Databricks shows multi-step AI agents outperform traditional RAG by 20%+ on hybrid data tasks, proving architecture—not model strength—is the limiting factor

Databricks' multi-step agentic approach achieved 20%+ performance gains over single-turn RAG baselines on Stanford's STaRK benchmark suite

Current RAG systems fail when questions require joining structured data with unstructured content, like sales figures with customer reviews

Testing revealed the performance gap is an architectural limitation, not a model quality issue—even stronger models underperformed by 21% on hybrid queries

Research validates Databricks' earlier instructed retriever work, which improved unstructured data retrieval using metadata-aware query techniques