aiqbench takeAI Products

Google Research's new Agentic RAG framework addresses a critical failure mode in enterprise search by enabling multi-hop, multi-source queries through an iterative planning and reasoning process. This approach, integrated into the Gemini Enterprise Agent Platform, improves factuality accuracy by up to 34% compared to standard single-step retrieval methods. The system's persistence in gathering sufficient context before responding makes it particularly relevant for complex, domain-specific enterprise tasks where partial answers are unacceptable.

Source coveringMarkTechPost

Google Research Adds Agentic RAG to Gemini Enterprise Agent Platform with a Sufficient Context Agent for multi-hop queries

MarkTechPostJune 8, 20265 min read

Google Research details an agentic RAG framework in Gemini Enterprise Agent Platform. A Sufficient Context Agent re-searches until multi-hop, multi-source queries have enough grounding to answer. The framework raises factuality accuracy up to 34% versus standard RAG. The post Google Research Adds Agentic RAG to Gemini Enterprise Agent Platform with a Sufficient Context Agent for multi-hop queries appeared first on MarkTechPost.

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…

Excerpt shown under the publisher's RSS terms · 89 words · full text and figures are on the publisher's site

Read at MarkTechPost ↗Ask about this article