Current single-step retrieval-augmented generation (RAG) systems weren’t designed for the multi-source, multi-hop queries of modern business workflows. If, for example, the query is, "What are the specs of the server used in Project X?", the system might find documents about Project X, but those documents might only mention a server ID. It won't know to take that ID and perform a second search in another database to find the specs. The result is a partial answer or a "not found" response because the information is spread across different "islands" of data, requiring deeper exploration to find the facts.Enter “agentic RAG”, which plans, reasons, and iteratively interacts with data sources, enabling the handling of complex queries to increase dependability and accuracy.Today, we’re excited to introduce Google’s Gemini Enterprise Agent Platform-hosted version of Cross-Corpus Retrieval powered by Agentic RAG. Like other multi-agent RAG frameworks, ours employs various agents that work together to reliably answer complex queries. Unlike other multi-agent frameworks, ours incorporates sufficient context to confirm if there is enough information for an accurate answer. Compared to standard RAG, our framework increases accuracy on factuality datasets by up to 34%. We also evaluated our system with proprietary, internal datasets and found that we achieve better grounding and improved reasoning accuracy on multiple domain-specific tasks.
Unlocking dependable responses with Gemini Enterprise Agent Platform’s Agentic RAG
Current single-step retrieval-augmented generation (RAG) systems weren’t designed for the multi-source, multi-hop queries of modern business workflows. If, for example, the query is, "What are the specs of the server used in Project X?", the system might find documents about Project X, but those documents might only mention a server ID. It won't know to take that ID and perform a second search in another database to find the specs. The result is a partial answer or a "not found" response because the information is spread across different "islands" of data, requiring deeper exploration to find the facts.Enter “agentic RAG”, which plans, reasons, and iteratively interacts with data sources, enabling the handling of complex queries to increase dependability and accuracy.Today, we’re excited to introduce Google’s Gemini Enterprise Agent Platform-hosted version of Cross-Corpus Retrieval powered by Agentic RAG. Like other multi-agent RAG frameworks, ours employs various agents that work together to reliably answer complex queries. Unlike other multi-agent frameworks, ours incorporates sufficient context to confirm if there is enough information for an accurate answer. Compared to standard RAG, our framework increases accuracy on factuality datasets by up to 34%. We also evaluated our system with proprietary, internal datasets and found that we achieve better grounding and improved reasoning accuracy on multiple domain-specific tasks.
