SOP-Bench: Complex Industrial SOPs for Evaluating LLM Agents

2025-06-09

AI Summary

Existing LLM evaluation benchmarks lack publicly available, industrial-standard SOP-aligned resources that capture the structural, constraint-driven, and domain-specific nature of real-world operational procedures—hindering rigorous assessment of LLMs’ planning and execution capabilities in long-horizon, multi-tool, rule-intensive tasks. Method: We introduce SOP-Bench, the first benchmark explicitly designed for industrial SOPs. It features an original synthetic SOP generation framework, yielding an open-source dataset of 1,800+ tasks across 10 industrial domains. We formalize API/tool interface modeling and incorporate human-validated test protocols, and conduct systematic evaluation using Function-Calling and ReAct agents. Contribution/Results: Experiments reveal critically low success rates (27%–48%) across models; tool misuse escalates sharply with toolset scale—approaching 100%—and performance varies significantly across domains. These findings underscore the urgent need for domain-adaptive fine-tuning and architectural improvements to support robust SOP-constrained reasoning.

Technology Category

• Planning, Routing, and Scheduling: Planning with Language Models
• Constraint Satisfaction and Optimization: Solvers and Tools
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Economics, Online Markets and Human Computation: Cost models of using LLMs in production systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Large Language Models (LLMs) demonstrate impressive general-purpose reasoning and problem-solving abilities. However, they struggle with executing complex, long-horizon workflows that demand strict adherence to Standard Operating Procedures (SOPs), a critical requirement for real-world industrial automation. Despite this need, there is a lack of public benchmarks that reflect the complexity, structure, and domain-specific nuances of SOPs. To address this, we present three main contributions. First, we introduce a synthetic data generation framework to create realistic, industry-grade SOPs that rigorously test the planning, reasoning, and tool-use capabilities of LLM-based agents. Second, using this framework, we develop SOP-Bench, a benchmark of over 1,800 tasks across 10 industrial domains, each with APIs, tool interfaces, and human-validated test cases. Third, we evaluate two prominent agent architectures: Function-Calling and ReAct Agents, on SOP-Bench, observing average success rates of only 27% and 48%, respectively. Remarkably, when the tool registry is much larger than necessary, agents invoke incorrect tools nearly 100% of the time. These findings underscore a substantial gap between current agentic capabilities of LLMs and the demands of automating real-world SOPs. Performance varies significantly by task and domain, highlighting the need for domain-specific benchmarking and architectural choices before deployment. SOP-Bench is publicly available at http://sop-bench.s3-website-us-west-2.amazonaws.com/. We also release the prompts underpinning the data generation framework to support new domain-specific SOP benchmarks. We invite the community to extend SOP-Bench with SOPs from their industrial domains.

Problem

• LLMs struggle with complex industrial SOP workflows
• Lack of public benchmarks for SOP complexity
• Agents perform poorly on domain-specific SOP tasks

Innovation

• Synthetic data framework for industrial SOPs
• SOP-Bench with 1800 tasks across domains
• Evaluation of Function-Calling and ReAct Agents