"The new agentic retrieval capability enhances relevance in conversational AI by up to 40% compared to traditional RAG, marking a significant advancement in intelligent querying."
"This process involves analyzing chat history and original queries to plan focused subqueries that execute in parallel, significantly improving retrieval effectiveness."
Microsoft's Azure AI Search has launched a public preview of agentic retrieval, significantly improving answer relevance in conversational AI by up to 40% compared to traditional RAG methods. The system analyzes conversation history and breaks down queries into focused subqueries executed concurrently. This new feature is accessible through a new Knowledge Agents object in the 2025-05-01-preview data plane REST API. By orchestrating a multi-turn retrieval process, Microsoft aims to advance knowledge retrieval systems designed for intelligent agents, providing high-quality grounding data for further applications.
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