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

AssetOpsBench is a new benchmark and evaluation system for AI agents in domain-specific industrial settings, starting with Asset Lifecycle Management. Unlike benchmarks focused on isolated tasks, it evaluates agent performance across six qualitative dimensions, emphasizing multi-agent coordination, complex failure modes, and multiple data streams. The system uses a large dataset including sensor telemetry, work orders, and curated scenarios to test agents on tasks like anomaly detection and failure reasoning. A central feature is its detailed analysis of failure modes through a pipeline called TrajFM, which uses LLMs to identify where and why agent behavior breaks down in realistic operational workflows.