Large Language Model Agents in Dynamic Mine Planning: Challenges, Opportunities, and Future Directions
TL;DR
The analysis proposes an incremental maturity pathway, advancing from bounded advisory systems to fully integrated planning frameworks tailored to mining’s operational requirements, serving as a theoretically grounded framework to guide future empirical validation in mining.
Abstract
The increasing availability of Large Language Model (LLM)-driven agents has introduced a new class of Artificial Intelligence (AI) systems capable of processing, organizing, and interacting with heterogeneous data sources using natural language. Although these systems are being adopted across various industries, their application in mining, particularly within Dynamic Mine Planning (DMP), remains nascent. A key research gap is the limited understanding of how LLM-driven agents can support multisource data integration across geological, operational, and safety domains in DMP to support adaptive decision-making. Traditional planning pipelines are typically too static to accommodate operational variability and real-time responsiveness. To address this gap, the review is structured around five research questions: agent applications in mining, transferable architectures from related domains, adoption barriers, multi-agent opportunities for real-time decision support, and future deployment priorities. Through a PRISMA-based synthesis of 33 peer-reviewed studies across mining and adjacent engineered sectors, the review examines LLM agents within the broader landscape of AI-embedded decision-making systems, from dispatch automation and predictive maintenance to Mining 5.0 frameworks, identifying current capabilities, performance benchmarks, and methodological gaps. The analysis proposes an incremental maturity pathway, advancing from bounded advisory systems to fully integrated planning frameworks tailored to mining’s operational requirements. As mining-specific LLM-agent implementations are largely conceptual, the pathway serves as a theoretically grounded framework to guide future empirical validation in mining. The proposed pathway offers a structured, conceptual reference for phasing in LLM-driven decision support toward greater autonomy. The findings advance both academic research and industry practice by clarifying the necessary data infrastructure, hybrid architectures, safety validation mechanisms, and governance conditions for deployment in mining operations.