state-of-the-art in agentic AI systems within electrical power engineering, providing a taxonomy and practical applications while addressing challenges in deployment.
Why It Matters
As AI technology evolves, understanding agentic AI systems is crucial for their effective integration into electrical engineering. This paper fills a gap in defining these systems and offers actionable insights for researchers and practitioners, ensuring safe and reliable deployment in critical infrastructure.
Key Takeaways
- Defines agentic AI systems and differentiates them from traditional AI.
- Presents four detailed use cases in electrical engineering demonstrating practical applications.
- Highlights the importance of a clear taxonomy for agentic AI.
- Offers recommendations for designing safe and accountable agentic AI systems.
- Discusses failure modes to ensure robust deployment in engineering contexts.
Electrical Engineering and Systems Science > Systems and Control arXiv:2511.14478 (eess) [Submitted on 18 Nov 2025 (v1), last revised 20 Feb 2026 (this version, v4)] Title:Agentic AI Systems in Electrical Power Systems Engineering: Current State-of-the-Art and Challenges Authors:Soham Ghosh, Gaurav Mittal View a PDF of the paper titled Agentic AI Systems in Electrical Power Systems Engineering: Current State-of-the-Art and Challenges, by Soham Ghosh and Gaurav Mittal View PDF Abstract:Agentic AI systems have recently emerged as a critical and transformative approach in artificial intelligence, offering capabilities that extend far beyond traditional AI agents and contemporary generative AI models. This rapid evolution necessitates a clear conceptual and taxonomical understanding to differentiate this new paradigm. Our paper addresses this gap by providing a comprehensive review that establishes a precise definition and taxonomy for "agentic AI," with the aim of distinguishing it from previous AI paradigms. The concepts are gradually introduced, starting with a highlight of its diverse applications across the broader field of engineering. The paper then presents four detailed, state-of-the-art use case applications specifically within electrical engineering. These case studies demonstrate practical impact, ranging from an advanced agentic framework for streamlining complex power system studies and benchmarking to a novel system developed for survival analysis of dynamic pri...
