SOP-Bench: Complex Industrial SOPs for Evaluating LLM Agents

TL;DR

SOP-Bench is a comprehensive benchmark with over 2,000 real-world SOP tasks across multiple domains, designed to evaluate and improve LLM-based agents' ability to execute complex, multi-step procedures using human-validated, executable workflows.

Contribution

The paper introduces SOP-Bench, a novel benchmark with authentic, expert-crafted SOP tasks for evaluating LLM agents' procedural reasoning and tool orchestration capabilities.

Findings

Newer models do not always perform better; e.g., Claude 4 outperforms Claude 4.5 on ReAct tasks.

Performance varies significantly across domains and model-agent combinations.

SOP-Bench enables systematic analysis of agent performance without costly real-world deployment.

Abstract

LLM-based agents struggle to execute complex, multi-step Standard Operating Procedures (SOPs) that are fundamental to industrial automation. Existing benchmarks fail to capture the procedural complexity and tool orchestration demands of real-world workflows. We introduce SOP-Bench, a benchmark of 2,000+ tasks from human expert-authored SOPs across 12 business domains (healthcare, logistics, finance, content moderation, etc.). Using a human-AI collaborative framework, experts crafted authentic SOPs while AI generated artifacts (tools, APIs, datasets), all human-validated, yielding realistic tasks with executable interfaces and ground-truth outputs. SOP-Bench serves as a research enabler for systematically investigating agent architectures, model capabilities, and deployment considerations across diverse procedural tasks. We demonstrate its utility through illustrative experiments with a subset of frontier models across Function-Calling (FC) and ReAct agents, revealing critical insights. For example, (1) newer models do not guarantee better performance - Claude 4 family outperforms Claude 4.5 family on ReAct tasks (Claude 4 Opus: 72.4% vs. Claude 4.5 Sonnet: 63.3% task success rate), demonstrating that production upgrades require validation; (2) no single model-agent combination dominates: best performances range from 57% to 100% depending on domain. These examples illustrate how SOP-Bench enables isolating and studying specific dimensions of agent performance without costly production experiments. Our goal is not to rank model capabilities or build optimal agents, but to provide a rigorous evaluation framework that enables the researchers and practitioners to systematically investigate agent design choices, model selection, and deployment strategies. We release the benchmark at https://github.com/amazon-science/sop-bench.