Databricks tested a stronger model against its multi-step agent on hybrid queries. The stronger model still lost by 21%.
Databricks' research highlights the limitations of single-turn retrieval-augmented generation (RAG) systems when handling complex queries that combine structured and unstructured data, demonstrating that a multi-step agentic approach can significantly improve performance on such tasks. Their Supervisor Agent architecture enables parallel querying of diverse data sources without requiring normalization, offering a more scalable solution for enterprises facing hybrid data challenges.
For AI professionals focused on AI deployment and infrastructure, Databricks' research highlights a key architectural insight: traditional single-turn RAG systems struggle with queries that require integration of structured and unstructured data due to their inability to efficiently handle hybrid data tasks. The multi-step agentic approach, exemplified by Databricks’ Supervisor Agent, shows a performance improvement of over 20% on benchmarks by employing parallel tool decomposition and declarative configuration, suggesting that adopting such an architecture can streamline complex enterprise data queries without extensive data normalization. This insight can guide decisions on whether to build custom RAG pipelines or leverage multi-step agents for enterprise AI applications.