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

AssetOpsBenchis 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.

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, and Work order summarization and prioritization.

AssetOpsBench evaluates agentic systems across six qualitative dimensions: Task Completion, Retrieval Accuracy, Result Verification, Sequence Correctness, Clarity and Justification, and Hallucination rate. 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.

Across early evaluations, we observe that many general-purpose agents perform well on surface-level reasoning but struggle with sustained multi-step coordination involving work orders, failure semantics, and temporal dependencies. A central contribution of AssetOpsBench is the explicit treatment of failure modes as first-class evaluation signals. Using a dedicated pipeline (TrajFM), it identifies where and why agent behavior breaks down.

In a community evaluation involving 225 users and 300+ agents, results showed that none of the leading models (including GPT-4 and LLaMA-3) could pass the 85-point threshold for deployment readiness. Common failures included ineffective error recovery (31.2%) and overstated completion (23.8%), where agents claimed success despite failing the task. This highlights the importance of rigorous benchmarking to ensure operators do not act upon incorrect AI-generated information.