reframed AR pipeline as a tutorial

Author: HeidiSteen

articles/search/agentic-retrieval-how-to-create-pipeline.md

@@ -1,26 +1,26 @@
 ---
-title: Build an agentic retrieval solution
+title: 'Tutorial: Build an agentic retrieval solution'
 titleSuffix: Azure AI Search
 description: Learn how to design and build a custom agentic retrieval solution where Azure AI Search handles data retrieval for your custom agents in AI Foundry.
 author: HeidiSteen
 ms.author: heidist
 manager: nitinme
 ms.date: 09/10/2025
 ms.service: azure-ai-search
-ms.topic: how-to
+ms.topic: tutorial
 ms.custom:
   - build-2025
 ---
 
-# Build an agent-to-agent retrieval solution using Azure AI Search
+# Tutorial: Build an agent-to-agent retrieval solution using Azure AI Search
 
 [!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 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.svg" 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.
+To run the code for this tutorial, download the [agentic-retrieval-pipeline-example](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/agentic-retrieval-pipeline-example) Python sample on GitHub.
 
 This exercise differs from the [Agentic Retrieval Quickstart](search-get-started-agentic-retrieval.md) in how it uses Azure AI Agent to retrieve data from the index, and how it uses an agent tool for orchestration. If you want to understand the retrieval pipeline in its simplest form, begin with the quickstart.
 
@@ -315,6 +315,18 @@ Look at output tokens in the [activity array](agentic-retrieval-how-to-retrieve.
 
 + Set `maxOutputSize` in the [knowledge agent](agentic-retrieval-how-to-create-knowledge-base.md) to govern the size of the response, or `maxRuntimeInSeconds` for time-bound processing.
 
+## Clean up resources
+
+When you're working in your own subscription, at the end of a project, it's a good idea to remove the resources that you no longer need. Resources left running can cost you money. You can delete resources individually or delete the resource group to delete the entire set of resources.
+
+You can also delete individual objects:
+
++ [Delete a knowledge agent](agentic-retrieval-how-to-create-knowledge-base.md#delete-an-agent)
+
++ [Delete a knowledge source](agentic-knowledge-source-how-to-search-index.md#delete-a-knowledge-source)
+
++ [Delete an index](search-how-to-manage-index#delete-an-index)
+
 ## Related content
 
 + [Agentic retrieval in Azure AI Search](agentic-retrieval-overview.md)

articles/search/agentic-retrieval-how-to-retrieve.md

@@ -27,7 +27,7 @@ This article also explains the three components of the retrieval response:
 The retrieve request can include instructions for query processing that override the defaults set on the knowledge agent.
 
 > [!NOTE]
-> By default, there's no model-generated "answer" in the response and you should pass the extracted response to an LLM so that it can ground its answer based on the search results. For an end-to-end example that includes this step, see [Build an agent-to-agent retrieval solution ](agentic-retrieval-how-to-create-pipeline.md) or [Azure OpenAI Demo](https://github.com/Azure-Samples/azure-search-openai-demo).
+> By default, there's no model-generated "answer" in the response and you should pass the extracted response to an LLM so that it can ground its answer based on the search results. For an end-to-end example that includes this step, see [Tutorial: Build an agent-to-agent retrieval solution ](agentic-retrieval-how-to-create-pipeline.md) or [Azure OpenAI Demo](https://github.com/Azure-Samples/azure-search-openai-demo).
 >
 >Alternatively, you can use [answer synthesis](agentic-retrieval-how-to-answer-synthesis.md) to bring answer formulation into the agentic pipeline. In this workflow, the retriever response consists of LLM-formulated answers instead of the raw search results.
 

articles/search/agentic-retrieval-overview.md

@@ -91,7 +91,7 @@ Your search index determines query execution and any optimizations that occur du
 
 ### Integration requirements
 
-Your application drives the pipeline by calling the knowledge agent and handling the response. The pipeline returns grounding data that you pass to an LLM for answer generation in your conversation interface. For implementation details, see [Build an agent-to-agent retrieval solution](agentic-retrieval-how-to-create-pipeline.md).
+Your application drives the pipeline by calling the knowledge agent and handling the response. The pipeline returns grounding data that you pass to an LLM for answer generation in your conversation interface. For implementation details, see [Tutorial: Build an agent-to-agent retrieval solution](agentic-retrieval-how-to-create-pipeline.md).
 
 > [!NOTE]
 > Only gpt-4o, gpt-4.1, and gpt-5 series models are supported for query planning. You can use any model for final answer generation.
@@ -117,7 +117,7 @@ Choose any of these options for your next step.
   + [Create a knowledge agent](agentic-retrieval-how-to-create-knowledge-base.md)
   + [Use answer synthesis for citation-backed responses](agentic-retrieval-how-to-answer-synthesis.md)
   + [Use a knowledge agent to retrieve data](agentic-retrieval-how-to-retrieve.md)
-  + [Build an agent-to-agent retrieval solution](agentic-retrieval-how-to-create-pipeline.md)
+  + [Tutorial: Build an agent-to-agent retrieval solution](agentic-retrieval-how-to-create-pipeline.md)
 
 + REST API reference:
 

articles/search/samples-python.md

@@ -42,7 +42,7 @@ Code samples from the Azure AI Search team demonstrate features and workflows. T
 | [Quickstart-Semantic-Search](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Semantic-Search) | [Quickstart: Semantic ranking](search-get-started-semantic.md) | Add semantic ranking to an index schema and run semantic queries. |
 | [Quickstart-Vector-Search](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Vector-Search) | [Quickstart: Vector search](search-get-started-vector.md) | Index and query vector content. |
 | [Tutorial-RAG](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Tutorial-RAG) | [Build a RAG solution using Azure AI Search](tutorial-rag-build-solution.md) | Create an indexing pipeline that loads, chunks, embeds, and ingests searchable content for RAG. |
-| [agentic-retrieval-pipeline-example](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/agentic-retrieval-pipeline-example) | [Build an agent-to-agent retrieval solution using Azure AI Search](agentic-retrieval-how-to-create-pipeline.md) | Unlike [Quickstart-Agentic-Retrieval](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Agentic-Retrieval), this sample incorporates Azure AI Agent for request orchestration. |
+| [agentic-retrieval-pipeline-example](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/agentic-retrieval-pipeline-example) | [Tutorial: Build an agent-to-agent retrieval solution using Azure AI Search](agentic-retrieval-how-to-create-pipeline.md) | Unlike [Quickstart-Agentic-Retrieval](https://github.com/Azure-Samples/azure-search-python-samples/tree/main/Quickstart-Agentic-Retrieval), this sample incorporates Azure AI Agent for request orchestration. |
 
 ## Accelerators
 

articles/search/search-get-started-agentic-retrieval.md

@@ -42,4 +42,4 @@ zone_pivot_groups: search-get-started-agentic-retrieval
 + [Agentic retrieval in Azure AI Search](agentic-retrieval-overview.md)
 + [Create a knowledge agent](agentic-retrieval-how-to-create-knowledge-base.md)
 + [Use a knowledge agent to retrieve data](agentic-retrieval-how-to-retrieve.md)
-+ [Build an agent-to-agent retrieval solution](agentic-retrieval-how-to-create-pipeline.md)
++ [Tutorial: Build an agent-to-agent retrieval solution](agentic-retrieval-how-to-create-pipeline.md)

articles/search/toc.yml

@@ -152,6 +152,8 @@ items:
       href: tutorial-document-extraction-image-verbalization.md
     - name: Verbalize images from a structured document layout
       href: tutorial-document-layout-image-verbalization.md
+  - name: Agentic retrieval tutorial
+    href: agentic-retrieval-how-to-create-pipeline.md
   - name: Classic RAG tutorials
     items:
     - name: Build a classic RAG solution
@@ -410,8 +412,6 @@ items:
         href: agentic-retrieval-how-to-answer-synthesis.md
       - name: Retrieve data using an agent
         href: agentic-retrieval-how-to-retrieve.md
-      - name: Build a retrieval pipeline
-        href: agentic-retrieval-how-to-create-pipeline.md
       - name: Migrate agentic retrieval code
         href: agentic-retrieval-how-to-migrate.md
     - name: Vector search