using new sdk and service.

.vscode/settings.json

@@ -0,0 +1,7 @@
+{
+    "python.testing.pytestArgs": [
+        "tests"
+    ],
+    "python.testing.unittestEnabled": false,
+    "python.testing.pytestEnabled": true
+}

README.md

@@ -1,28 +1,30 @@
 # Getting Started with Agents Using Azure AI Foundry
 
-The agent leverages the Azure AI Agent service and utilizes file search for knowledge retrieval from uploaded files, enabling it to generate responses with citations. The solution also includes built-in monitoring capabilities with tracing to ensure easier troubleshooting and optimized performance.
+The agent leverages Foundry Agent Service and utilizes file search for knowledge retrieval from uploaded files, enabling it to generate responses with citations. The solution also includes built-in monitoring capabilities with tracing to ensure easier troubleshooting and optimized performance.
 
 <div style="text-align:center;">
 
 [**SOLUTION OVERVIEW**](#solution-overview) \| [**GETTING STARTED**](#getting-started) \| [**LOCAL DEVELOPMENT**](#local-development) \| [**RESOURCE CLEAN-UP**](#resource-clean-up) \| [**GUIDANCE**](#guidance) \| [**TROUBLESHOOTING**](./docs/troubleshooting.md)
 
 </div>
 
+**Note**: With any AI solutions you create using these templates, you are responsible for assessing all associated risks, and for complying with all applicable laws and safety standards. Learn more in the transparency documents for [Agent Service](https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Flearn.microsoft.com%2Fen-us%2Fazure%2Fai-foundry%2Fresponsible-ai%2Fagents%2Ftransparency-note&data=05%7C02%7Chowieleung%40microsoft.com%7C42645ec29da244bd920508de2095bcad%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C638984024651276233%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=Un4HtoksTeodWPQMQp7zh8BNW6j%2BeIw4mcs6gbS4e6E%3D&reserved=0) and [Agent Framework](https://nam06.safelinks.protection.outlook.com/?url=https%3A%2F%2Fgithub.com%2Fmicrosoft%2Fagent-framework%2Fblob%2Fmain%2FTRANSPARENCY_FAQ.md&data=05%7C02%7Chowieleung%40microsoft.com%7C42645ec29da244bd920508de2095bcad%7C72f988bf86f141af91ab2d7cd011db47%7C1%7C0%7C638984024651325701%7CUnknown%7CTWFpbGZsb3d8eyJFbXB0eU1hcGkiOnRydWUsIlYiOiIwLjAuMDAwMCIsIlAiOiJXaW4zMiIsIkFOIjoiTWFpbCIsIldUIjoyfQ%3D%3D%7C0%7C%7C%7C&sdata=19nfzxn8ZN1qr7Hy77fn%2BgFXD1sc%2BXiuPuUi3H2NNz4%3D&reserved=0).
+
 ## Solution Overview
 
 This solution deploys a web-based chat application with an AI agent running in Azure Container App.
 
-The agent leverages the Azure AI Agent service and utilizes Azure AI Search for knowledge retrieval from uploaded files, enabling it to generate responses with citations. The solution also includes built-in monitoring capabilities with tracing to ensure easier troubleshooting and optimized performance.
+The agent leverages the Foundry Agent Service and utilizes Azure AI Search for knowledge retrieval from uploaded files, enabling it to generate responses with citations. The solution also includes built-in monitoring capabilities with tracing to ensure easier troubleshooting and optimized performance.
 
-This solution creates an Azure AI Foundry project and Azure AI services. More details about the resources can be found in the [resources](#resources) documentation. There are options to enable logging, tracing, and monitoring.
+This solution creates a Microsoft Foundry project and Foundry Tools. More details about the resources can be found in the [resources](#resources) documentation. There are options to enable logging, tracing, and monitoring.
 
 Instructions are provided for deployment through GitHub Codespaces, VS Code Dev Containers, and your local development environment.
 
 ### Solution Architecture
 
-![Architecture diagram showing that user input is provided to the Azure Container App, which contains the app code. With user identity and resource access through managed identity, the input is used to form a response. The input and the Azure monitor are able to use the Azure resources deployed in the solution: Application Insights, Azure AI Foundry Project, Azure AI Services, Storage account, Azure Container App, and Log Analytics Workspace.](docs/images/architecture.png)
+![Architecture diagram showing that user input is provided to the Azure Container App, which contains the app code. With user identity and resource access through managed identity, the input is used to form a response. The input and the Azure monitor are able to use the Azure resources deployed in the solution: Application Insights, Microsoft Foundry Project, Foundry Tools, Storage account, Azure Container App, and Log Analytics Workspace.](docs/images/architecture.png)
 
-The app code runs in Azure Container App to process the user input and generate a response to the user. It leverages Azure AI projects and Azure AI services, including the model and agent.
+The app code runs in an Azure Container App to process user input and generate a response to the user. It leverages Microsoft Foundry projects and Foundry Tools, including the model and agent.
 
 ### Key Features
 
@@ -38,11 +40,14 @@ Integrated monitoring capabilities, including Azure Monitor and Application Insi
 - **[Flexible Deployment Options](./docs/deployment.md)**<br/>
 The solution supports deployment through GitHub Codespaces, VS Code Dev Containers, or local environments, providing flexibility for different development workflows.
 
+- **[Continuous Evaluation](./docs/other_features.md#continuous-evaluation)**<br/>
+Proactively monitor and assess your agent's performance over time with continuous evaluation that automatically checks real-world interactions to identify potential issues before they impact users.
+
 - **[Agent Evaluation](./docs/other_features.md#agent-evaluation)**<br/>
-This solution demonstrates how you can evaluate your agent's performance and quality during local development and incorporate it into monitoring and CI/CD workflow.
+This solution demonstrates how you can evaluate your agent's performance and quality through Pytest.
 
 - **[AI Red Teaming Agent](./docs/other_features.md#ai-red-teaming-agent)**<br/>
-Facilitates the creation of an AI Red Teaming Agent that can run batch automated scans for safety and security scans on your Agent solution to check your risk posture before deploying it into production.
+Facilitates the creation of an AI Red Teaming Agent through Pytest that can run batch automated scans for safety and security on your Agent solution to check your risk posture before deploying it into production.
 
 <br/>
 
@@ -111,9 +116,9 @@ The majority of the Azure resources used in this infrastructure are on usage-bas
 
 You can try the [Azure pricing calculator](https://azure.microsoft.com/pricing/calculator) for the resources:
 
-- **Azure AI Foundry**: Free tier. [Pricing](https://azure.microsoft.com/pricing/details/ai-studio/)  
+- **Microsoft Foundry**: Free tier. [Pricing](https://azure.microsoft.com/pricing/details/ai-studio/)  
 - **Azure Storage Account**: Standard tier, LRS. Pricing is based on storage and operations. [Pricing](https://azure.microsoft.com/pricing/details/storage/blobs/)  
-- **Azure AI Services**: S0 tier, defaults to gpt-4o-mini. Pricing is based on token count. [Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/)  
+- **Foundry Tools**: S0 tier, defaults to gpt-4o-mini. Pricing is based on token count. [Pricing](https://azure.microsoft.com/pricing/details/cognitive-services/)  
 - **Azure Container App**: Consumption tier with 0.5 CPU, 1GiB memory/storage. Pricing is based on resource allocation, and each month allows for a certain amount of free usage. [Pricing](https://azure.microsoft.com/pricing/details/container-apps/)  
 - **Log analytics**: Pay-as-you-go tier. Costs based on data ingested. [Pricing](https://azure.microsoft.com/pricing/details/monitor/)  
 - **Agent Evaluations**: Incurs the cost of your provided model deployment used for local evaluations.  

azure.yaml

@@ -4,7 +4,7 @@
 
 name: azd-get-started-with-ai-agents
 metadata:
-  template: azd-get-started-with-ai-agents@1.0.4
+  template: azd-get-started-with-ai-agents@2.0.0b1
 requiredVersions:
   azd: ">=1.14.0"
 
@@ -85,4 +85,4 @@ pipeline:
     - AZURE_EXISTING_AIPROJECT_ENDPOINT
     - AZURE_EXISTING_AGENT_ID
     - ENABLE_AZURE_MONITOR_TRACING
-    - AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED
+    - OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT

docs/deploy_customization.md

@@ -50,7 +50,7 @@ To override any of those resource names, run `azd env set <key> <value>` before
 
 ## Customizing model deployments
 
-For more information on the Azure OpenAI models and non-Microsoft models that can be used in your deployment, view the [list of models supported by Azure AI Agent Service](https://learn.microsoft.com/azure/ai-services/agents/concepts/model-region-support).
+For more information on the Azure OpenAI models and non-Microsoft models that can be used in your deployment, view the [list of models supported by Foundry Agent Service](https://learn.microsoft.com/azure/ai-services/agents/concepts/model-region-support).
 
 To customize the model deployments, you can set the following environment variables:
 

docs/deployment.md

@@ -184,9 +184,9 @@ When you start a deployment, most parameters will have default values. You can c
 
 | **Setting** | **Description** |  **Default value** |
 |------------|----------------|  ------------|
-| **Existing Project Resource ID** | Specify an existing project resource ID to be used instead of provisioning new Azure AI Foundry project and Azure AI services. |   |
+| **Existing Project Resource ID** | Specify an existing project resource ID to be used instead of provisioning new Azure AI Foundry project and Foundry Tools. |   |
 | **Azure Region** | Select a region with quota which supports your selected model. |   |
-| **Model** | Choose from the [list of models supported by Azure AI Agent Service](https://learn.microsoft.com/azure/ai-services/agents/concepts/model-region-support) for your selected region. | gpt-4o-mini |  
+| **Model** | Choose from the [list of models supported by Foundry Agent Service](https://learn.microsoft.com/azure/ai-services/agents/concepts/model-region-support) for your selected region. | gpt-4o |  
 | **Model Format** | Choose from OpenAI or Microsoft, depending on your model. | OpenAI |  
 | **Model Deployment Capacity** | Configure capacity for your model. | 80k |
 | **Embedding Model** | Choose from text-embedding-3-large, text-embedding-3-small, and text-embedding-ada-002. This may only be deployed if Azure AI Search is enabled. |  text-embedding-3-small |
@@ -225,7 +225,7 @@ azd env set ENABLE_AZURE_MONITOR_TRACING true
 To enable message contents to be included in the traces, set the following environment variable. Note that the messages may contain personally identifiable information.
 
 ```shell
-azd env set AZURE_TRACING_GEN_AI_CONTENT_RECORDING_ENABLED true
+azd env set OTEL_INSTRUMENTATION_GENAI_CAPTURE_MESSAGE_CONTENT true
 ```
 
 You can view the App Insights tracing in Azure AI Foundry. Select your project on the Azure AI Foundry page and then click 'Tracing'.
@@ -239,9 +239,8 @@ You can view the App Insights tracing in Azure AI Foundry. Select your project o
 
 The default for the model capacity in deployment is 80k tokens for chat model and 50k for embedded model for AI Search. For optimal performance, it is recommended to increase to 100k tokens. You can change the capacity by following the steps in [setting capacity and deployment SKU](deploy_customization.md#customizing-model-deployments).
 
-- Navigate to the home screen of the [Azure AI Foundry Portal](https://ai.azure.com/)
-- Select Quota Management buttom at the bottom of the home screen
-* In the Quota tab, click the GlobalStandard dropdown and select the model and region you are using for this accelerator to see your available quota. Please note gpt-4o-mini and text-embedding-3-small are used as default.
+- Navigate to [Monitor and track your quota usage](https://ai.azure.com/managementCenter/quota)
+* In the Quota tab, click the GlobalStandard dropdown and select the model and region you are using for this accelerator to see your available quota. Please note gpt-4o and text-embedding-3-small are used as default.
 - Request more quota or delete any unused model deployments as needed.
 
 </details>
@@ -269,7 +268,7 @@ Once you've opened the project in [Codespaces](#github-codespaces) or in [Dev Co
 
 3. You will be prompted to provide an `azd` environment name (like "azureaiapp"), select a subscription from your Azure account, and select a location which has quota for all the resources. Then, it will provision the resources in your account and deploy the latest code.
 
-    - For guidance on selecting a region with quota and model availability, follow the instructions in the [quota recommendations](#quota-recommendations) section and ensure that your model is available in your selected region by checking the [list of models supported by Azure AI Agent Service](https://learn.microsoft.com/azure/ai-services/agents/concepts/model-region-support)
+    - For guidance on selecting a region with quota and model availability, follow the instructions in the [quota recommendations](#quota-recommendations) section and ensure that your model is available in your selected region by checking the [list of models supported by Foundry Agent Service](https://learn.microsoft.com/azure/ai-services/agents/concepts/model-region-support)
     - This deployment will take 7-10 minutes to provision the resources in your account and set up the solution with sample data.
     - If you get an error or timeout with deployment, changing the location can help, as there may be availability constraints for the resources. You can do this by running `azd down` and deleting the `.azure` folder from your code, and then running `azd up` again and selecting a new region.