Databricks research shows multi-step agents outperform RAG

New research from Databricks demonstrates that multi-step AI agents significantly outperform single-turn RAG systems when answering questions that require integrating data from both structured databases and unstructured documents. Traditional RAG struggles with queries that blend precise data filters with semantic searches, such as correlating sales figures with customer reviews. Databricks' multi-step agent approach, implemented as a Supervisor Agent, decomposes queries, searches data sources in parallel, and can self-correct when initial attempts fail. This architectural advantage allows agents to query data in its native format, bypassing the need for extensive data normalization often required by custom RAG pipelines.