Google Research· Research

Unlocking dependable responses with Gemini Enterprise Agent Platform’s Agentic RAG

Cyrus Rashtchian, Research Scientist, and Da-Cheng Juan, Engineering Manager, Google Research We introduce our new agentic RAG framework. Based on a collaboration between Google Research and Google Cloud, our multi-agent workflow goes beyond standard RAG by breaking down complex enterprise queries and iteratively searching for sufficient context before generating dependable responses. Quick links RAG Engine Cross Corpus Retrieval Share Copy link × Current single-step retrieval-augmented generation (RAG) systems weren’t designed for the multi-source, multi-hop queries of modern business workflows. If, for example, the query is, "What are the specs of the server used in Project X?", the system might find documents about Project X, but those documents might only mention a server ID. It won't know to take that ID and perform a second search in another database to find the specs. The result is a partial answer or a "not found" response because the information is spread across different "islands" of data, requiring deeper exploration to find the facts. …

Announced

05 Jun 2026 11:26 UTC