This tutorial provides the foundations for building auditable, production-grade agents for Industry 4.0 through end to end MCP grounded agent pipeline that connects specialized MCP servers to high velocity industrial data.
Dhaval Patel, Chathurangi Shyalika, Shuxin Lin et al.· Proceedings of the 32nd ACM...· 0 citations
As Large Language Model (LLM) agents transition from general-purpose assistants to specialized enterprise tools, grounding them in industrial reality remains a significant challenge. To address this, we developed AssetOpsBench, a comprehensive framework for the lifecycle of AI agents in Industrial Asset Operations and Maintenance, built around the Model Context Protocol (MCP) as the standard for connecting agents to complex enterprise data silos. In this hands-on tutorial, participants will learn to build reliable industrial agents using MCP and AssetOpsBench. The session is divided into two parts. In the first part, we dive into an end to end MCP grounded agent pipeline that connects specialized MCP servers to high velocity industrial data including time series telemetry, IoT streams, failure mode records, and work order histories while orchestrating workflows with Plan Execute and Reflexion planners. In the second part, we unlock predictive analytics such as anomaly detection and Remaining Useful Life (RUL) prediction within agentic workflows, assess agent reliability through multi-dimensional evaluation, and ground agents against physical constraints. Whether for researchers or practitioners, this tutorial provides the foundations for building auditable, production-grade agents for Industry 4.0. AssetOpsBench is accessible at: https://github.com/IBM/AssetOpsBench.
Dhaval Patel, Chathurangi Shyalika, Shuxin Lin et al.· Proceedings of the 32nd ACM...· 0 citations
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