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

AssetOpsBench is a benchmark framework designed to bridge the gap between AI agent benchmarks and the complexities of real-world industrial operations, specifically targeting Asset Lifecycle Management. Unlike traditional benchmarks focused on isolated tasks, AssetOpsBench evaluates agent performance across six qualitative dimensions in high-stakes, multi-agent industrial environments, emphasizing decision trace quality and failure awareness under incomplete data. The framework features 2.3 million sensor telemetry points, 140+ curated scenarios, and a failure analysis pipeline that identifies and clusters failure patterns without exposing raw execution traces. Early evaluations reveal that while general-purpose agents perform well at surface-level reasoning, they struggle with multi-agent coordination and complex failure semantics. AssetOpsBench aims to uncover and understand agent failures, providing feedback that helps developers refine workflows and improve agent designs iteratively. Despite extensive testing, no models have yet met the 85-point readiness threshold, highlighting the maturity gap in deploying AI agents for industrial applications. The framework also supports evolving failure taxonomies and encourages community participation to enhance the robustness of agentic systems in industrial settings.