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    <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.</abstract>
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%0 Conference Proceedings
%T Agent-Ops: A Multi-Agent Orchestration Framework for End-to-End SOP Automation in E-Commerce Operations
%A Singh, Apoorva
%A Agrawal, Sanjay
%A Adhikari, Sayanta
%A Puranik, Vinayak S.
%A Tiwari, Shivam
%A Assudani, Dheeraj
%Y Li, Yunyao
%Y Rehm, Georg
%Y Tu, Mei
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, USA
%@ 979-8-89176-394-4
%F singh-etal-2026-agent
%X 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.
%U https://aclanthology.org/2026.acl-industry.29/
%P 436-446

Markdown (Informal)

[Agent-Ops: A Multi-Agent Orchestration Framework for End-to-End SOP Automation in E-Commerce Operations](https://aclanthology.org/2026.acl-industry.29/) (Singh et al., ACL 2026)

• Agent-Ops: A Multi-Agent Orchestration Framework for End-to-End SOP Automation in E-Commerce Operations (Singh et al., ACL 2026)

ACL

• Apoorva Singh, Sanjay Agrawal, Sayanta Adhikari, Vinayak S Puranik, Shivam Tiwari, and Dheeraj Assudani. 2026. Agent-Ops: A Multi-Agent Orchestration Framework for End-to-End SOP Automation in E-Commerce Operations. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (ACL 2026), pages 436–446, San Diego, California, USA. Association for Computational Linguistics.