Opening article
AssetOpsBench is a new benchmark and evaluation system designed to test agentic AI in industrial asset management. It evaluates multi-agent coordination, decision traces, and failure modes across six qualitative dimensions under real-world operational constraints.
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.
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:
This feedback-driven evaluation loop enables iterative improvement: developers can diagnose failure patterns, refine agent design or workflow structure, and resubmit updated agents for further evaluation. Both planning-focused and execution-focused agents are supported, allowing researchers and practitioners to explore diverse agentic designs within the same benchmark framework.
Experts helped curate 150+ scenarios. Each scenario includes metadata: task type, output format, category, and sub-agents. The tasks designed span across:
Failure analysis in AssetOpsBench is implemented through a dedicated trajectory-level pipeline ( TrajFM ), which combines LLM-based reasoning with statistical clustering to surface interpretable failure patterns from agent execution traces. This pipeline operates in three stages: (1) trajectory-level failure extraction using an LLM-guided diagnostic prompt, (2) embedding-based clustering to group recurring failure patterns, and (3) analysis and visualization to support developer feedback and iteration.
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.
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. Agents that explicitly model operational context and uncertainty tend to produce more stable and interpretable trajectories, even when final task completion is partial.
This feedback-oriented evaluation is intentional: in industrial settings, understanding why an agent fails is often more valuable than a binary success signal.
The introduction of AssetOpsBench highlights the gap between general-purpose AI reasoning and the rigorous, safety-critical demands of industrial environments, pushing the industry toward failure-aware and multi-agent systems.