Google Research team has introduced a new agentic RAG framework. It is built into the Gemini Enterprise Agent Platform. It powers a feature called Cross-Corpus Retrieval, now in public preview.
The target is a known failure mode in enterprise search. Standard single-step RAG was not built for multi-source, multi-hop queries. Ask “What are the specs of the server used in Project X?” The system may find a document naming a server ID. It will not know to take that ID and search a second database for specs. The answer comes back partial, or as “not found.”
What is Google’s New Agentic RAG
Agentic RAG plans, reasons, and iteratively interacts with data sources. It handles complex queries to increase dependability and accuracy. Google’s version is the Gemini Enterprise Agent Platform-hosted Cross-Corpus Retrieval powered by Agentic RAG. Like other multi-agent RAG frameworks, it uses agents that work together. Unlike them, it adds a sufficient context check before generating a response. Compared to standard RAG, it increases accuracy on factuality datasets by up to 34%. Google’s research team also tested it on proprietary internal datasets. It reports better grounding and improved reasoning accuracy on domain-specific tasks.
