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

Summary

AssetOpsBench is an evaluation framework designed to benchmark agentic AI systems within industrial Asset Lifecycle Management operations, such as chillers and air handling units. The benchmark incorporates 2.3 million sensor telemetry points, 4,200 work orders, 53 structured failure modes, and over 140 curated scenarios spanning four agent roles. Systems are assessed across six qualitative criteria, including task completion, retrieval accuracy, result verification, sequence correctness, clarity and justification, and hallucination rate. Evaluation traces are analyzed using a trajectory-level diagnostic pipeline called TrajFM, which extracts and clusters failure patterns. In community tests covering over 300 agents, no evaluated model family reached the 85-point readiness threshold.

Context

Existing AI benchmarks excel at isolated tasks like coding or navigation but fail to capture the complex failure modes, multi-data-stream integration, and multi-agent coordination demands of real-world, safety-critical industrial operations.

Approach / What changed

The creators built AssetOpsBench, a benchmark featuring 140+ curated scenarios, 2.3M telemetry points, and 4.2K work orders evaluated across six qualitative dimensions. It employs TrajFM, a trajectory-level pipeline using LLM diagnostic prompts and embedding-based clustering to detect failure patterns without exposing raw execution traces.

Takeaways

• None of the evaluated model families reached the 85-point threshold required for deployment readiness, with GPT-4.1 scoring highest at 68.2 in planning and 72.4 in execution.
• Multi-agent setups dropped task accuracy from 68% in single-agent architectures to 47% due to context loss, asynchronous issues, and cascading failures.
• The primary failure modes across 881 traces were ineffective error recovery (31.2%), overstated completion (23.8%), and formatting issues (21.4%).

Related reading

• Amazon · Aug 21, 2026

  SOP-Bench: A new benchmark for evaluating AI agents on real business procedures

  AI agents often struggle to execute standard operating procedures because real-world business workflows contain implicit domain knowledge and procedural ambiguity that standard benchmarks overlook. To evaluate agent reliability, researchers introduced SOP-Bench, an open framework featuring more than 2,000 runnable tasks across 12 industrial domains paired with functioning tool interfaces and ground-truth answers. Domain experts authored the workflows, while an Anthropic Claude 3.5 Sonnet v2 model generated mock APIs, tool code, and edge-case datasets before expert verification. Experiments evaluating function-calling and reasoning agents across 11 frontier models revealed that newer models did not consistently outperform older versions, and providing 20 extraneous tools nearly halved task success. The benchmark, baseline agents, and evaluation code are released on GitHub and HuggingFace for reproducible testing.

  Rohith Nama, Nandi Subhrangshu

• Hugging Face · Sep 22, 2025