Google Launches Agentic RAG on Gemini Enterprise, Lifting Accuracy by Up to 34%
Updated
Updated · Google Research · Jun 5
Google Launches Agentic RAG on Gemini Enterprise, Lifting Accuracy by Up to 34%
2 articles · Updated · Google Research · Jun 5
Summary
Google put its Cross-Corpus Retrieval system powered by Agentic RAG into public preview on the Gemini Enterprise Agent Platform, targeting complex enterprise queries that standard single-step RAG often answers incompletely.
Up to 34% higher accuracy came from a multi-agent workflow that breaks questions into sub-tasks, searches iteratively across data sources, and uses a “sufficient context” check to keep retrieving until evidence is complete.
On FramesQA, the system answered 90.1% of questions correctly in a cross-corpus setup with 4 possible data sources, while latency stayed within 3% of the single-corpus version.
Google said the design improves grounding, reasoning and auditability for business uses such as healthcare and other domain-specific workflows where relevant facts are scattered across separate databases.
Can Google's framework solve the coordination chaos that causes 40% of complex AI projects to fail?
With agentic AI burning 10x more tokens, can businesses truly afford the massive accuracy boost it promises?
As AI agents gain autonomy, how can companies prevent them from creating new, complex security vulnerabilities?
Agentic RAG on Gemini Enterprise: How Google’s Multi-Agent AI Drives 300% Enterprise Growth and Industry Transformation
Overview
Google's Agentic RAG framework on Gemini Enterprise marks a major leap in enterprise AI by moving beyond traditional RAG limitations. It uses a multi-agent workflow with advanced reasoning and planning, allowing the system to break down complex queries, gather relevant information, and refine answers through iterative steps. This approach makes AI responses more dependable and accurate, especially for challenging real-world enterprise questions. By enabling AI to plan, reason, and interact with various data sources, Agentic RAG empowers businesses to handle intricate information retrieval and generation tasks with greater confidence and reliability.