AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial
  Asset Operations and Maintenance
Authors: name: Dhaval Patel, name: Shuxin Lin, name: James Rayfield, name: Nianjun Zhou, name: Roman Vaculin, name: Natalia Martinez, name: Fearghal O'donncha, name: Jayant Kalagnanam

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.