Why I Stopped Using One Agent and Built a Multi-Agent Pipeline Instead

Single AI agents often struggle with complex tasks like text-to-SQL because they must simultaneously handle competing goals like intent parsing, schema mapping, and query generation in one context. A more effective solution is a multi-agent pipeline, which breaks the problem down into a series of specialized agents that each tackle a single, focused task. For instance, one agent decomposes the user's request, another maps it to the database schema, a third builds the query, and a crucial "critic" agent validates the result. This modular architecture, orchestrated using tools like LangGraph, allows each agent to work with a clean context, significantly improving the accuracy and reliability of the final output.