SOP-Bench: Complex Industrial SOPs for Evaluating LLM Agents
Problem Statement
LLM agents increasingly target industrial automation but no benchmark captures the procedural complexity, tool-calling demands, and domain diversity of real-world SOPs. Existing benchmarks are too simplistic to reveal meaningful differences in agent architectures or model capabilities, forcing costly production experiments for validation. This gap prevents researchers from systematically studying agent design choices, model selection, and deployment strategies.
Key Novelty
- First large-scale benchmark (2,000+ tasks) grounded in human expert-authored SOPs spanning 12 diverse industrial domains with authentic procedural complexity
- Human-AI collaborative construction framework where domain experts author SOPs while AI generates executable artifacts (tools, APIs, datasets), all human-validated for realism
- Systematic evaluation framework enabling controlled comparison of agent architectures (Function-Calling vs. ReAct) and frontier models without production deployment
Evaluation Highlights
- Claude 4 Opus achieves 72.4% task success on ReAct tasks vs. Claude 4.5 Sonnet at 63.3%, showing newer models do not guarantee better performance
- Best model-agent performance ranges from 57% to 100% depending on domain, revealing no single dominant model-agent combination across industrial contexts
Breakthrough Assessment
Methodology
- Domain experts author authentic, multi-step SOPs across 12 industrial domains (healthcare, logistics, finance, content moderation, etc.), ensuring real-world procedural fidelity
- AI models generate corresponding executable artifacts—tools, APIs, datasets, and ground-truth outputs—which are then validated and refined by human experts for correctness and realism
- Benchmark tasks are evaluated using frontier LLM agents under two architectures (Function-Calling and ReAct), with task success rate as the primary metric to surface domain- and architecture-specific performance differences
System Components
2,000+ procedural tasks derived from human-authored SOPs across 12 business domains, each with defined steps, decision points, and expected outcomes
AI-generated and human-validated tools, APIs, and datasets that ground tasks in realistic, callable interfaces mirroring production environments
Evaluation harness for agents that use structured tool-calling APIs to execute SOP steps, measuring step-level and task-level success
Evaluation harness for reasoning-and-acting agents that interleave thought generation and tool use, enabling comparison with FC agents across the same tasks
Pipeline combining domain expert SOP authoring with AI artifact generation and human validation to ensure benchmark authenticity and scalability
Results
| Model / Agent | Architecture | Domain | Task Success Rate |
|---|---|---|---|
| Claude 4 Opus | ReAct | Aggregate | 72.4% |
| Claude 4.5 Sonnet | ReAct | Aggregate | 63.3% |
| Best model-agent combo | Varies | Best domain | ~100% |
| Best model-agent combo | Varies | Worst domain | ~57% |
Key Takeaways
- Do not assume production LLM upgrades improve agent performance—always validate on domain-specific procedural benchmarks before deployment, as newer models (e.g., Claude 4.5) can underperform older ones (Claude 4 Opus) on agentic tasks
- Agent architecture choice (Function-Calling vs. ReAct) interacts strongly with domain and model, meaning practitioners must co-optimize model selection and agent design rather than treating them independently
- SOP-Bench provides a cost-effective evaluation sandbox for industrial AI teams to stress-test agent pipelines across diverse procedural workflows before committing to expensive production experiments
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.