• AssetOpsBench introduces a multi-agent benchmark for industrial asset lifecycle management. • Covers 2.3M sensor telemetry points and 4.2K work orders. • Evaluates agents across six dimensions: trace quality, evidence, failure awareness, actionability. • Includes 140+ curated scenarios, 53 failure modes, 150+ expert‑designed tasks. • Focuses on anomaly detection, diagnostics, KPI forecasting, and work order prioritization. • Moves beyond single‑agent models to multi‑agent coordination for safety‑critical operations.

Article Summaries:

  • AssetOpsBench is a new benchmark designed to evaluate AI agents in industrial asset‑management contexts, specifically for equipment such as chillers and air‑handling units. It contains 2.3 million sensor telemetry points, 140+ curated scenarios across four agent roles, 4.2 k work orders, and 53 structured failure modes. The framework assesses agents on six qualitative dimensions-task completion, retrieval accuracy, result verification, sequence correctness, clarity/justification, and hallucination rate-emphasizing multi‑agent coordination, evidence grounding, and failure awareness. Early tests show general‑purpose agents excel at surface reasoning but struggle with sustained multi‑step coordination and temporal dependencies, highlighting the need for context‑aware modeling in industrial AI systems.

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