{"ID":2888322,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2507.23429","arxiv_id":"2507.23429","title":"Chatting with your ERP: A Recipe","abstract":"This paper presents the design, implementation, and evaluation behind a Large Language Model (LLM) agent that chats with an industrial production-grade ERP system. The agent is capable of interpreting natural language queries and translating them into executable SQL statements, leveraging open-weight LLMs. A novel dual-agent architecture combining reasoning and critique stages was proposed to improve query generation reliability.","short_abstract":"This paper presents the design, implementation, and evaluation behind a Large Language Model (LLM) agent that chats with an industrial production-grade ERP system. The agent is capable of interpreting natural language queries and translating them into executable SQL statements, leveraging open-weight LLMs. A novel dual...","url_abs":"https://arxiv.org/abs/2507.23429","url_pdf":"https://arxiv.org/pdf/2507.23429v1","authors":"[\"Jorge Ruiz Gómez\",\"Lidia Andrés Susinos\",\"Jorge Alamo Olivé\",\"Sonia Rey Osorno\",\"Manuel Luis Gonzalez Hernández\"]","published":"2025-07-31T11:09:50Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.DB\",\"cs.ET\",\"cs.HC\",\"cs.MA\"]","methods":"[\"Large Language Model\",\"Language Model\"]","has_code":false}