A recent engineering post from a Chinese AI company, Kunming Intelligence, presents a multi-agent architecture for text-to-SQL and data analysis. The core idea is that a single model generating SQL, analysis, and reports in one step degrades sharply in complex scenarios. Instead, they decompose the pipeline into four specialized agents: query planning, SQL generation, insight analysis, and report generation. Each agent has a clear responsibility, with inputs and outputs designed for cross-validation and collaboration. The post details the engineering implementation, including agent interfaces and coordination mechanisms. For developers building LLM-powered data tools, this pattern offers a practical alternative to monolithic prompts. It highlights how task decomposition can improve reliability and output quality, especially when dealing with multi-step analytical queries. The approach is relevant to teams working on natural language interfaces for databases, BI tools, and automated reporting systems.
A Chinese engineering blog describes a multi-agent system for text-to-SQL and data analysis, decomposing the task into four specialized agents to improve accuracy and quality in complex scenarios.