Enterprise teams often hit a wall when they need answers that span multiple data sources. A question like “What are the specs of the server used in Project X?” may return a server ID from one document, but the system stops there and never looks up the detailed specs in another database. The result is incomplete or a “not found” reply, forcing users to manually stitch together information or accept partial answers.
Google’s new agentic RAG framework solves this exact problem. Instead of a single‑step match, it treats a query as a research project handled by a team of specialized agents. An Orchestrator decides whether the request needs more than one hop. A Planner maps out which data stores to check. A Query Rewriter turns a vague ask into precise search terms. A Search Fanout sends those queries to all relevant repositories at once. The results are gathered, and a Sufficient Context Agent reviews them against the original prompt. If anything is missing, it logs exactly what is lacking and triggers another round of rewriting and searching. Only when the context is judged sufficient does a Synthesis Agent craft a final, accurate answer.
Because the system persists until it has enough information, it avoids guessing or prematurely saying it doesn’t know. Tests on the FramesQA benchmark show the agentic RAG reaches 90.1 % accuracy while correctly choosing the right corpus from four options, with latency staying within 3 % of a single‑source run. Compared to standard RAG, factuality gains of up to 34 % have been reported.
This approach fits any enterprise workflow that pulls data from siloed systems—healthcare teams combining medication, diet, and allergy records; engineering teams linking server IDs to spec sheets; finance groups joining budget numbers with timeline logs. The Cross‑Corpus Retrieval feature, now in public preview on the Gemini Enterprise Agent Platform, delivers these capabilities out of the box.
In short, the agentic RAG gives enterprises a dependable way to answer complex, multi‑source questions without manual effort, improving both accuracy and user trust.
#AI #Product #EnterpriseSearch #RAG #Gemini #Innovation