Can an LLM read a 30+ page Standard Operating Procedure (SOP) and produce a team of agents that reliably execute it? Industrial SOPs bury workflow logic in prose, leave inter-step dependencies implicit, and scatter referenced entities across sections, forcing planners to parse, sequence, and tool-map within one context window. We argue that the missing primitive is procedural chunking: decomposing a document into self-contained executable units. Unlike RAG-style chunking, each chunk must be independently executable and chunks must jointly form a valid DAG encoding data and process dependencies, turning a prose SOP into a compact orchestrable workflow graph. We present ProChunker, a three-phase self-optimizing LLM pipeline (Boundary Detection, Metadata Extraction, Dependency Construction) with Stochastic Best-of-N prompt exploration guided by five custom metrics (Boundary Clarity, Entity Reference Coverage, Agent Self-Containedness Agreement, Step Coverage, Graph Validity); we then fine-tune Qwen3-1.7B on the pipeline’s own phase outputs, cutting latency by ∼27 min. Across three corpora (500 BioProBench protocols, six Industrial SOPs, 14 SOP-Bench documents), ProChunker improves the five-metric composite over the strongest baseline by +0.036 to +0.111 across corpora. Our study shows ProChunker increases agent construction success rate by +17% and resolves >90% of procedural ambiguity.

While Large Language Models excel at reasoning and language understanding, they struggle with multi-step operational workflows requiring precise procedural adherence, which is fundamental for industrial automation. Existing SOP-guided agents assume well-defined procedures and structured APIs, failing to address enterprise realities like incomplete SOPs, dynamic web interfaces, and unpredictable document formats. We present Agent-Ops, an end-to-end multi-agent framework automating Standard Operating Procedures in e-commerce. Agent-Ops contributes: (1) SOP Groomer, a human-AI framework transforming ambiguous documentation into automation-ready specifications, improving accuracy by 13.2%, (2) WebAgent, achieving 91.3% task completion and 86.5% execution consistency through demonstration-based learning, and (3) a Document Verification Agent performing multi-lingual validation across tax invoices, certificates, and supply chain documents with 94.2% accuracy. Deployed across seven SOP categories in three geographic regions, Agent-Ops achieves 85-97% end-to-end accuracy while reducing case resolution from 30 to 5 minutes (83% reduction). Production deployment with over 1000 Account Managers validates that LLM-based agents achieve enterprise-grade reliability when augmented with robust web automation, comprehensive document understanding, and systematic SOP refinement.