# X-GridAgent: An LLM-Powered Agentic AI System for Assisting Power Grid Analysis arXiv: https://arxiv.org/abs/2512.20789 ยท HTML: https://mlsanity.com/paper/2512.20789/ ## Key facts - Cited by 10 papers in the map (data through 14 Oct 2026). - Placed in Financial trading and forecasting agents, one of 16 directions in LLM agents and tool use. - Builds on 23 of its 46 references that are in the map. - First posted on arXiv 23 Dec 2025. ## Placed in - LLM agents and tool use / [Financial trading and forecasting agents](https://mlsanity.com/agents/financial-trading-and-forecasting-agents/) ## Builds on (most cited first, 23 of 23) - [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023) - [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019) - [A Survey of Large Language Models](https://mlsanity.com/paper/2303.18223/) (2023) - [A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2405.06211/) (2024) - [Model Context Protocol (MCP): Landscape, Security Threats, and Future Research Directions](https://mlsanity.com/paper/2503.23278/) (2025) - [Agentic Retrieval-Augmented Generation: A Survey on Agentic RAG](https://mlsanity.com/paper/2501.09136/) (2025) - [Query Rewriting for Retrieval-Augmented Large Language Models](https://mlsanity.com/paper/2305.14283/) (2023) - [Multilingual Universal Sentence Encoder for Semantic Retrieval](https://mlsanity.com/paper/1907.04307/) (2019) - [Agent AI: Surveying the Horizons of Multimodal Interaction](https://mlsanity.com/paper/2401.03568/) (2024) - [Dense Text Retrieval based on Pretrained Language Models: A Survey](https://mlsanity.com/paper/2211.14876/) (2022) - [Large Language Models for Robotics: A Survey](https://mlsanity.com/paper/2311.07226/) (2023) - [pandapower - an Open Source Python Tool for Convenient Modeling, Analysis and Optimization of Electric Power Systems](https://mlsanity.com/paper/1709.06743/) (2017) - [Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges](https://mlsanity.com/paper/2102.01168/) (2021) - [Exploring the Capabilities and Limitations of Large Language Models in the Electric Energy Sector](https://mlsanity.com/paper/2403.09125/) (2024) - [Enhancing LLMs for Power System Simulations: A Feedback-driven Multi-agent Framework](https://mlsanity.com/paper/2411.16707/) (2024) - [GridMind: LLMs-Powered Agents for Power System Analysis and Operations](https://mlsanity.com/paper/2509.02494/) (2025) - [Electricity Demand and Grid Impacts of AI Data Centers: Challenges and Prospects](https://mlsanity.com/paper/2509.07218/) (2025) - [Grid-Agent: An LLM-Powered Multi-Agent System for Power Grid Control](https://mlsanity.com/paper/2508.05702/) (2025) - [Large Language Model Powered Automated Modeling and Optimization of Active Distribution Network Dispatch Problems](https://mlsanity.com/paper/2507.21162/) (2025) - [PowerGraph-LLM: Novel Power Grid Graph Embedding and Optimization with Large Language Models](https://mlsanity.com/paper/2501.07639/) (2025) - [PowerChain: A Verifiable Agentic AI System for Automating Distribution Grid Analyses](https://mlsanity.com/paper/2508.17094/) (2025) - [Can Large Language Model Agents Balance Energy Systems?](https://mlsanity.com/paper/2502.10557/) (2025) - [Towards Carbon-Free Electricity: A Flow-Based Framework for Power Grid Carbon Accounting and Decarbonization](https://mlsanity.com/paper/2308.03268/) (2023) ## Built on by (most cited first, 10 of 10) - [PseudoAct: Leveraging Pseudocode Synthesis for Flexible Planning and Action Control in Large Language Model Agents](https://mlsanity.com/paper/2602.23668/) (2026) - [Integrating Graphs, Large Language Models, and Agents: Reasoning and Retrieval](https://mlsanity.com/paper/2604.15951/) (2026) - [AutoB2G: Agentic Simulation and Reinforcement Learning for Spatio-Temporal Grid-Interactive Building Control](https://mlsanity.com/paper/2603.26005/) (2026) - [Grid-Orch: An LLM-Powered Orchestrator for Distribution Grid Simulation and Analytics](https://mlsanity.com/paper/2605.12728/) (2026) - [Power Systems Agent Benchmark: Executable Evaluation of AI Agents in Electric Power Engineering](https://mlsanity.com/paper/2606.20950/) (2026) - [Orchestrating Power Grid Studies with Multi-Agent AI and MCP Servers](https://mlsanity.com/paper/2607.14158/) (2026) - [LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications](https://mlsanity.com/paper/2607.18147/) (2026) - [Engineering Trustworthy Agentic AI for Critical Systems](https://mlsanity.com/paper/2607.18548/) (2026) - [A-CPES: A Reference Framework for Agentic AI in Cyber-Physical Energy Systems](https://mlsanity.com/paper/2608.22672/) (2026) - [A Governance-Aware Large Language Model Orchestrated Agentic Digital Twin for Transmission System Operator Control Room Decision Support](https://mlsanity.com/paper/2609.22476/) (2026) ## Abstract (arXiv) The growing complexity of power system operations has created an urgent need for intelligent, automated tools to support reliable and efficient grid management. Conventional analysis tools often require significant domain expertise and manual effort, which limits their accessibility and adaptability. To address these challenges, this paper presents X-GridAgent, a novel large language model (LLM)-powered agentic AI system designed to automate complex power system analysis through natural language queries. The system integrates domain-specific tools and specialized databases under a three-layer hierarchical architecture comprising planning, coordination, and action layers. This architecture offers high flexibility and adaptability to previously unseen tasks, while providing a modular and extensible framework that can be readily expanded to incorporate new tools, data sources, or analytical capabilities. To further enhance performance, we introduce two novel algorithms: (1) LLM-driven prompt refinement with human feedback, and (2) schema-adaptive hybrid retrieval-augmented generation (RAG) for accurate information retrieval from large-scale structured grid datasets. Experimental evaluations across a variety of user queries and power grid cases demonstrate the effectiveness and reliability of X-GridAgent in automating interpretable and rigorous power system analysis.