@inproceedings{santos-etal-2026-multi,
    title = "Multi-Agent Architecture with {RAG} and Dynamic Context Windows for Text-to-{SQL} Optimization",
    author = "Santos, Willgnner Ferreira  and
      Santos, Paulo Victor dos  and
      Martins, Marcella Scoczynski Ribeiro  and
      Lekakis, Larissa Freire  and
      Rosa, Frederico Lemes  and
      Costa, Bruno Matheus  and
      Filho, Miguel Alves Pereira  and
      Montalv{\~a}o, Isabella Alves",
    editor = "Souza, Marlo  and
      de-Dios-Flores, Iria  and
      Santos, Diana  and
      Freitas, Larissa  and
      Souza, Jackson Wilke da Cruz  and
      Ribeiro, Eug{\'e}nio",
    booktitle = "Proceedings of the 17th International Conference on Computational Processing of {P}ortuguese ({PROPOR} 2026) - Vol. 1",
    month = apr,
    year = "2026",
    address = "Salvador, Brazil",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-dnd/2026.propor-1.100/",
    pages = "988--993",
    ISBN = "979-8-89176-387-6",
    abstract = "Natural language interfaces supported by LLMs have been used to translate user questions into SQL queries, but sending the complete database schema in each prompt entails high token consumption and computational cost, especially in corporate databases with hundreds of tables. This work presents a multi-agent Text-to-SQL architecture with dynamic context windows, which combines RAG and metadata dictionaries to select, at query time, only the relevant tables and columns. In a case study with Firebird enterprise databases, the approach reduces by an average of 84.4{\%} the number of processed tokens, resulting in more efficient queries without loss of quality, thereby contributing to the democratization of access to corporate databases."
}