fix: Waf changes

docs/CustomizingAzdParameters.md

@@ -17,6 +17,8 @@ By default this template will use the environment name as the prefix to prevent
 | `AZURE_AI_MODEL_NAME`           | string  | `o3`                    | Specifies the `o` model name.                                                         |
 | `AZURE_AI_MODEL_VERSION`        | string  | `2025-04-16`            | Specifies the `o` model version.                                                      |
 | `AZURE_AI_MODEL_CAPACITY`       | integer | `200`                   | Sets the model capacity (choose based on your subscription's available `o` capacity). |
+| `AZURE_ENV_VM_ADMIN_USERNAME`   | string  | ``                      | The administrator username for the virtual machine.                                   |
+| `AZURE_ENV_VM_ADMIN_PASSWORD`   | string  | ``                      | The administrator password for the virtual machine.                                   |
 
 ## How to Set a Parameter
 

docs/DeploymentGuide.md

@@ -34,6 +34,40 @@ This will allow the scripts to run for the current session without permanently c
 
 ## Deployment Options & Steps
 
+### Sandbox or WAF Aligned Deployment Options
+
+The [`infra`](../infra) folder of the Multi Agent Solution Accelerator contains the [`main.bicep`](../infra/main.bicep) Bicep script, which defines all Azure infrastructure components for this solution.
+
+By default, the `azd up` command uses the [`main.parameters.json`](../infra/main.parameters.json) file to deploy the solution. This file is pre-configured for a **sandbox environment** — ideal for development and proof-of-concept scenarios, with minimal security and cost controls for rapid iteration.
+
+For **production deployments**, the repository also provides [`main.waf.parameters.json`](../infra/main.waf.parameters.json), which applies a [Well-Architected Framework (WAF) aligned](https://learn.microsoft.com/en-us/azure/well-architected/) configuration. This option enables additional Azure best practices for reliability, security, cost optimization, operational excellence, and performance efficiency, such as:
+
+**How to choose your deployment configuration:**
+
+* Use the default `main.parameters.json` file for a **sandbox/dev environment**
+* For a **WAF-aligned, production-ready deployment**, copy the contents of `main.waf.parameters.json` into `main.parameters.json` before running `azd up`
+
+---
+
+### VM Credentials Configuration
+
+By default, the solution sets the VM administrator username and password from environment variables.
+If you do not configure these values, a randomly generated GUID will be used for both the username and password.
+
+To set your own VM credentials before deployment, use:
+
+```sh
+azd env set AZURE_ENV_VM_ADMIN_USERNAME <your-username>
+azd env set AZURE_ENV_VM_ADMIN_PASSWORD <your-password>
+```
+
+> [!TIP]
+> Always review and adjust parameter values (such as region, capacity, security settings and log analytics workspace configuration) to match your organization’s requirements before deploying. For production, ensure you have sufficient quota and follow the principle of least privilege for all identities and role assignments.
+
+
+> [!IMPORTANT]
+> The WAF-aligned configuration is under active development. More Azure Well-Architected recommendations will be added in future updates.
+
 Pick from the options below to see step-by-step instructions for GitHub Codespaces, VS Code Dev Containers, and Local Environments.
 
 | [![Open in GitHub Codespaces](https://github.com/codespaces/badge.svg)](https://codespaces.new/microsoft/Container-Migration-Solution-Accelerator) | [![Open in Dev Containers](https://img.shields.io/static/v1?style=for-the-badge&label=Dev%20Containers&message=Open&color=blue&logo=visualstudiocode)](https://vscode.dev/redirect?url=vscode://ms-vscode-remote.remote-containers/cloneInVolume?url=https://github.com/microsoft/Container-Migration-Solution-Accelerator) |
@@ -126,12 +160,28 @@ By default, the **o3 model capacity** in deployment is set to **200k tokens**.
 
 > **We recommend increasing the capacity to 500k tokens, if available, for optimal performance.**
 
-To adjust quota settings, follow these [steps](./AzureAIModelQuotaSettings.md.md).
+To adjust quota settings, follow these [steps](./AzureAIModelQuotaSettings.md).
 
 **⚠️ Warning:** Insufficient quota can cause deployment errors. Please ensure you have the recommended capacity or request additional capacity before deploying this solution.
 
 </details>
 
+<details>
+
+  <summary><b>Reusing an Existing Log Analytics Workspace</b></summary>
+
+  Guide to get your [Existing Workspace ID](/docs/re-use-log-analytics.md)
+
+</details>
+
+<details>
+
+  <summary><b>Reusing an Existing Azure AI Foundry Project</b></summary>
+
+  Guide to get your [Existing Project ID](/docs/re-use-foundry-project.md)
+
+</details>
+
 ### Deploying with AZD
 
 Once you've opened the project in [Codespaces](#github-codespaces), [Dev Containers](#vs-code-dev-containers), or [locally](#local-environment), you can deploy it to Azure by following these steps:

docs/re-use-foundry-project.md

@@ -0,0 +1,44 @@
+[← Back to *DEPLOYMENT* guide](/docs/DeploymentGuide.md#deployment-steps)
+
+# Reusing an Existing Azure AI Foundry Project
+To configure your environment to use an existing Azure AI Foundry Project, follow these steps:
+---
+### 1. Go to Azure Portal
+Go to https://portal.azure.com
+
+### 2. Search for Azure AI Foundry
+In the search bar at the top, type "Azure AI Foundry" and click on it. Then select the Foundry service instance where your project exists.
+
+![alt text](../docs/images/re_use_foundry_project/azure_ai_foundry_list.png)
+
+### 3. Navigate to Projects under Resource Management
+On the left sidebar of the Foundry service blade:
+
+- Expand the Resource Management section
+- Click on Projects (this refers to the active Foundry project tied to the service)
+
+### 4. Click on the Project
+From the Projects view: Click on the project name to open its details
+
+    Note: You will see only one project listed here, as each Foundry service maps to a single project in this accelerator
+
+![alt text](../docs/images/re_use_foundry_project/navigate_to_projects.png)
+
+### 5. Copy Resource ID
+In the left-hand menu of the project blade: 
+
+- Click on Properties under Resource Management
+- Locate the Resource ID field
+- Click on the copy icon next to the Resource ID value
+
+![alt text](../docs/images/re_use_foundry_project/project_resource_id.png)
+
+### 6. Set the Foundry Project Resource ID in Your Environment
+Run the following command in your terminal
+```bash
+azd env set AZURE_EXISTING_AI_PROJECT_RESOURCE_ID '<Existing Foundry Project Resource ID>'
+```
+Replace `<Existing Foundry Project Resource ID>` with the value obtained from Step 5.
+
+### 7. Continue Deployment
+Proceed with the next steps in the [deployment guide](/docs/DeploymentGuide.md#deployment-steps).

docs/re-use-log-analytics.md

@@ -0,0 +1,31 @@
+[← Back to *DEPLOYMENT* guide](/docs/DeploymentGuide.md#deployment-steps)
+
+# Reusing an Existing Log Analytics Workspace
+To configure your environment to use an existing Log Analytics Workspace, follow these steps:
+---
+### 1. Go to Azure Portal
+Go to https://portal.azure.com
+
+### 2. Search for Log Analytics
+In the search bar at the top, type "Log Analytics workspaces" and click on it and click on the workspace you want to use.
+
+![alt text](../docs/images/re_use_log/logAnalyticsList.png)
+
+### 3. Copy Resource ID
+In the Overview pane, Click on JSON View
+
+![alt text](../docs/images/re_use_log/logAnalytics.png)
+
+Copy Resource ID that is your Workspace ID
+
+![alt text](../docs/images/re_use_log/logAnalyticsJson.png)
+
+### 4. Set the Workspace ID in Your Environment
+Run the following command in your terminal
+```bash
+azd env set AZURE_ENV_LOG_ANALYTICS_WORKSPACE_ID '<Existing Log Analytics Workspace Id>'
+```
+Replace `<Existing Log Analytics Workspace Id>` with the value obtained from Step 3.
+
+### 5. Continue Deployment
+Proceed with the next steps in the [deployment guide](/docs/DeploymentGuide.md#deployment-steps).