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.