AssetOpsBench: Benchmarking Al Agents for Task Automation in Industrial Asset Operations and Maintenance
Abstract
AI for Industrial Asset Lifecycle Management aims to automate complex operational workflows—such as condition monitoring, maintenance planning, and intervention scheduling—to reduce human workload and minimize system downtime. Traditional AI/ML approaches have primarily tackled these problems in isolation, solving narrow tasks within the broader operational pipeline. In contrast, the emergence of AI agents and large language models (LLMs) introduces a next-generation opportunity: enabling end-to-end automation across the entire asset lifecycle. This paper envisions a future where AI agents autonomously manage tasks that previously required distinct expertise and manual coordination. To this end, we introduce AssetOpsBench—a unified framework and environment designed to guide the development, orchestration, and evaluation of domain-specific agents tailored for Industry 4.0 applications. We outline the key requirements for such holistic systems and provide actionable insights into building agents that integrate perception, reasoning, and control for real-world industrial operations. The software is available at https://github.com/IBM/AssetOpsBench.
Bio
Dhaval Patel is a Senior Technical Staff Member at IBM, with over 13 years of relevant experience in the field of computer science. He is a highly skilled software engineer and research scientist with extensive knowledge of data mining, text mining, natural language processing, distributed computing, high performance computing, big data computing, applied statistics, and more. Dhaval's earlier research focused on large-scale data analysis for knowledge discovery. His overall research goal is to develop scalable algorithms and systems to enable efficient knowledge discovery over dynamic, heterogeneous, and massive-scale complex data. He has worked with a variety of data types, including interval data, time series data, spatial data, spatiotemporal data, social network data, and more. Currently, Dhaval is working on operationalizing artificial intelligence in the physical world, making real systems capable of predicting near-future events. He has also worked extensively with big data, machine learning, data mining, project management, and time series data analytics. He has developed a strong understanding of sensor data from the oil, petroleum, and nano Scala VLSI industries and has developed algorithms for Spark 2.0.x. Dhaval's educational background includes a Ph.D. in Computer Science from the National University of Singapore, an M.Tech in Information Technology from IIT, and a B.E in Information Technology from Shree U.V. Patel College of Engineering in Gujarat. Dhaval is a member of several prestigious organizations, including IEEE and ACM, and has been recognized as a Senior Member in both.