AssetOpsBench: Benchmarking AI Agents for Task Automation in Industrial Asset Operations and Maintenance
This work addresses the need for end-to-end automation in industrial asset management, which could reduce human workload and system downtime, though it appears incremental as it builds on existing AI agent and LLM technologies.
The paper tackles the problem of automating complex industrial asset operations and maintenance workflows by introducing AssetOpsBench, a unified framework and environment for developing and evaluating domain-specific AI agents, with the software made publicly available.
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.