Howie/agents v2 (#190)

Starting on November 18, 2025, we will introduce the azure-ai-projects 2.0.0b2 SDK. Because this release now includes agent-related capabilities, it will serve as the unified replacement for both azure-ai-projects 1.x.x and azure-ai-agents 1.x.x.

Agents created with azure-ai-agents 1.x.x or through earlier versions of the Foundry UI are managed by different back-end services and use a different version ID format. As a result, existing agents will not be compatible with the new Foundry UI or with azure-ai-projects ≥ 2.0.0b2.

To continue supporting older project and agent SDK versions, this repository provides a dedicated branch named foundry-project-agents-classic, which now serves as the primary development branch for legacy environments. If you are maintaining or extending existing solutions, you should rebase your work from this branch rather than from main.

Author: howieleung

.github/workflows/azure-dev.yml

@@ -10,7 +10,7 @@ on:
       - main
 
 # Set up permissions for deploying with secretless Azure federated credentials
-# https://learn.microsoft.com/en-us/azure/developer/github/connect-from-azure?tabs=azure-portal%2Clinux#set-up-azure-login-with-openid-connect-authentication
+# https://learn.microsoft.com/azure/developer/github/connect-from-azure?tabs=azure-portal%2Clinux#set-up-azure-login-with-openid-connect-authentication
 permissions:
   id-token: write
   contents: read

.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
 
@@ -32,17 +34,20 @@ The AI agent uses file search or Azure AI Search to retrieve knowledge from uplo
 - **[Customizable AI Model Deployment](./docs/deploy_customization.md#customizing-model-deployments)**<br/>
 The solution allows users to configure and deploy AI models, such as gpt-4o-mini, with options to adjust model capacity, and knowledge retrieval methods.
 
-- **[Built-in Monitoring and Tracing](./docs/other_features.md#tracing-and-monitoring)**<br/>
+- **[Built-in Monitoring and Tracing](./docs/observability.md#tracing-and-monitoring)**<br/>
 Integrated monitoring capabilities, including Azure Monitor and Application Insights, enable tracing and logging for easier troubleshooting and performance optimization.
 
 - **[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.
 
-- **[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.
+- **[Continuous Evaluation](./docs/observability.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/observability.md#agent-evaluation)**<br/>
+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.
+- **[AI Red Teaming Agent](./docs/observability.md#ai-red-teaming-agent)**<br/>
+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.  
@@ -135,7 +140,7 @@ You may want to consider additional security measures, such as:
 
 > **Important Security Notice** <br/>
 This template, the application code and configuration it contains, has been built to showcase Microsoft Azure specific services and tools. We strongly advise our customers not to make this code part of their production environments without implementing or enabling additional security features.  <br/><br/>
-For a more comprehensive list of best practices and security recommendations for Intelligent Applications, [visit our official documentation](https://learn.microsoft.com/en-us/azure/ai-foundry/).
+For a more comprehensive list of best practices and security recommendations for Intelligent Applications, [visit our official documentation](https://learn.microsoft.com/azure/ai-foundry/).
 
 ### Resources
 

SECURITY.md

@@ -4,15 +4,15 @@
 
 Microsoft takes the security of our software products and services seriously, which includes all source code repositories managed through our GitHub organizations, which include [Microsoft](https://github.com/Microsoft), [Azure](https://github.com/Azure), [DotNet](https://github.com/dotnet), [AspNet](https://github.com/aspnet), [Xamarin](https://github.com/xamarin), and [our GitHub organizations](https://opensource.microsoft.com/).
 
-If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://docs.microsoft.com/en-us/previous-versions/tn-archive/cc751383(v=technet.10)), please report it to us as described below.
+If you believe you have found a security vulnerability in any Microsoft-owned repository that meets [Microsoft's definition of a security vulnerability](https://docs.microsoft.com/previous-versions/tn-archive/cc751383(v=technet.10)), please report it to us as described below.
 
 ## Reporting Security Issues
 
 **Please do not report security vulnerabilities through public GitHub issues.**
 
 Instead, please report them to the Microsoft Security Response Center (MSRC) at [https://msrc.microsoft.com/create-report](https://msrc.microsoft.com/create-report).
 
-If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com).  If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://www.microsoft.com/en-us/msrc/pgp-key-msrc).
+If you prefer to submit without logging in, send email to [secure@microsoft.com](mailto:secure@microsoft.com).  If possible, encrypt your message with our PGP key; please download it from the [Microsoft Security Response Center PGP Key page](https://www.microsoft.com/msrc/pgp-key-msrc).
 
 You should receive a response within 24 hours. If for some reason you do not, please follow up via email to ensure we received your original message. Additional information can be found at [microsoft.com/msrc](https://www.microsoft.com/msrc). 
 
@@ -36,6 +36,6 @@ We prefer all communications to be in English.
 
 ## Policy
 
-Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/en-us/msrc/cvd).
+Microsoft follows the principle of [Coordinated Vulnerability Disclosure](https://www.microsoft.com/msrc/cvd).
 
 <!-- END MICROSOFT SECURITY.MD BLOCK -->

airedteaming/ai_redteaming.py

@@ -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

azure.yaml

@@ -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: