Google Research published a deep dive into Agentic RAG, a retrieval-augmented generation technique purpose-built for the Gemini Enterprise Agent Platform. The approach aims to solve one of the hardest problems in enterprise AI: producing dependable, verifiable responses from large language models.
The system adds an agentic layer on top of standard RAG pipelines, enabling the model to actively reason about which documents to retrieve, when to retrieve more, and how to synthesize conflicting information. This reduces hallucinations and gives users clearer provenance for each claim in a response.
The blog post includes architectural diagrams and performance benchmarks, showing that Agentic RAG improves factual accuracy on enterprise knowledge bases compared to naive retrieval approaches. The system is designed to integrate with existing enterprise data sources and access controls.