topic (docs): [general] rebrand Azure AI Agent service to Azure AI Foundry Agent Service (#50)

* rebrand Azure AI Agent service to Azure AI Foundry Agent Service

* fix: include bicep files

* Address PR Feedback: shorten texts and caps fixes

Co-authored-by: Chad Kittel <[email protected]>

---------

Co-authored-by: Chad Kittel <[email protected]>

Author: ferantivero

CONTRIBUTING.md

@@ -1,4 +1,4 @@
-# Contributing to the Azure OpenAI and AI Agent service chat basic reference implementatio
+# Contributing to the Azure AI Foundry Agent service chat basic reference implementatio
 
 This project welcomes contributions and suggestions.  Most contributions require you to agree to a
 Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us

README.md

@@ -1,30 +1,30 @@
-#  Azure OpenAI and AI Agent service chat basic reference implementation
+# Azure AI Foundry Agent service chat basic reference implementation
 
-This reference implementation illustrates an approach running a chat application and an AI orchestration layer in a single region. It uses Azure AI Agent service as the orchestrator and Azure OpenAI foundation models. This repository directly supports the [Basic end-to-end chat reference architecture](https://learn.microsoft.com/azure/architecture/ai-ml/architecture/basic-openai-e2e-chat) on Microsoft Learn.
+This reference implementation illustrates an approach running a chat application and an AI orchestration layer in a single region. It uses Azure AI Foundry Agent service as the orchestrator and Azure OpenAI foundation models. This repository directly supports the [Basic end-to-end chat reference architecture](https://learn.microsoft.com/azure/architecture/ai-ml/architecture/basic-openai-e2e-chat) on Microsoft Learn.
 
-The reference implementation illustrates a basic example of a chat application. For a reference implementation that implements more enterprise requirements, please see the [OpenAI end-to-end baseline reference implementation](https://github.com/Azure-Samples/openai-end-to-end-baseline). That implementation addresses many of the [production readiness changes](https://github.com/search?q=repo%3AAzure-Samples%2Fopenai-end-to-end-basic+%22Production+readiness+change%22&type=code) identified in this code.
+The reference implementation illustrates a basic example of a chat application. For a reference implementation that implements more enterprise requirements, please see the [Azure AI Foundry Agent service end-to-end baseline reference implementation](https://github.com/Azure-Samples/openai-end-to-end-baseline). That implementation addresses many of the [production readiness changes](https://github.com/search?q=repo%3AAzure-Samples%2Fopenai-end-to-end-basic+%22Production+readiness+change%22&type=code) identified in this code.
 
 ## Architecture
 
 The implementation covers the following scenarios:
 
 - [Setting up Azure AI Foundry to host agents](#setting-up-azure-ai-foundry-to-host-agents)
-- [Deploying an agent into Azure AI Agent service](#deploying-an-agent-into-azure-ai-agent-service)
+- [Deploying an agent into Azure AI Foundry Agent service](#deploying-an-agent-into-azure-ai-agent-service)
 - [Invoking the agent from .NET code hosted in an Azure Web App](#invoking-the-agent-from-net-code-hosted-in-an-azure-web-app)
 
 ### Setting up Azure AI Foundry to host agents
 
-Azure AI Foundry hosts Azure AI Agent service as a capability. Azure AI Agent service's REST APIs are exposed as a AI Foundry internet facing endpoing. This architecture deploys the Azure AI Agent service your own Azure subscription.
+Azure AI Foundry hosts Azure AI Foundry Agent service as a capability. Foundry Agent Service's REST APIs are exposed as an internet facing endpoint.
 
-### Deploying an agent into Azure AI Agent service
+### Deploying an agent into Azure AI Foundry Agent service
 
-Agents can be created via the Azure AI Foundry portal, [Azure AI Agents SDK](https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Agents.Persistent), or the [REST API](https://learn.microsoft.com/rest/api/aifoundry/aiagents/). The creation and invocation of agents are a data plane operation.
+Agents can be created via the Azure AI Foundry portal, [Azure AI Persistent Agents client library](https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Agents.Persistent), or the [REST API](https://learn.microsoft.com/rest/api/aifoundry/aiagents/). The creation and invocation of agents are a data plane operation.
 
 Ideally agents should be source-controlled and a versioned asset. You then can deploy agents in a coordinated way with the rest of your workload's code. In this deployment guide, you'll create an agent through the REST API to simulate a deployment pipeline which could have created the agent.
 
 ### Invoking the agent from .NET code hosted in an Azure Web App
 
-A chat UI application is deployed into Azure App Service. The .NET code uses the [Azure AI Agents SDK](https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Agents.Persistent) to connect to the workload's agent. The endpoint for the agent is exposed over internet through the Azure AI Foundry.
+A chat UI application is deployed into Azure App Service. The .NET code uses the [Azure AI Persistent Agents client library](https://github.com/Azure/azure-sdk-for-net/tree/main/sdk/ai/Azure.AI.Agents.Persistent) to connect to the workload's agent. The endpoint for the agent is exposed over internet through the Azure AI Foundry.
 
 ## Deployment guide
 
@@ -105,7 +105,7 @@ The following steps are required to deploy the infrastructure from the command l
      -p yourPrincipalId=$PRINCIPAL_ID
    ```
 
-### 2. Deploy an agent in the Azure AI Agent service
+### 2. Deploy an agent in the Azure AI Foundry Agent service
 
 To test this scenario, you'll be deploying an AI agent. The agent uses a GPT model combined with a Bing search for grounding data. Deploying an AI agent requires data plane access to Azure AI Foundry. In this architecture, you will interact with the Azure AI Foundry portal and its resources over internet.
 
@@ -123,7 +123,7 @@ To test this scenario, you'll be deploying an AI agent. The agent uses a GPT mod
 
 1. Click **+ Add** button in the Knowledge section.
 
-1. From the popup window select the knowledge type 'Grounding with Bing Search'. 
+1. From the popup window select the knowledge type 'Grounding with Bing Search'.
 
 1. Then choose the existing connection named 'bingaiagent' and click the **Connect** button.
 
@@ -177,7 +177,7 @@ Workloads build chat functionality into an application. Those interfaces usually
 
 > Sometimes the prior command will fail with a `GatewayTimeout`. If you receive that error, you're safe to simply execute the command again.
 
-### 5. Try it out! Test the deployed application that calls into the Azure AI Agent service
+### 5. Try it out! Test the deployed application that calls into the Azure AI Foundry Agent service
 
 After the deployment is complete, you can try the deployed application by navigating to the Web App's URL in a web browser.
 
@@ -209,4 +209,4 @@ Please see our [Contributor guide](./CONTRIBUTING.md).
 
 This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/). For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or contact <[email protected]> with any additional questions or comments.
 
-With :heart: from Azure Patterns & Practices, [Azure Architecture Center](https://azure.com/architecture).
+With :heart: from Azure Patterns & Practices, [Azure Architecture Center](https://azure.com/architecture).

infra-as-code/bicep/ai-foundry-project.bicep

@@ -8,7 +8,7 @@ param location string = resourceGroup().location
 @minLength(2)
 param existingAiFoundryName string
 
-@description('The existing Bing grounding data account that is available to Azure AI Agent agents in this project.')
+@description('The existing Bing grounding data account that is available to Azure AI Foundry Agent agents in this project.')
 @minLength(1)
 param existingBingAccountName string
 
@@ -40,7 +40,7 @@ resource aiFoundry 'Microsoft.CognitiveServices/accounts@2025-04-01-preview' exi
       type: 'SystemAssigned'
     }
     properties: {
-      description: 'Chat using internet data in your Azure AI Agent.'
+      description: 'Chat using internet data in your Azure AI Foundry Agent.'
       displayName: 'Chat with Internet Data'
     }
 
@@ -62,7 +62,7 @@ resource aiFoundry 'Microsoft.CognitiveServices/accounts@2025-04-01-preview' exi
         }
       }
     }
-    
+
     @description('Create project connection to Bing grounding data. Useful for future agents that get created.')
     resource bingGroundingConnection 'connections' = {
       name: replace(existingBingAccountName, '-', '')

infra-as-code/bicep/ai-foundry.bicep

@@ -35,7 +35,7 @@ resource logAnalyticsWorkspace 'Microsoft.OperationalInsights/workspaces@2025-02
 
 // ---- New resources ----
 
-@description('Deploy Azure AI Foundry (account) with Azure AI Agent service capability.')
+@description('Deploy Azure AI Foundry (account) with Azure AI Foundry Agent service capability.')
 resource aiFoundry 'Microsoft.CognitiveServices/accounts@2025-04-01-preview' = {
   name: aiFoundryName
   location: location
@@ -59,7 +59,7 @@ resource aiFoundry 'Microsoft.CognitiveServices/accounts@2025-04-01-preview' = {
     publicNetworkAccess: 'Enabled'
   }
 
-  @description('Models are managed at the account level. Deploy the GPT model that will be used for the Azure AI Agent logic.')
+  @description('Models are managed at the account level. Deploy the GPT model that will be used for the Azure AI Foundry Agent logic.')
   resource model 'deployments' = {
     name: 'agent-model'
     sku: {

infra-as-code/bicep/main.bicep

@@ -40,9 +40,9 @@ resource logAnalyticsWorkspace 'Microsoft.OperationalInsights/workspaces@2025-02
   }
 }
 
-// Deploy the Azure AI Foundry account and Azure AI Agent service components.
+// Deploy the Azure AI Foundry account and Azure AI Foundry Agent service components.
 
-@description('Deploy Azure AI Foundry with Azure AI Agent capability. No projects yet deployed.')
+@description('Deploy Azure AI Foundry with Azure AI Foundry Agent capability. No projects yet deployed.')
 module deployAzureAIFoundry 'ai-foundry.bicep' = {
   scope: resourceGroup()
   name: 'aiFoundryDeploy'
@@ -54,13 +54,13 @@ module deployAzureAIFoundry 'ai-foundry.bicep' = {
   }
 }
 
-@description('Deploy the Bing account for Internet grounding data to be used by agents in the Azure AI Agent service.')
+@description('Deploy the Bing account for Internet grounding data to be used by agents in the Azure AI Foundry Agent Service.')
 module deployBingAccount 'bing-grounding.bicep' = {
   scope: resourceGroup()
   name: 'bingAccountDeploy'
 }
 
-@description('Deploy the Azure AI Foundry project into the AI Foundry account. This is the project is the home of the Azure AI Agent service.')
+@description('Deploy the Azure AI Foundry project into the AI Foundry account. This is the project is the home of the Foundry Agent Service.')
 module deployAzureAiFoundryProject 'ai-foundry-project.bicep' = {
   scope: resourceGroup()
   name: 'aiFoundryProjectDeploy'
@@ -85,7 +85,7 @@ module deployApplicationInsights 'application-insights.bicep' = {
   }
 }
 
-@description('Deploy the web app for the front end demo UI. The web application will call into the Azure AI Agent service.')
+@description('Deploy the web app for the front end demo UI. The web application will call into the Azure AI Foundry Agent Service.')
 module deployWebApp 'web-app.bicep' = {
   scope: resourceGroup()
   name: 'webAppDeploy'

infra-as-code/bicep/web-app.bicep

@@ -21,7 +21,7 @@ param logAnalyticsWorkspaceName string
 @minLength(1)
 param existingWebApplicationInsightsResourceName string
 
-@description('The name of the existing Azure AI Foundry instance that the the Azure Web App code will be calling for Azure AI Agent Service agents.')
+@description('The name of the existing Azure AI Foundry instance that the Azure Web App code will be calling for Foundry Agent Service agents.')
 @minLength(2)
 param existingAzureAiFoundryResourceName string
 
@@ -49,7 +49,7 @@ resource azureAiUserRole 'Microsoft.Authorization/roleDefinitions@2022-04-01' ex
   scope: subscription()
 }
 
-@description('Existing Azure AI Foundry account. This account is where the agents hosted in Azure AI Agent Service will be deployed. The web app code calls to these agents.')
+@description('Existing Azure AI Foundry account. This account is where the agents hosted in Foundry Agent Service will be deployed. The web app code calls to these agents.')
 resource aiFoundry 'Microsoft.CognitiveServices/accounts@2025-04-01-preview' existing = {
   name: existingAzureAiFoundryResourceName