AI Agents for Industrial Asset Operations & Maintenance

📘 Tutorials: Learn more from our detailed guides —
ReActXen IoT Agent (EMNLP 2025) | AssetOpsBench Technical Material

📄 Paper | 🤗 HF-Dataset | 📢 Blog | Contributors

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📑 Table of Contents

1. Announcements
2. Introduction
3. Datasets
4. AI Agents
5. Multi-Agent Frameworks
6. System Diagram
7. Leaderboards
8. Docker Setup
9. Talks & Events
10. External Resources
11. Contributors

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Announcements

• 🎯 Upcoming Events: Tutorial at AAAI 2026 – Agents for Industry 4.0 Applications.
• 🕓 Past Event: 2025-10-03 – 2 Hour Workshop AI Agents and Their Role in Industry 4.0 Applications (NJIT-ACM)
• 🏆 Accepted Papers: Parts of papers are accepted at NeurIPS 2025, EMNLP 2025 Research Track, and EMNLP 2025 Industry Track.
• 🚀 2025-09-01: CODS 2025 Competition launched – Access AI Agentic Challenge AssetOpsBench-Live.
• 📦 2025-06-01: AssetOpsBench v1.0 released with 141 industrial Scenarios.

✨ Stay tuned for new tracks, competitions, and community events.

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Introduction

AssetOpsBench is a unified framework for developing, orchestrating, and evaluating domain-specific AI agents in industrial asset operations and maintenance.

It provides:

• 4 domain-specific agents
• 2 multi-agent orchestration frameworks

Designed for maintenance engineers, reliability specialists, and facility planners, it allows reproducible evaluation of multi-step workflows in simulated industrial environments.

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Datasets: 141 Scenarios

AssetOpsBench scenarios span multiple domains:

Domain	Example Task
IoT	"List all sensors of Chiller 6 in MAIN site"
FSMR	"Identify failure modes detected by Chiller 6 Supply Temperature"
TSFM	"Forecast 'Chiller 9 Condenser Water Flow' for the week of 2020-04-27"
WO	"Generate a work order for Chiller 6 anomaly detection"

Some tasks focus on a single domain, others are multi-step end-to-end workflows.
Explore all scenarios HF-Dataset.

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AI Agents

Domain-Specific Agents (Important tools)

• IoT Agent: get_sites, get_history, get_assets, get_sensors
• FMSR Agent: get_sensors, get_failure_modes, get_failure_sensor_mapping
• TSFM Agent: forecasting, timeseries_anomaly_detection
• WO Agent: generate_work_order

Multi-Agent Frameworks (Blue Prints)

• MetaAgent: reAct-based single-agent-as-tool orchestration
• AgentHive: plan-and-execute sequential workflow

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System Diagram

Visual overview of AssetOpsBench workflow:

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Leaderboards

• Evaluated with 7 Large Language Models
• Trajectories scored using LLM Judge (Llama-4-Maverick-17B)
• 6-dimensional criteria measure reasoning, execution, and data handling

Example: MetaAgent leaderboard

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Run AssetOpsBench in Docker

• Please Refer to the
• Pre-built Docker Images: assetopsbench-basic (minimal) & assetopsbench-extra (full)
• Conda environment: assetopsbench
• Full setup guide

cd /path/to/AssetOpsBench
chmod +x benchmark/entrypoint.sh
docker-compose -f benchmark/docker-compose.yml build
docker-compose -f benchmark/docker-compose.yml up

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External Resources

• 📄 Paper: AssetOpsBench: Benchmarking AI Agents for Industrial Asset Operations
• 🤗 HuggingFace: Scenario & Model Hub
• 📢 Blog: Insights, Tutorials, and Updates
• 🎥 Recorded Talks: Link coming soon.

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Contributors

Thanks goes to these wonderful people ✨

DhavalRepo18 💻 📖	ShuxinLin 💻 📖	jtrayfield 💻 📖	nianjunz 💻 📖	ChathurangiShyalika 💻 📖	PUSHPAK-JAISWAL 💻 📖	bradleyjeck 💻 📖
florenzi002 💻 📖	kushwaha001 💻	Mohit Gupta 📖	Ayan Das 📖 💻

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