@inproceedings{singh-etal-2026-agent,
    title = "Agent-Ops: A Multi-Agent Orchestration Framework for End-to-End {SOP} Automation in {E}-Commerce Operations",
    author = "Singh, Apoorva  and
      Agrawal, Sanjay  and
      Adhikari, Sayanta  and
      Puranik, Vinayak S  and
      Tiwari, Shivam  and
      Assudani, Dheeraj",
    editor = "Li, Yunyao  and
      Rehm, Georg  and
      Tu, Mei",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-industry.29/",
    pages = "436--446",
    ISBN = "979-8-89176-394-4",
    abstract = "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."
}