Asset Ops Bench

Abstract

1. Introduction

[](#) [](#) [](#publications) [](#publications) [](#publications) [](#publications) [](#publications) [](#publications)

9Asset classes 141+Scenarios 5 + 1Domain agents + utility server 2Orchestration frameworks 20+University extensions 500+Competition submissions

2. Objective

AssetOpsBench - Industry 4.0: A unified benchmark and framework for building, orchestrating, and evaluating domain-specific AI agents for Industry 4.0 asset operations and maintenance, with 460+ scenarios, 5 specialist agents (IoT, FMSR, TSFM, Work Order,...), and multi-agent orchestration blueprints (MetaAgent, AgentHive) over MCP.

This project demonstrates how Python can be applied to a real-world Data Science problem.

4. Technology Stack

• Plan Execute — plan-and-execute sequential workflow to work with any LLM
• Deep Agent — planning, sub-agents, and virtual filesystem for long-horizon tasks
• Claude Agent — ReAct-based orchestrator using Claude with agent-as-tool delegation
• OpenAI Agent — ReAct-based orchestrator using OpenAI models with agent-as-tool delegation
• ReActXen IoT Agent (EMNLP 2025)
• FailureSensorIQ (NeurIPS 2025)
• AssetOpsBench Lab (AAAI 2026)
• SPIRAL (AAAI 2026)

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/IBM/AssetOpsBench.git
cd AssetOpsBench

1. Run on Colab — no install required (illustration of LLM Agent)
2. Try the HF Playground — interactive demo
3. Read INSTRUCTIONS.md — full setup, MCP servers, plan-execute runner

# Clone and install
git clone https://github.com/IBM/AssetOpsBench.git
cd AssetOpsBench
pip install -e .

# Try a scenario (to be enabled)
python -m assetopsbench.run --scenario "List all sensors of Chiller 6 in MAIN site"

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Turn the analysis into an interactive dashboard
• Automate data refresh with a scheduled job
• Add a predictive model on top of the analysis

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What is the source of the dataset and how was missing data handled?
2. Which exploratory analysis steps revealed the most useful insight?
3. Why were these particular charts chosen to present the data?
4. Which statistical or ML technique supports the conclusions?
5. How could the analysis be automated or refreshed with new data?

9. Source Code & License

This project is developed by IBM and published on GitHub under the Apache License 2.0. Please follow the license terms and credit the original author when you use or modify this code.