Bridging the Gap Between AI Agent Benchmarks and Industrial Reality

AssetOpsBench is a comprehensive benchmark and evaluation system with six qualitative dimensions that bridges the gap for agentic AI in domain-specific settings, starting with industrial Asset Lifecycle Management.

Introduction

While existing AI benchmarks excel at isolated tasks reminiscent of coding or web navigation, they often fail to capture the complexity of real-world industrial operations. To bridge this gap, we introduce AssetOpsBench, a framework specifically designed to judge agent performance across six critical dimensions of commercial applications. Unlike traditional benchmarks, AssetOpsBench emphasizes the necessity for multi-agent coordination—moving beyond `lone wolf’ models to systems that may handle complex failure modes, integrate multiple data streams, and manage intricate work orders. By specializing in these high-stakes, multi-agent dynamics, the benchmark ensures that AI agents are assessed on their ability to navigate the nuances and safety-critical demands of a real industrial environment.

AssetOpsBench is built for asset operations reminiscent of chillers and air handling units. It comprises:

• 2.3M sensor telemetry points
• 140+ curated scenarios across 4 agents
• 4.2K work orders for diverse scenarios
• 53 structured failure modes

Experts helped curate 150+ scenarios. Each scenario includes metadata: task type, output format, category, and sub-agents. The tasks designed span across:

• Anomaly detection in sensor streams
• Failure mode reasoning and diagnostics
• KPI forecasting and evaluation
• Work order summarization and prioritization

Evaluation Framework and Overall Feedback

AssetOpsBench evaluates agentic systems across six qualitative dimensions designed to reflect real operational constraints in industrial asset management. Reasonably than optimizing for a single success metric, the benchmark emphasizes decision trace quality, evidence grounding, failure awareness, and actionability under incomplete and noisy data.

Each agent run is scored across six criteria:

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

Across early evaluations, we observe that many general-purpose agents perform well on surface-level reasoning but struggle with sustained multi-step coordination involving work orders, failure semantics, and temporal dependencies. Agents that explicitly model operational context and uncertainty are likely to produce more stable and interpretable trajectories, even when final task completion is partial.

This feedback-oriented evaluation is intentional: in industrial settings, understanding why an agent fails is usually more useful than a binary success signal.

Failure Modes in Industrial Agentic Workflows

A central contribution of AssetOpsBench is the specific treatment of failure modes as first-class evaluation signals in agentic industrial workflows. Reasonably than treating failure as a binary consequence, AssetOpsBench analyzes full multi-agent execution trajectories to discover where, how, and why agent behavior breaks down under realistic operational constraints.

Failure evaluation in AssetOpsBench is implemented through a dedicated trajectory-level pipeline (TrajFM), which mixes LLM-based reasoning with statistical clustering to surface interpretable failure patterns from agent execution traces. This pipeline operates in three stages: (1) trajectory-level failure extraction using an LLM-guided diagnostic prompt, (2) embedding-based clustering to group recurring failure patterns, and (3) evaluation and visualization to support developer feedback and iteration.

Across industrial scenarios, recurrent failure modes include:

• Misalignment between sensor telemetry, alerts, and historical work orders
• Overconfident conclusions drawn under missing, delayed, or insufficient evidence
• Inconsistent aggregation of heterogeneous data modalities across agents
• Premature motion selection without adequate verification or validation steps
• Breakdowns in multi-agent coordination, reminiscent of ignored inputs or motion–reasoning mismatches

Importantly, AssetOpsBench doesn’t rely solely on a hard and fast, hand-crafted failure taxonomy. While a structured set of predefined failure categories (e.g., verification errors, step repetition, role violations) is used for consistency, the system is explicitly designed to discover latest failure patterns that emerge in practice. Additional failure modes identified by the LLM are embedded and clustered mechanically, allowing the taxonomy to evolve as latest agent designs and behaviors are evaluated.

To preserve industrial confidentiality, raw execution traces are never exposed. As an alternative, agents receive aggregated scores across six evaluation dimensions along with clustered failure-mode summaries that specify why an agent failed, without revealing sensitive data or intermediate reasoning steps. This feedback-driven design enables developers to diagnose weaknesses, refine agent workflows, and iteratively resubmit improved agents.

This failure-aware evaluation reflects the realities of commercial asset management, where cautious, degradation-aware reasoning—and the flexibility to acknowledge uncertainty, defer motion, or escalate appropriately—is usually preferable to aggressive but brittle automation.

Submit an Agent for Evaluation

AssetOpsBench-Live is designed as an open, competition-ready benchmark, and we welcome submissions of agent implementations from the community. Agents are evaluated in a controlled, privacy-preserving environment that reflects real industrial asset management constraints.

To submit an agent, developers first validate their implementation locally using a provided simulated environment, which incorporates representative sensor data, work orders, alerts, and failure-mode catalogs. Agents are then containerized and submitted for distant execution on hidden evaluation scenarios.

Submitted agents are evaluated across six qualitative dimensions—task completion, accuracy, result verification, motion sequencing, clarity, and hallucination—using a consistent, reproducible evaluation protocol. Execution traces will not be exposed; as an alternative, participants receive aggregated scores and structured failure-mode feedback that highlights where and why an agent’s reasoning or coordination broke down.

This feedback-driven evaluation loop enables iterative improvement: developers can diagnose failure patterns, refine agent design or workflow structure, and resubmit updated agents for further evaluation. Each planning-focused and execution-focused agents are supported, allowing researchers and practitioners to explore diverse agentic designs throughout the same benchmark framework.

Experiment and Observations

We performed a community evaluation where we tested two tracks:

1. Planning-oriented multi-agent orchestration
2. Execution-oriented dynamic multi-agent workflow.

Across 225 users and 300+ agents and leading open source models, listed here are the observations:

Note: Not one of the models could pass our evaluation criteria benchmark and get 85 points, which is the brink for deployment readiness.

Distribution of Failures

Across 881 agent execution traces, failure distribution was as follows:

• 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%

Beyond this, 185 traces had one latest failure pattern and 164 had multiple novel failures.

Key Error Findings

1. “Sounds Right, Is Fallacious”: Agents claim to have accomplished tasks (23.8%) and output success even after unsuccessful failure recovery (31.2%). AssetOps benchmarking is very important to uncover this in order that operators don’t act upon misinformation.
2. Tool Usage: That is the largest differentiator between high and low performing agents, with top agents having 94% tool accuracy in comparison with 61% of low performers.
3. Multi-agent Multiplies Failures: Task accuracy between single agent (68%) vs multi-agent (47%) shows the complexity multi-agent brings with context loss, asynchronous issues, and cascaded failures.
4. Domain Knowledge: Agents with access to failure mode databases and maintenance manuals performed higher. Nonetheless, RAG knowledge wasn’t all the time used appropriately, suggesting a necessity for structured reasoning.
5. Ambiguity: Missing sensors, conflicting logs, and vague operator descriptions caused the success rate to drop 34%. Agents should have clarification strategies embedded.

Where to start?