AssetOpsBench: Benchmarking AI 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 and maintenance scheduling, to minimize system downtime. While traditional AI/ML approaches solve narrow tasks in isolation, Large Language Model (LLM) agents offer a next-generation opportunity for end-to-end automation. In this paper, we introduce AssetOpsBench, a unified framework for orchestrating and evaluating domain-specific agents for Industry 4.0. AssetOpsBench provides a multimodal ecosystem comprising a catalog of four domain-specific agents, a curated dataset of 140+ human-authored natural-language queries grounded in real industrial scenarios, and a simulated, CouchDB-backed IoT environment. We introduce an automated evaluation framework that uses three key metrics to analyze architectural trade-offs between the Tool-As-Agent and Plan-Executor paradigms, along with a systematic procedure for the automated discovery of emerging failure modes. The practical relevance of AssetOpsBench is demonstrated by its broad community adoption, with 250+ users and over 500 agents submitted to our public benchmarking platform, supporting reproducible and scalable research for real-world industrial operations. The code is accesible at this https URL .

Artificial Intelligence (cs.AI); Multiagent Systems (cs.MA)

arXiv:2506.03828 [cs.AI]

(or arXiv:2506.03828v4 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2506.03828 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1145/3770855.3817562 Focus to learn more DOI(s) linking to related resources

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