Merge pull request #5430 from MicrosoftDocs/main

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articles/search/media/agentic-retrieval/agent-to-agent-pipeline.png

@@ -5,7 +5,7 @@ description: Learn about agentic retrieval concepts, architecture, and use cases
 author: HeidiSteen
 ms.author: heidist
 manager: nitinme
-ms.date: 05/19/2025
+ms.date: 06/08/2025
 ms.service: azure-ai-search
 ms.topic: concept-article
 ms.custom:
@@ -17,20 +17,22 @@ ms.custom:
 
 [!INCLUDE [Feature preview](./includes/previews/preview-generic.md)]
 
-In Azure AI Search, *agentic retrieval* is a new parallel query processing architecture that incorporates user conversation history and Azure OpenAI models to plan, retrieve and synthesize queries for improved results. It produces high-quality grounding data for custom chat and generative AI solutions that include knowledge agents.
+In Azure AI Search, *agentic retrieval* is a new query pipeline designed for complex questions posed by users or agents in chat and copilot apps. It uses a large language model (LLM) to break down a question into smaller subqueries, often using chat history for context. These subqueries run in parallel, each searching for the most relevant content in your index. The results are ranked for semantic relevance, combined, and sent back to your LLM to help generate accurate answers using your proprietary content.
 
 Programmatically, agentic retrieval is supported through a new Knowledge Agents object in the 2025-05-01-preview data plane REST API and in Azure SDK prerelease packages that provide the feature. A knowledge agent's retrieval response is designed for downstream consumption by other agents and chat apps.
 
 ## Why use agentic retrieval
 
-You should use agentic retrieval when you want to send high quality data to an agent or to ground a chat experience with inputs that include your proprietary content.
+You should use agentic retrieval when you want to provide agents and apps with the most relevant content for answering harder questions, leveraging chat context and your proprietary content.
 
 The *agentic* aspect is a reasoning step in query planning processing that's performed by a supported large language model (LLM) that you provide. The LLM analyzes the entire chat thread to identify the underlying information need. Instead of a single, catch-all query, the model breaks down compound questions into focused subqueries based on: user questions, chat history, and parameters on the request. The subqueries target your indexed documents (plain text and vectors) in Azure AI Search.This hybrid approach ensures you surface both keyword matches and semantic similarities at once, dramatically improving recall. 
 
 The *retrieval* component is the ability to run subqueries simultaneously, merge results, semantically rank results, and return a three-part response that includes grounding data for the next conversation turn, reference data so that you can inspect the source content, and an activity plan that shows query execution steps.
 
 Query expansion and parallel execution, plus the retrieval response, are the key capabilities of agentic retrieval that make it the best choice for generative AI (RAG) applications.
 
+:::image type="content" source="media/agentic-retrieval/agentric-retrieval-example.png" alt-text="Diagram of a complex query with implied context and an intentional typo." lightbox="media/agentic-retrieval/agentric-retrieval-example.png" :::
+
 Agentic retrieval adds latency to query processing, but it makes up for it by adding these capabilities:
 
 + Reads in chat history as an input to the retrieval pipeline.

articles/search/media/agentic-retrieval/agentic-retrieval-architecture.png

@@ -5,7 +5,7 @@ description: Learn how to design and build a custom agentic retrieval solution w
 author: HeidiSteen
 ms.author: heidist
 manager: nitinme
-ms.date: 05/21/2025
+ms.date: 06/08/2025
 ms.service: azure-ai-search
 ms.topic: how-to
 ms.custom:
@@ -16,7 +16,9 @@ ms.custom:
 
 [!INCLUDE [Feature preview](./includes/previews/preview-generic.md)]
 
-This article describes an approach or pattern for building a solution that uses Azure AI Search for knowledge retrieval, and how to integrate knowledge retrieval into a custom solution that includes Azure AI Agent.
+This article describes an approach or pattern for building a solution that uses Azure AI Search for knowledge retrieval, and how to integrate knowledge retrieval into a custom solution that includes Azure AI Agent. This pattern uses an agent tool to invoke an agentic retrieval pipeline in Azure AI Search.
+
+:::image type="content" source="media/agentic-retrieval/agent-to-agent-pipeline.png" alt-text="Diagram of Azure AI Search integration with Azure AI Agent service." lightbox="media/agentic-retrieval/agent-to-agent-pipeline.png" :::
 
 This article supports the [agentic-retrieval-pipeline-example](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/agentic-retrieval-pipeline-example) Python sample on GitHub.
 

articles/search/media/agentic-retrieval/agentric-retrieval-example.png

@@ -8,14 +8,14 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 05/30/2025
+ms.date: 06/08/2025
 ---
 
 # Retrieve data using a knowledge agent in Azure AI Search
 
 [!INCLUDE [Feature preview](./includes/previews/preview-generic.md)]
 
-In Azure AI Search, *agentic retrieval* is a new parallel query architecture that uses a chat completion model for query planning. It generates subqueries that broaden the scope of what's searchable and relevant.
+In Azure AI Search, *agentic retrieval* is a new parallel query architecture that uses a large language model (LLM) for query planning. It generates subqueries that broaden the scope of what's searchable and relevant. It incorporates chat history for context. The LLM studies the query and subdivides it into more targeted queries, using different phrases and terminology for subquery composition.
 
 This article explains how to use the [**retrieve method**](/rest/api/searchservice/knowledge-retrieval/retrieve?view=rest-searchservice-2025-05-01-preview&preserve-view=true) that invokes a knowledge agent and parallel query processing. This article also explains the three components of the retrieval response: 
 
@@ -26,7 +26,7 @@ This article explains how to use the [**retrieve method**](/rest/api/searchservi
 The retrieve request can include instructions for query processing that override the defaults set on the knowledge agent.
 
 > [!NOTE]
-> There's no model-generated "answer" in the response. Instead, the response passes content to an LLM that grounds its answer based on the content. For an end-to-end example that includes this step, see [Build an agent-to-agent retrieval solution ](search-agentic-retrieval-how-to-pipeline.md) or [Azure OpenAI Demo](https://github.com/Azure-Samples/azure-search-openai-demo).
+> There's no model-generated "answer" in the response. Instead, you should pass the response to an LLM that grounds its answer based on the content. For an end-to-end example that includes this step, see [Build an agent-to-agent retrieval solution ](search-agentic-retrieval-how-to-pipeline.md) or [Azure OpenAI Demo](https://github.com/Azure-Samples/azure-search-openai-demo).
 
 ## Prerequisites