Standard retrieval-augmented generation has a stubborn failure mode: when the first search returns partial results, the model either hallucinates or gives up. Google Research and Google Cloud just shipped a fix in the form of an agentic RAG framework that plans, searches, checks its own work, and loops back for more information when the evidence is thin.

The system is now available as a public preview inside the Gemini Enterprise Agent Platform under the Cross-Corpus Retrieval feature. Compared to standard RAG, the framework increases accuracy on factuality datasets by up to 34%, and it can route queries across multiple unrelated corpora without a meaningful latency penalty.

Why vanilla RAG breaks on real enterprise questions

Current single-step RAG systems weren't designed for the multi-source, multi-hop queries of modern business workflows. Ask something like "What are the specs of the server used in Project X?" and a typical pipeline retrieves the Project X docs, sees a server ID, and stops. It won't know to take that ID and perform a second search in another database to find the specs, so you get a half answer or a "not found."

The agentic version treats retrieval less like a single lookup and more like a small research team that decomposes the question, dispatches parallel searches, and refuses to answer until the evidence actually covers the prompt.