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.