Google’s Agentic RAG keeps searching until enterprise answers hold up

The useful shift in enterprise RAG is not a bigger search box; it is knowing when the retrieved evidence is still incomplete. On June 5, 2026, Google Research detailed Agentic RAG for Gemini Enterprise Agent Platform, a public preview feature that adds planning, routing, iterative retrieval, and a sufficient-context check to multi-source business questions.

The failure mode is easy to recognize. A user asks for the specifications of the server used in Project X. One document mentions Project X but only contains a server ID, while the actual specifications live in another database. Standard RAG can stop after the first retrieval step and produce a partial answer or a false “not found.” Google’s framework treats that as a workflow problem.

The system splits the job across agents. A Root Agent parses the request, a Planner Agent decides which data paths matter, a Query Rewriter turns broad questions into focused searches, and a Search Fanout Agent queries multiple retrieval sources. The distinctive piece is the Sufficient Context Agent, which reviews the retrieved snippets, an intermediate draft, and an explicit analysis of missing information before the final answer is allowed through.

If the evidence is thin, the workflow loops. In Google’s clinical example, a doctor asks for discharge medications, dietary restrictions, and allergic reactions after surgery. If the first search finds medication and diet notes but not allergy evidence, the sufficient-context step flags the gap and gives targeted feedback. The query rewriter can then search for terms such as rashes or adverse events, instead of letting the system answer from an incomplete record.

Google reports a measurable gain rather than only a product diagram. On factuality datasets, the framework improved accuracy by up to 34% over standard RAG. In the FramesQA evaluation, it used 824 queries and a corpus of 2,676 PDF documents. In the cross-corpus setting, where the planner had to choose the right source among four datasets, the system answered 90.1% of questions correctly, with latency staying within about 3% of the single-corpus version on average.

The enterprise stake is auditability. Agentic systems are being asked to work across finance records, customer files, project logs, policies, and support systems. A response that explains what it found, what it lacked, and why it searched again is easier to challenge than a fluent answer built from the first retrieved chunks. The next test is whether customers can run this loop with real permissions, logging, latency budgets, and retrieval costs intact.