AssetOpsBench: Bridging the Gap Between AI Agent Benchmarks and Industrial Reality

AssetOpsBench is a comprehensive benchmark and evaluation system with six qualitative dimensions that bridges the gap for agentic AI in domain-specific settings, starting with industrial Asset Lifecycle Management.

Introduction

While existing AI benchmarks excel at isolated tasks such as coding or web navigation, they often fail to capture the complexity of real-world industrial operations. To bridge this gap, we introduce AssetOpsBench, a framework specifically designed to evaluate agent performance across six critical dimensions of industrial applications. Unlike traditional benchmarks, AssetOpsBench emphasizes the need for multi-agent coordination—moving beyond `lone wolf' models to systems that can handle complex failure modes, integrate multiple data streams, and manage intricate work orders. By focusing on these high-stakes, multi-agent dynamics, the benchmark ensures that AI agents are assessed on their ability to navigate the nuances and safety-critical demands of a true industrial environment.

AssetOpsBench is built for asset operations such as chillers and air handling units. It comprises:

• 2.3M sensor telemetry points
• 140+ curated scenarios across 4 agents
• 4.2K work orders for diverse scenarios
• 53 structured failure modes

Experts helped curate 150+ scenarios. Each scenario includes metadata: task type, output format, category, and sub-agents. The tasks designed span across:

• Anomaly detection in sensor streams
• Failure mode reasoning and diagnostics
• KPI forecasting and analysis
• Work order summarization and prioritization

Evaluation Framework and Overall Feedback

AssetOpsBench evaluates agentic systems across six qualitative dimensions designed to reflect real operational constraints in industrial asset management. Rather than optimizing for a single success metric, the benchmark emphasizes decision trace quality, evidence grounding, failure awareness, and actionability under incomplete and noisy data.

Each agent run is scored across six criteria: