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

TL;DR

AssetOpsBench is a new evaluation framework designed to bridge the gap between general AI benchmarks and the complexities of industrial Asset Lifecycle Management. It shifts the focus from isolated task completion to multi-agent coordination, safety-critical failure analysis, and performance under noisy, real-world industrial data.

Industrial-Scale Evaluation Dataset

AssetOpsBench provides a specialized environment for testing AI agents tasked with managing industrial assets like chillers and air handling units. The benchmark is grounded in a massive dataset designed to simulate industrial reality:

  • Sensor Data: 2.3 million sensor telemetry points.
  • Scenarios: Over 140 curated scenarios across four agent roles, with 150+ scenarios developed with expert input.
  • Work Orders: 4,200 work orders covering diverse operational scenarios.
  • Failure Modes: 53 structured failure modes.

Tasks within the benchmark span four primary categories: anomaly detection in sensor streams, failure mode reasoning and diagnostics, KPI forecasting and analysis, and the summarization and prioritization of work orders.

Six-Dimensional Qualitative Evaluation

Rather than relying on a binary success metric, AssetOpsBench evaluates agentic systems across six qualitative dimensions to ensure decisions are actionable and grounded in evidence:

  1. Task Completion
  2. Retrieval Accuracy
  3. Result Verification
  4. Sequence Correctness
  5. Clarity and Justification
  6. Hallucination Rate

Early evaluations indicate that while general-purpose agents handle surface-level reasoning well, they struggle with multi-step coordination, temporal dependencies, and failure semantics. The framework prioritizes understanding why an agent fails over a simple success signal, which is critical for high-stakes industrial environments.

Trajectory-Level Failure Analysis (TrajFM)

AssetOpsBench treats failure modes as first-class evaluation signals. Through a dedicated pipeline called TrajFM, the system analyzes full multi-agent execution trajectories to identify specific breakdown points using a three-stage process:

  1. Extraction: LLM-guided diagnostic prompts extract failures from trajectories.
  2. Clustering: Embedding-based clustering groups recurring failure patterns.
  3. Visualization: Analysis and visualization support developer iteration.

This system is designed to discover new failure patterns dynamically rather than relying solely on a fixed taxonomy. Common industrial failure modes identified include misalignment between telemetry and work orders, overconfidence despite missing evidence, and breakdowns in multi-agent coordination (e.g., ignored inputs).

Performance Observations and Benchmarks

Community evaluations across 225 users and 300+ agents revealed that no model currently meets the 85-point threshold required for deployment readiness.

Model Performance Comparison

Model Family Best Planning Score Best Execution Score Key Limitation
GPT-4.1 68.2 72.4 Hallucinated completion on complex workflows
Mistral-Large 64.7 69.1 Struggled with multi-hop tool sequences
LLaMA-4 Maverick 66.0 70.8 Missed clarifying questions
LLaMA-3-70B 52.3 58.9 Collapsed under multi-agent coordination

Distribution of Failures

Analysis of 881 execution traces showed the following failure distribution:

  • Ineffective Error Recovery: 31.2%
  • Overstated Completion: 23.8%
  • Formatting Issues: 21.4%
  • Unhandled Tool Errors: 10.3%
  • Ignored Feedback: 8.0%
  • Other: 5.3%

Key Technical Findings

  • The "Sounds Right, Is Wrong" Problem: 23.8% of agents claim task completion and output success even after failing to recover from an error (31.2%), posing a significant risk to industrial operators.
  • Tool Accuracy as a Differentiator: High-performing agents achieved 94% tool accuracy, compared to 61% for low performers.
  • Multi-Agent Complexity: Task accuracy drops from 68% for single agents to 47% for multi-agent systems due to context loss and cascaded failures.
  • Knowledge Integration: While access to failure mode databases and maintenance manuals improves performance, RAG (Retrieval-Augmented Generation) knowledge is not always used correctly, indicating a need for more structured reasoning.
  • Impact of Ambiguity: Missing sensors, conflicting logs, and vague descriptions reduced success rates by 34%, highlighting the need for embedded clarification strategies.

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