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

2025-06-04

AI Summary

To address the fragmentation of traditional AI models and their inability to support end-to-end collaboration across the full lifecycle of industrial assets, this paper proposes the first unified evaluation paradigm for multimodal AI agents tailored to Industry 4.0. Methodologically, we design an LLM-based agent architecture that tightly integrates industrial knowledge graphs, heterogeneous sensor interfaces, and digital twin simulation environments—enabling autonomous, cross-phase orchestration of tasks such as condition monitoring, maintenance planning, and intervention scheduling. Key contributions include: (1) an open-source, reproducible benchmarking framework (on GitHub) featuring standardized task suites, evaluation protocols, and baseline agents; and (2) substantial improvements in cross-task generalization and real-world production-line deployability, establishing a measurable, scalable evaluation infrastructure for industrial AI agent systems.

Technology Category

• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Multiagent Systems: Modeling other Agents

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

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.

Problem

• Benchmarking AI agents for industrial task automation
• Enabling end-to-end automation in asset lifecycle management
• Developing domain-specific agents for Industry 4.0 applications

Innovation

• AI agents for end-to-end industrial automation
• Unified framework for domain-specific agent development
• Integration of perception, reasoning, and control