Autonomous intelligence for mining productivity: a multi-agent AI approach to operational value creation

Key points

• First, no integrated agentic architecture has been proposed for mining IROCs that orchestrates Supervisory Control and Data Acquisition systems, digital twins, predictive analytics and human-machine interfaces under safety-critical constraints.1
• It synthesises industry needs and quantified gains documented in analogous industries that motivate an agentic approach to mining IROCs.1
• It introduces EI2ROC, a three-tier multi-agent architecture combining a Large Language Model orchestrator with specialised monitoring and production-capacityprediction agents communicating through a Model Context Protocol-style tool interface.1
• Finally, it proposes a multi-dimensional benchmarking framework for industrial agentic AI comprising scenario library, harness, metrics and humancalibration protocol.1

Sources (1)

• [1]Autonomous intelligence for mining productivity: a multi-agent AI approach to operational value creation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 19, 01:24 PM

  Integrated Remote Operation Centres (IROCs) are emerging as the architectural frontier of Industry 4.0 for capital-intensive sectors such as mining, oil and gas, and energy.

  First, no integrated agentic architecture has been proposed for mining IROCs that orchestrates Supervisory Control and Data Acquisition systems, digital twins, predictive analytics and human-machine interfaces under safety-critical constraints.

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