AssetOpsBench: Benchmarking AI agents for industrial asset management and multi-agent coordination

AI Impact Summary

AssetOpsBench introduces a domain-specific benchmark suite for industrial AI agents, emphasizing multi-agent coordination and uncertainty under noisy sensor data. The TrajFM pipeline combines LLM-guided failure extraction with clustering to surface reusable failure patterns across 881 traces, enabling developers to diagnose weaknesses without exposing raw traces. Early results across GPT-4.1, Mistral-Large, LLaMA-4 Maverick, and LLaMA-3-70B show no model meets deployment readiness (85-point threshold), highlighting the gap between academic benchmarks and production reliability in asset operations. The framework's privacy-preserving, feedback-driven evaluation and support for both planning and execution tracks offer a concrete path to improving reliability before live deployment.