SOP-Bench: Complex Industrial SOPs for Evaluating LLM Agents (2506.08119v1)

Abstract: 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.

Evaluating the Agentic Capabilities of LLMs with SOP-Bench

The paper "SOP-Bench: Complex Industrial SOPs for Evaluating LLM Agents" presents a detailed framework and benchmark designed to test the effectiveness of LLM agents in executing real-world Standard Operating Procedures (SOPs). The authors have identified significant gaps in LLM capabilities, specifically in their ability to handle complex, long-horizon workflows adhering strictly to SOPs, which are critical for industrial automation.

Overview

The authors introduce SOP-Bench, a synthetic benchmark that aims to address the lack of publicly available benchmarks capturing the complexity and nuances of industrial SOPs. SOP-Bench comprises three main contributions:

1. Synthetic Data Generation Framework: The authors developed a novel framework capable of generating realistic, industry-grade SOPs. This framework rigorously tests LLM-based agents on their planning, reasoning, and tool-use capabilities.
2. SOP-Bench Benchmark: A comprehensive benchmark consisting of over 1,800 tasks spanning 10 industrial domains. Each task is paired with APIs, tool interfaces, and human-validated test cases.
3. Evaluation of Agent Architectures: The authors evaluated two prominent agent architectures: Function-Calling Agent and ReAct Agent, observing average task success rates of 27% and 48% respectively. They highlight a critical failure: when faced with a large tools registry, the agents frequently invoked incorrect tools, revealing a stark gap between current LLM capabilities and the demands of SOP-based automation.

Strong Numerical Results and Claims

The paper provides compelling numerical insights showing how LLM agents grapple with SOP tasks. Notably, in cases where unnecessary tools were introduced to the registry, agents invoked incorrect tools almost 100% of the time, underscoring the need for refined tool selection strategies. The disparity in performance between Function-Calling Agent and ReAct Agent, in terms of success rates, is significant, suggesting architectural differences impact SOP execution capabilities.

Implications and Speculations

The authors assert substantial theoretical and practical implications for the future development of AI:

• Theoretical Context: The paper emphasizes the complexity and domain-specific requirements of SOPs, advocating for bespoke benchmarks and methodologies to evaluate LLM agents effectively.
• Practical Applications: The authors propose SOP-Bench as a tool for evaluating LLM agents before deployment in domains such as healthcare, content moderation, and industrial logistics. This has the potential to streamline workflows and enhance automation reliability.
• Future AI Developments: The insights derived from SOP-Bench could inform LLM architecture improvements, particularly in agentic capabilities. The benchmark may guide the development of agents with better tool-use strategies, state-tracking, and decision-making processes, ultimately fostering more robust AI-powered industrial automation.

Conclusion

The introduction of SOP-Bench marks a critical step in evaluating the agentic capabilities of LLM-based agents using realistic SOPs. By highlighting the substantial gap between current agent performance and SOP execution demands, the paper calls for continued refinement in LLM architectures and invites the research community to expand SOP-Bench with additional domain-specific SOPs. This work sets the stage for future innovations in AI agent deployment within complex industrial environments.