@@ -33,10 +33,18 @@ To sign in:
 
 1. On your local system, open a command-line tool.
 
-1. Sign in to Azure. If you have multiple subscriptions, select the one whose ID you obtained in [Get service information](#get-service-information).
+1. Check for the active tenant and subscription in your local environment.
 
    ```azurecli
-   az login
+   az account show
+   ```
+
+1. If the active subscription and tenant aren't valid for your search service, change the variables. You can check for the subscription ID on the search service overview page in the Azure portal. You can check for the tenant ID by clicking through to the subscription. In the Azure portal, the tenant ID is referred to as the **Parent management group**. Make a note of the values that are valid for your search service and run the following commands to update your local environment.
+
+   ```azurecli
+    az account set --subscription <your-subscription-id>
+
+    az login --tenant <your-tenant-id>
    ```
 
 ## Connect to Azure AI Search

@@ -25,35 +25,39 @@ Keyless connections provide enhanced security through granular permissions and i
 
 [!INCLUDE [Setup](./search-get-started-rbac-setup.md)]
 
-## Get token
-
-Before you connect to your Azure AI Search service, use the Azure CLI to sign in to the subscription that contains your service and generate a Microsoft Entra ID token. You use this token to authenticate requests in the next section.
-
-To get your token:
+## Sign in to Azure
 
-1. On your local system, open a command-line tool.
+Before you connect to your Azure AI Search service, use the Azure CLI to sign in to the subscription that contains your service.
 
 1. Check for the active tenant and subscription in your local environment.
 
    ```azurecli
    az account show
    ```
 
-1. If the active subscription and tenant aren't valid for your search service, change the variables. You can check for the subscription ID on the search service overview page in the Azure portal. You can check for the tenant ID by clicking through to the subscription. Make a note of the values that are valid for your search service and run the following commands to update your local environment.
+1. If the active subscription and tenant aren't valid for your search service, change the variables. You can check for the subscription ID on the search service overview page in the Azure portal. You can check for the tenant ID by clicking through to the subscription. In the Azure portal, the tenant ID is referred to as the **Parent management group**. Make a note of the values that are valid for your search service and run the following commands to update your local environment.
 
    ```azurecli
     az account set --subscription <your-subscription-id>
 
     az login --tenant <your-tenant-id>
    ```
 
+## Get token
+
+REST API calls require the inclusion of a Microsoft Entra ID token. You use this token to authenticate requests in the next section.
+
+To get your token:
+
+1. On your local system, open a command-line tool.
+
 1. Generate an access token.
 
    ```azurecli
    az account get-access-token --scope https://search.azure.com/.default --query accessToken --output tsv
    ```
 
-1. Make a note of the token output.
+1. Copy the token output.
 
 ## Connect to Azure AI Search
 

@@ -7,7 +7,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 08/29/2025
+ms.date: 09/30/2025
 ---
 
 # Create a knowledge agent in Azure AI Search
@@ -31,17 +31,17 @@ After you create a knowledge agent, you can update its properties at any time. I
 
 + Familiarity with [agentic retrieval concepts and use cases](search-agentic-retrieval-concept.md).
 
-+ A [supported chat completion model](#supported-models) on Azure OpenAI.
-
 + Azure AI Search, in any [region that provides semantic ranker](search-region-support.md), on the basic pricing tier or higher. Your search service must have a [managed identity](search-how-to-managed-identities.md) for role-based access to the model.
 
-+ Permissions on Azure AI Search. **Search Service Contributor** can create and manage a knowledge agent. **Search Index Data Reader** can run queries. Instructions are provided in this article. [Quickstart: Connect to a search service](/azure/search/search-get-started-rbac?pivots=rest) explains how to configure roles and get a personal access token for REST calls.
++ A [supported chat completion model](#supported-models) on Azure OpenAI.
 
-+ A [knowledge source](search-knowledge-source-overview.md) that identifies searchable content used by the agent. It can be either a [search index knowledge source](search-knowledge-source-how-to-index.md) or a [blob knowledge source](search-knowledge-source-how-to-blob.md)
++ Permission requirements. **Search Service Contributor** can create and manage a knowledge agent. **Search Index Data Reader** can run queries. Instructions are provided in this article. [Quickstart: Connect to a search service](/azure/search/search-get-started-rbac?pivots=rest) explains how to configure roles and get a personal access token for REST calls.
+
++ Content requirements. A [knowledge source](search-knowledge-source-overview.md) that identifies searchable content used by the agent. It can be either a [search index knowledge source](search-knowledge-source-how-to-index.md) or a [blob knowledge source](search-knowledge-source-how-to-blob.md)
 
 + API requirements. To create or use a knowledge agent, use the [2025-08-01-preview](/rest/api/searchservice/operation-groups?view=rest-searchservice-2025-08-01-preview&preserve-view=true) data plane REST API. Or, use a preview package of an Azure SDK that provides knowledge agent APIs: [Azure SDK for Python](https://github.com/Azure/azure-sdk-for-python/blob/main/sdk/search/azure-search-documents/CHANGELOG.md), [Azure SDK for .NET](https://github.com/Azure/azure-sdk-for-net/blob/main/sdk/search/Azure.Search.Documents/CHANGELOG.md#1170-beta3-2025-03-25), [Azure SDK for Java](https://github.com/Azure/azure-sdk-for-java/blob/main/sdk/search/azure-search-documents/CHANGELOG.md). **There's no Azure portal support knowledge agents at this time**.
 
-To follow the steps in this guide, we recommend [Visual Studio Code](https://code.visualstudio.com/download) with a [REST client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client) for sending preview REST API calls to Azure AI Search. T
+To follow the steps in this guide, we recommend [Visual Studio Code](https://code.visualstudio.com/download) with a [REST client](https://marketplace.visualstudio.com/items?itemName=humao.rest-client) for sending preview REST API calls to Azure AI Search or the [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) and [Jupyter package](https://pypi.org/project/jupyter/).
 
 ## Deploy a model for agentic retrieval
 
@@ -72,7 +72,7 @@ Use Azure OpenAI or an equivalent open source model:
 
 Azure AI Search needs access to the chat completion model. You can use key-based or role-based authentication (recommended).
 
-### Use role-based authentication
+### [**Use roles**](#tab/rbac)
 
 If you're using role-based authentication, on your Azure OpenAI resource, assign the **Cognitive Services User** role to a search service managed identity.
 
@@ -96,7 +96,7 @@ In Azure, you must have **Owner** or **User Access Administrator** permissions o
 > [!IMPORTANT]
 > If you use role-based authentication, be sure to remove all references to the API key in your requests. In a request that specifies both approaches, the API key is used instead of roles.
 
-### Use key-based authentication
+### [**Use keys**](#tab/keys)
 
 You can use API keys if you don't have permission to create role assignments.
 
@@ -114,10 +114,94 @@ You can use API keys if you don't have permission to create role assignments.
       @api-key: {{search-api-key}}
    ```
 
+---
+
 ## Check for existing knowledge agents
 
 The following request lists knowledge agents by name. Within the knowledge agents collection, all knowledge agents must be uniquely named. It's helpful to know about existing knowledge agents for reuse or for naming new agents.
 
+### [**Python**](#tab/python-get-agents)
+
+```python
+# List existing knowledge agents on the search service
+from azure.search.documents.indexes import SearchIndexClient
+
+index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
+
+try:
+    agents = {agent.name: agent for agent in index_client.list_agents(api_version=search_api_version)}
+    print(f"\nKnowledge agents on search service '{search_endpoint}':")
+    
+    if agents:
+        print(f"Found {len(agents)} knowledge agent(s):")
+        for i, (name, agent) in enumerate(sorted(agents.items()), 1):
+            print(f"{i}. Name: {name}")
+            if agent.knowledge_sources:
+                ks_names = [ks.name for ks in agent.knowledge_sources]
+                print(f"   Knowledge Sources: {', '.join(ks_names)}")
+            print()
+    else:
+        print("No knowledge agents found.")
+        
+except Exception as e:
+    print(f"Error listing knowledge agents: {str(e)}")
+```
+
+You can also return a single agent by name to review its JSON definition.
+
+```python
+# Get knowledge agent definition for earth-knowledge-agent-2
+from azure.search.documents.indexes import SearchIndexClient
+import json
+
+index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
+
+try:
+    agent_name = "earth-knowledge-agent-2"
+    agent = index_client.get_agent(agent_name, api_version=search_api_version)
+    
+    print(f"Knowledge agent '{agent_name}':")
+    print(f"Name: {agent.name}")
+    
+    if agent.description:
+        print(f"Description: {agent.description}")
+    
+    if agent.models:
+        print(f"\nModels ({len(agent.models)}):")
+        for i, model in enumerate(agent.models, 1):
+            print(f"  {i}. {type(model).__name__}")
+            if hasattr(model, 'azure_open_ai_parameters'):
+                params = model.azure_open_ai_parameters
+                print(f"     Resource: {params.resource_url}")
+                print(f"     Deployment: {params.deployment_name}")
+                print(f"     Model: {params.model_name}")
+    
+    if agent.knowledge_sources:
+        print(f"\nKnowledge Sources ({len(agent.knowledge_sources)}):")
+        for i, ks in enumerate(agent.knowledge_sources, 1):
+            print(f"  {i}. {ks.name} (threshold: {ks.reranker_threshold})")
+    
+    if agent.output_configuration:
+        config = agent.output_configuration
+        print(f"\nOutput: {config.modality} (activity: {config.include_activity})")
+    
+    # Full JSON definition
+    print(f"\nJSON definition:")
+    print(json.dumps(agent.as_dict(), indent=2))
+    
+except Exception as e:
+    print(f"Error: {str(e)}")
+    
+    # Show available agents
+    try:
+        agents = {agent.name: agent for agent in index_client.list_agents(api_version=search_api_version)}
+        print(f"\nAvailable agents: {list(agents.keys())}")
+    except Exception:
+        print("Could not list available agents.")
+```
+
+### [**REST**](#tab/rest-get-agents)
+
 ```http
 # List knowledge agents
 GET {{search-url}}/agents?api-version=2025-08-01-preview
@@ -134,6 +218,8 @@ GET {{search-url}}/agents/{{agent-name}}?api-version=2025-08-01-preview
    Authorization: Bearer {{accessToken}}
 ```
 
+---
+
 ## Create a knowledge agent
 
 A knowledge agent drives the agentic retrieval pipeline. In application code, it's called by other agents or chat bots. 
@@ -142,6 +228,44 @@ Its composition consists of connections between *knowledge sources* (searchable
 
 To create an agent, use the 2025-08-01-preview data plane REST API or an Azure SDK preview package that provides equivalent functionality.
 
+Recall that you must have an existing [knowledge source](search-knowledge-source-overview.md) to give to the agent.
+
+### [**Python**](#tab/python-create-agent)
+
+```python
+from azure.search.documents.indexes.models import KnowledgeAgent, KnowledgeAgentAzureOpenAIModel, KnowledgeSourceReference, AzureOpenAIVectorizerParameters, KnowledgeAgentOutputConfiguration, KnowledgeAgentOutputConfigurationModality
+from azure.search.documents.indexes import SearchIndexClient
+
+aoai_params = AzureOpenAIVectorizerParameters(
+    resource_url=aoai_endpoint,
+    deployment_name=aoai_gpt_deployment,
+    model_name=aoai_gpt_model,
+)
+
+output_cfg = KnowledgeAgentOutputConfiguration(
+    modality=KnowledgeAgentOutputConfigurationModality.ANSWER_SYNTHESIS,
+    include_activity=True,
+)
+
+agent = KnowledgeAgent(
+    name=knowledge_agent_name,
+    models=[KnowledgeAgentAzureOpenAIModel(azure_open_ai_parameters=aoai_params)],
+    knowledge_sources=[
+        KnowledgeSourceReference(
+            name=knowledge_source_name,
+            reranker_threshold=2.5,
+        )
+    ],
+    output_configuration=output_cfg,
+)
+
+index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
+index_client.create_or_update_agent(agent, api_version=search_api_version)
+print(f"Knowledge agent '{knowledge_agent_name}' created or updated successfully.")
+```
+
+### [**REST**](#tab/rest-create-agent)
+
 ```http
 @search-url=<YOUR SEARCH SERVICE URL>
 @agent-name=<YOUR AGENT NAME>
@@ -242,14 +366,68 @@ PUT {{search-url}}/agents/{{agent-name}}?api-version=2025-08-01-preview
 
 + `encryptionKey` is optional. Include an encryption key definition if you're supplementing with [customer-managed keys](search-security-manage-encryption-keys.md).
 
-<!-- --- -->
+---
 
 ## Query the knowledge agent
 
-Call the **retrieve** action on the knowledge agent object to confirm the model connection and return a response. Use the [2025-08-01-preview](/rest/api/searchservice/operation-groups?view=rest-searchservice-2025-08-01-preview&preserve-view=true) data plane REST API or an Azure SDK preview package that provides equivalent functionality for this task.
+Call the **retrieve** action on the knowledge agent object to confirm the model connection and return a response. Use the [2025-08-01-preview](/rest/api/searchservice/operation-groups?view=rest-searchservice-2025-08-01-preview&preserve-view=true) data plane REST API or an Azure SDK preview package that provides equivalent functionality for this task. For more information about the **retrieve** API and the shape of the response, see [Retrieve data using a knowledge agent in Azure AI Search](search-agentic-retrieval-how-to-retrieve.md).
 
 Replace "where does the ocean look green?" with a query string that's valid for your search index.
 
+### [**Python**](#tab/python-query-agent)
+
+Start with instructions.
+
+```python
+instructions = """
+A Q&A agent that can answer questions about the Earth at night.
+If you don't have the answer, respond with "I don't know".
+"""
+
+messages = [
+    {
+        "role": "system",
+        "content": instructions
+    }
+]
+```
+
+Then send the query.
+
+```python
+from azure.search.documents.agent import KnowledgeAgentRetrievalClient
+from azure.search.documents.agent.models import KnowledgeAgentRetrievalRequest, KnowledgeAgentMessage, KnowledgeAgentMessageTextContent, SearchIndexKnowledgeSourceParams
+
+agent_client = KnowledgeAgentRetrievalClient(endpoint=search_endpoint, agent_name=knowledge_agent_name, credential=credential)
+query_1 = """
+    where does the ocean look green??
+    """
+
+messages.append({
+    "role": "user",
+    "content": query_1
+})
+
+req = KnowledgeAgentRetrievalRequest(
+    messages=[
+        KnowledgeAgentMessage(
+            role=m["role"],
+            content=[KnowledgeAgentMessageTextContent(text=m["content"])]
+        ) for m in messages if m["role"] != "system"
+    ],
+    knowledge_source_params=[
+        SearchIndexKnowledgeSourceParams(
+            knowledge_source_name=knowledge_source_name,
+        )
+    ]
+)
+
+result = agent_client.retrieve(retrieval_request=req, api_version=search_api_version)
+print(f"Retrieved content from '{knowledge_source_name}' successfully.")
+```
+
+### [**REST**](#tab/rest-query-agent)
+
 ```http
 # Send grounding request
 POST {{search-url}}/agents/{{agent-name}}/retrieve?api-version=2025-08-01-preview
@@ -301,55 +479,34 @@ The response to the previous query might look like this:
         }
       ]
     }
-  ],
+  ]
 ```
 
+---
 
-<!-- ```http
-# Send grounding request
-POST {{search-url}}/agents/{{agent-name}}/retrieve?api-version=2025-08-01-preview
-   Content-Type: application/json
-   Authorization: Bearer {{accessToken}}
+## Delete an agent
 
-{
-    "messages" : [
-            {
-                "role" : "assistant",
-                "content" : [
-                  { "type" : "text", "text" : "You are a helpful assistant for Contoso Human Resources. You have access to a search index containing guidelines about health care coverage for Washington state. If you can't find the answer in the search, say you don't know." }
-                ]
-            },
-            {
-                "role" : "user",
-                "content" : [
-                  { "type" : "text", "text" : "What are my vision benefits?" }
-                ]
-            }
-        ],
-    "targetIndexParams" :  [
-        { 
-            "indexName" : "{{index-name}}",
-            "filterAddOn" : "State eq 'WA'",
-            "IncludeReferenceSourceData": true,
-            "rerankerThreshold" : 2.5
-            "maxDocsForReranker": 250
-        } 
-    ]
-}
-``` -->
+If you no longer need the agent, or if you need to rebuild it on the search service, use this request to delete the current object.
 
-For more information about the **retrieve** API and the shape of the response, see [Retrieve data using a knowledge agent in Azure AI Search](search-agentic-retrieval-how-to-retrieve.md).
+### [**Python**](#tab/python-delete-agent)
 
-## Delete an agent
+```python
+from azure.search.documents.indexes import SearchIndexClient
 
-If you no longer need the agent, or if you need to rebuild it on the search service, use this request to delete the current object.
+index_client = SearchIndexClient(endpoint=search_endpoint, credential=credential)
+index_client.delete_agent(knowledge_agent_name)
+print(f"Knowledge agent '{knowledge_agent_name}' deleted successfully.")
+```
 
+### [**REST**](#tab/rest-delete-agent)
 ```http
 # Delete agent
 DELETE {{search-url}}/agents/{{agent-name}}?api-version=2025-08-01-preview
    Authorization: Bearer {{accessToken}}
 ```
 
+---
+
 ## Related content
 
 + [Agentic retrieval in Azure AI Search](search-agentic-retrieval-concept.md)

@@ -7,7 +7,7 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 08/29/2025
+ms.date: 10/01/2025
 ---
 
 # Design an index for agentic retrieval in Azure AI Search
@@ -16,13 +16,13 @@ ms.date: 08/29/2025
 
 In Azure AI Search, *agentic retrieval* is a new parallel query architecture that uses a chat completion model for query planning, generating subqueries that broaden the scope of what's searchable and relevant.
 
-Subqueries are created internally. Certain aspects of the subqueries are determined by your search index. This article explains which index elements have an effect on agentic retrieval. None of the required elements are new or specific to agentic retrieval, which means you can use an existing index if it meets the criteria identified in this article, even if it was created using earlier API versions.
+Subqueries are created internally. Certain aspects of the subqueries are determined by your search index. This article explains which index elements have an effect on the query logic. None of the required elements are new or specific to agentic retrieval, which means you can use an existing index if it meets the criteria identified in this article, even if it was created using earlier API versions.
 
-A search index that's used in agentic retrieval is specified as *knowledge source* and is either:
+A search index that's used in agentic retrieval is specified as *knowledge source* on a *knowledge agent*, and is either:
 
 + An existing indexing containing searchable content. This index is made available to agentic retrieval through a [search index knowledge source](search-knowledge-source-how-to-index.md) definition.
 
-+ A generated index created from a generated blob indexer pipeline. This index is generated and populated using information from a [blob knowledge source](search-knowledge-source-how-to-blob.md). It's based on a template that meets all of the criteria for knowledge agents and agentic retrieval. 
++ A generated index created from a blob indexer pipeline. This index is generated and populated using information from a [blob knowledge source](search-knowledge-source-how-to-blob.md). It's based on a template that meets all of the criteria for knowledge agents and agentic retrieval. 
 
 ## Criteria for agentic retrieval
 
@@ -64,7 +64,7 @@ Here's an example index that works for agentic retrieval. It meets the criteria
       "synonymMaps": []
     },
     {
-      "name": "page_chunk_text_3_large", "type": "Collection(Edm.Single)",
+      "name": "page_chunk_vector_text_3_large", "type": "Collection(Edm.Single)",
       "searchable": true, "retrievable": false, "filterable": false, "sortable": false, "facetable": false,
       "dimensions": 3072,
       "vectorSearchProfile": "hnsw_text_3_large",
@@ -91,9 +91,7 @@ Here's an example index that works for agentic retrieval. It meets the criteria
   "tokenizers": [],
   "tokenFilters": [],
   "charFilters": [],
-  "similarity": {
-    "@odata.type": "#Microsoft.Azure.Search.BM25Similarity"
-  },
+  "similarity": {},
   "semantic": {
     "defaultConfiguration": "semantic_config",
     "configurations": [
@@ -152,21 +150,14 @@ Here's an example index that works for agentic retrieval. It meets the criteria
 
 In agentic retrieval, a large language model (LLM) is used twice. First, it's used to create a query plan. After the query plan is executed and search results are generated, those results are passed to the LLM again, this time as grounding data that's used to formulate an answer. 
 
-LLMs consume and emit tokenized strings of human readable plain text content. For this reason, you must have `searchable` fields that provide plain text strings, and are `retrievable` in the response. Vector fields and vector search are also important because they add similarity search to information retrieval. Vectors enhance and improve the quality of search, but aren't otherwise strictly required. Azure AI Search has built-in capabilities that [simplify and automate vectorization](vector-search-overview.md).
+LLMs consume and emit tokenized strings of human readable plain text content. For this reason, you must have `searchable` fields that provide plain text strings, and are `retrievable` in the response. Vector fields and vector search are also important because they add similarity search to information retrieval. Vectors enhance and improve the quality of search that produces grounding data, but aren't otherwise strictly required. Azure AI Search has built-in capabilities that [simplify and automate vectorization](vector-search-overview.md).
 
 The previous example index includes a vector field that's used at query time. You don't need the vector in results because it isn't human or LLM readable, but notice that its `searchable` for vector search. Since you don't need vectors in the response, both `retrievable` and `stored` are false. 
 
-The vectorizer defined in the vector search configuration is critical. It determines whether your vector field is used during query execution. The vectorizer encodes subqueries into vectors at query time for similarity search over the vectors. The vectorizer must be the same embedding model used to create the vectors in the index.
+The vectorizer defined in the vector search configuration is critical. It determines whether your vector field is used during query execution. The vectorizer encodes string subqueries into vectors at query time for similarity search over the vectors. The vectorizer must be the same embedding model used to create the vectors in the index.
 
 All `searchable` fields are included in query execution. There's no support for a `select` statement that explicitly states which fields to query.
 
-<!-- 
-> [!div class="checklist"]
-> + A fields collection with `searchable` text and vetor fields, and `retrievable` text fields
-> + Vector fields that are queried are fields having a vectorizer
-> + Fields selected in the response string are semantic fields (title, terms, content)
-> + Fields in reference source data are all `retrievable` fields, assuming reference source data is true -->
-
 ## Add a description
 
 An index `description` field is a user-defined string that you can use to provide guidance to LLMs and Model Context Protocol (MCP) servers when deciding to use a specific index for a query. This human-readable text is invaluable when a system must access several indexes and make a decision based on the description. 

@@ -69,7 +69,7 @@ POST https://{{search-url}}/agents/{{agent-name}}/retrieve?api-version=2025-08-0
                 "content" : [
                   { "type" : "text", "text" : "You can answer questions about the Earth at night.
                     Sources have a JSON format with a ref_id that must be cited in the answer.
-                    If you do not have the answer, respond with 'I don't know'." }
+                    If you do not have the answer, respond with 'I do not know'." }
                 ]
             },
             {

@@ -7,22 +7,24 @@ author: HeidiSteen
 ms.author: heidist
 ms.service: azure-ai-search
 ms.topic: how-to
-ms.date: 08/29/2025
+ms.date: 10/01/2025
 ---
 
 # Create a knowledge source
 
+[!INCLUDE [Feature preview](./includes/previews/preview-generic.md)]
+
 A knowledge source wraps a search index with extra properties for agentic retrieval. It's a required definition in a knowledge agent. We provide guidance on how to create specific knowledge sources, but generally, you can:
 
 + Create multiple knowledge sources as top-level resources on your search service.
 
-+ Reference one or more knowledge sources in a knowledge agent. In an agentic retrieval pipeline, it's possible to query against multiple knowledge sources in single request. Subqueries are generated for each knowledge sources. Top results are returned in the retrieval response.
++ Reference one or more knowledge sources in a knowledge agent. In an agentic retrieval pipeline, it's possible to query against multiple knowledge sources in single request. Subqueries are generated for each knowledge source. Top results are returned in the retrieval response.
 
-Make sure you have at least one knowledge source before creating a knowledge agent. The full specification of a knowledge agent is in the [REST API reference](/rest/api/searchservice/knowledge-sources/create-or-update?view=rest-searchservice-2025-08-01-preview&preserve-view=true). 
+Make sure you have at least one knowledge source before creating a knowledge agent. The full specification of a knowledge source and a knowledge agent is in the [REST API reference](/rest/api/searchservice). 
 
 ## Key points about a knowledge source
 
-+ Creation path: first create knowledge source, then create knowledge agents. Deletion path: update or delete knowledge agents, delete knowledge sources last.
++ Creation path: first create a knowledge source, then create a knowledge agent. Deletion path: update or delete knowledge agents, delete knowledge sources last.
 
 + A knowledge source, its index, and the knowledge agent must all exist on the same search service.
 
@@ -53,15 +55,15 @@ When you have multiple knowledge sources, set the following properties to bias q
 + Setting `alwaysQuerySource` forces query planning to always include the knowledge source.
 + Setting `retrievalInstructions` provides guidance that includes or excludes a knowledge source. 
 
-Retrieval instructions are sent as a prompt to the large language model (LLM) used for query planning. This prompt is helpful when you have multiple knowledge sources and want to provide guidance on when to use each one. For example, if you have separate indexes for product information, job postings, and technical support, the retrieval instructions might say "use the jobs index only if the question is about a job application."
+Retrieval instructions are sent as a user-defined prompt to the large language model (LLM) used for query planning. This prompt is helpful when you have multiple knowledge sources and want to provide guidance on when to use each one. For example, if you have separate indexes for product information, job postings, and technical support, the retrieval instructions might say "use the jobs index only if the question is about a job application."
 
 The `alwaysQuerySource` property overrides `retrievalInstructions`. You should set `alwaysQuerySource` to false when providing retrieval instructions.
 
 ### Attempt fast path processing
 
 Fast path is opportunistic query processing that approaches the millisecond query performance of regular search. If you enable it, the search engine attempts fast path under the following conditions:
 
-+ `attemptFastPath` is set to true in `knowledgeSourceReferences`.
++ `attemptFastPath` is set to true in `outputConfiguration`.
 
 + The query input is a single message that's fewer than 512 characters.
 
@@ -75,13 +77,23 @@ Under fast path, `retrievalInstructions` are ignored. In general, `alwaysQuerySo
 
 To achieve the fastest possible response times, follow these best practices:
 
-+ Set `modality` to `answerSynthesis` to get a response framed as an LLM-formulated answer. It takes a few extra seconds, but it improves the quality of the response and saves time overall if the answer is usable without further LLM processing.
+1. In the knowledge agent:
+
+   + Set `outputConfiguration.attemptFastPath` to true.
+
+   + Set `outputConfiguration.modality` to `answerSynthesis` to get a response framed as an LLM-formulated answer. It takes a few extra seconds, but it improves the quality of the response and saves time overall if the answer is usable without further LLM processing.
+
+   + Retain `outputConfiguration.includeActivity` set to true (default setting) for insights about query execution and elapsed time.
+
+   + Retain `knowledgeSource.includeReferences` set to true (default setting) for details about each individually scored result.
+
+   + Set `knowledgeSources.alwaysQuerySource` to true.
 
-+ Retain `includeActivity` set to true (default setting) for insights about query execution and elapsed time.
+   + Set `knowledgeSources.retrievalInstructions` to false.
 
-+ Retain `includeReferences` set to true (default setting) for details about each individually scored result.
+   + Set `knowledgeSources.includeReferenceSourceData` to false if you don't need the verbatim content from the index. Omitting this information simplifies the response and makes it more readable.
 
-+ Set `includeReferenceSourceData` to false if you don't need the verbatim content from the index. Omitting this information simplifies the response and makes it more readable.
+1. In the [retrieve action](search-agentic-retrieval-how-to-retrieve.md), provide a single message query that's fewer than 512 characters.
 
 ## Delete a knowledge source