AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial
  Asset Operations and Maintenance
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.
68473c983ec10bdd8ab4dcd3
Dhaval Patel
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2025-06-10T09:20:37.895Z
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Dhaval
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DhavalPatel
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Shuxin Lin
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2025-06-09T19:57:13.307Z
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Shuxin Lin
user
shuxinl
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James Rayfield
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2025-06-10T15:20:27.863Z
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James Rayfield
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jtrayfield
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Nianjun Zhou
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2025-06-13T07:41:57.610Z
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Zhou
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Nianjun
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Roman Vaculin
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Natalia Martinez
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2025-06-16T12:56:28.716Z
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Natalia Martinez Gil
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natalialmg91
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Fearghal O'donncha
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2025-06-13T07:41:59.912Z
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O'Donncha
user
Fearghal
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Jayant Kalagnanam
AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial
  Asset Operations and Maintenance
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.
A unified framework, AssetOpsBench, is introduced to enable end-to-end automation of industrial asset lifecycle management through domain-specific AI agents.
AI agents large language models LLMs end-to-end automation AssetOpsBench Industry 4.0 perception reasoning control