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AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance

arXiv cs.MAby [Submitted on 4 Jun 2025 (v1), last revised 2 Apr 2026 (this version, v3)]April 3, 20262 min read1 views

arXiv:2506.03828v3 Announce Type: replace-cross 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 intro

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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 .

Comments: 25 pages, 18 figures

Subjects:

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

Cite as: arXiv:2506.03828 [cs.AI]

(or arXiv:2506.03828v3 [cs.AI] for this version)

https://doi.org/10.48550/arXiv.2506.03828

arXiv-issued DOI via DataCite

Submission history

From: Dhaval Patel Dr [view email] [v1] Wed, 4 Jun 2025 10:57:35 UTC (5,345 KB) [v2] Mon, 16 Mar 2026 02:36:52 UTC (8,186 KB) [v3] Thu, 2 Apr 2026 02:21:44 UTC (8,186 KB)

Original source

arXiv cs.MA

https://arxiv.org/abs/2506.03828

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