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

Standard RAG systems have a blind spot: they search once, grab what they find, and generate an answer whether or not that answer is actually complete. Ask something that requires hopping between databases — say, a server ID in one document that needs specs from an entirely different system — and a vanilla setup either gives you half the picture or shrugs and says it couldn't find anything. Google Research says its new Cross-Corpus Retrieval system, powered by what it calls agentic RAG and now live as a public preview in Gemini Enterprise Agent Platform, is built specifically to close that gap.

The architecture splits the work across specialized agents rather than relying on one retrieval step. A Planner Agent figures out which databases need checking. A Query Rewriter turns a vague question into several sharper search queries. A Search Fanout Agent fires those queries at multiple sources at once. That part isn't new — plenty of multi-agent RAG frameworks look roughly like this. What Google added is a Sufficient Context Agent, which acts like a quality inspector at the end of the line, reading the retrieved snippets and a draft answer to decide whether the system actually has what it needs, or whether it's about to bluff its way through a gap.

The hospital example in Google's writeup makes the case well. A doctor asks about a patient's discharge meds, diet restrictions, and any allergic reactions during a knee surgery stay. The first search pulls meds and diet info easily but comes up dry on allergies. A standard RAG system would likely stop there and either guess or admit defeat. Here, the Sufficient Context Agent flags the specific gap — no allergy data — and sends the Query Rewriter back out with a narrower search for terms like