AssetOpsBench: Benchmarking AI Agents for Industrial Asset Operations & Maintenance

📄 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

• 2025-06-01: AssetOpsBench v1.0 released with 140+ industrial scenarios.
• 2025-09-01: CODS Competition launched. Access AI Agentic Challenge AssetOpsBench-Live
• Upcoming Events: Tutorial at AAAI 2026 – Agents for Industry 4.0 Applications.
• 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: 140+ 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 here.

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AI Agents

Domain-Specific Agents

• 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

• 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

• 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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Talks & Events

• Workshops: Participate in GenAIBench-26 at AAAI 2025 focusing on multi-agent AI workflows.
• Webinars & Seminars: Learn best practices for industrial task automation with AI agents.
• Competitions: Benchmark your agents on real-world industrial scenarios using AssetOpsBench.

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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 📖

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