fixing links

Author: HeidiSteen

articles/search/search-agentic-retrieval-concept.md

@@ -158,7 +158,7 @@ Choose any of these options for your next step.
   + [Use an agent to retrieve data](search-agentic-retrieval-how-to-retrieve.md)
   + [Build an agent-to-agent retrieval solution](search-agentic-retrieval-how-to-pipeline.md).
 
-+ REST API reference, [Agents](/rest/api/searchservice/knowledge-agents/create-or-update?view=rest-searchservice-2025-05-01-preview&preserve-view=true) and [retrieve](/rest/api/searchservice/knowledge-retrieval/retrieve?view=rest-searchservice-2025-05-01-preview&preserve-view=true).
++ REST API reference, [Agents](/rest/api/searchservice/knowledge-agents?view=rest-searchservice-2025-05-01-preview&preserve-view=true) and [retrieve](/rest/api/searchservice/knowledge-retrieval/retrieve?view=rest-searchservice-2025-05-01-preview&preserve-view=true).
 
 + [Azure OpenAI Demo](https://github.com/Azure-Samples/azure-search-openai-demo), updated to use agentic retrieval.
 

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

@@ -17,7 +17,7 @@ ms.date: 05/10/2025
 
 This article describes an approach or pattern for building a solution that uses Azure AI Search for data retrieval and how to integrate the retrieval into a custom solution that includes Azure AI Agent.
 
-This article supports the [agent-example](https://github.com/Azure-Samples/agentic-retrieval-pipeline-example) Python sample on GitHub.
+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.
 
 This exercise differs from the [Agentic Retrieval Quickstart](search-get-started-agentic-retrieval.md) in how it uses Azure AI Agent to determine whether to retrieve data from the index, and how it uses an agent tool for orchestration.
 

articles/search/search-document-level-access-overview.md

@@ -4,7 +4,7 @@ titleSuffix: Azure AI Search
 description: Conceptual overview of document-level permissions in Azure AI Search.    
 ms.service: azure-ai-search    
 ms.topic: conceptual    
-ms.date: 05/19/2025    
+ms.date: 05/10/2025    
 author: gmndrg    
 ms.author: gimondra    
 ---  
@@ -49,7 +49,7 @@ By offloading permission enforcement to Azure AI Search, developers can focus on
 
 Document-level permissions in Azure AI Search provide a structured framework for enforcing access controls that align with organizational policies. By using Microsoft Entra-based ACLs and RBAC roles, organizations can create systems that support robust compliance and promote trust among users. These built-in capabilities reduce the need for custom coding, offering a standardized approach to document-level security.  
 
-## Related content
+## Reference documents
 
 To help you dive deeper into document-level access control in Azure AI Search, here are more articles and samples:  
 
@@ -59,3 +59,7 @@ To help you dive deeper into document-level access control in Azure AI Search, h
 | **Index ADLS Gen2 permissions metadata using built-in indexers** | [How to index permissions using ADLS Gen2 indexer](search-indexer-access-control-lists-and-role-based-access.md) and [Tutorial: Index ADLS Gen2 permissions metadata using an indexer](tutorial-adls-gen2-indexer-acls.md) |  [azure-search-python-samples/Quickstart-Document-Permissions-Pull-API](https://github.com/Azure-Samples/azure-search-python-samples/blob/main/Quickstart-Document-Permissions-Pull-API) |
 | **Query using Microsoft Entra token-based permissions** | [How to query using Microsoft Entra token-based permissions](https://aka.ms/azs-query-preserving-permissions) | See previous samples. |
 | **Security trimming via filters**              | [Security trimming via filters](search-security-trimming-for-azure-search.md)               |  Not available. |
+
+## Next steps  
+
+- [Tutorial: Index ADLS Gen2 permissions metadata](tutorial-adls-gen2-indexer-acls.md)