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## Overview
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This guide walks you through deploying the Multi Agent Custom Automation Engine Solution Accelerator to Azure. The deployment process takes approximately 10-15 minutes for the default Development/Testing configuration and includes both infrastructure provisioning and application setup.
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This guide walks you through deploying the Multi Agent Custom Automation Engine Solution Accelerator to Azure. The deployment process takes approximately 4-6 minutes for the default Development/Testing configuration and includes both infrastructure provisioning and application setup.
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🆘 **Need Help?** If you encounter any issues during deployment, check our [Troubleshooting Guide](./TroubleShootingSteps.md) for solutions to common problems.
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-[GPT Model Capacity](https://learn.microsoft.com/en-us/azure/ai-services/openai/concepts/models)
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**Recommended Regions:** East US, East US2, Australia East, Japan East, UK South, France Central
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📖 **Follow:**[Quota Check Instructions](./quota_check.md) to ensure sufficient capacity.
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<!--**Recommended Configuration:**
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**Recommended Configuration:**
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-**Default:** 200k tokens (minimum)
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- **Optimal:** 500k tokens (recommended for best performance)-->
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-**Optimal:** 500k tokens (recommended for best performance)
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> **Note:** When you run `azd up`, the deployment will automatically show you regions with available quota, so this pre-check is optional but helpful for planning purposes. You can customize these settings later in [Step 3.3: Advanced Configuration](#33-advanced-configuration-optional).
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You can customize various deployment settings before running `azd up`, including Azure regions, AI model configurations (deployment type, version, capacity), container registry settings, and resource names.
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📖 **Complete Guide:** See [Parameter Customization Guide](../docs/CustomizingAzdParameters.md) for the full list of available parameters and their usage.
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📖 **Complete Guide:** See [Parameter Customization Guide](./CustomizingAzdParameters.md) for the full list of available parameters and their usage.
By default, the **GPT model capacity** in deployment is set to **150k tokens**.
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By default, the **GPT model capacity** in deployment is set to **140k tokens**.
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To adjust quota settings, follow these [steps](./AzureGPTQuotaSettings.md).
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💡 **Before You Start:** If you encounter any issues during deployment, check our [Troubleshooting Guide](./TroubleShootingSteps.md) for common solutions.
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⚠️ **Critical: Redeployment Warning** - If you have previously run `azd up` in this folder (i.e., a `.azure` folder exists), you must [create a fresh environment](#creating-a-new-environment) to avoid conflicts and deployment failures.
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### 4.1 Authenticate with Azure
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```shell
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azd auth login --tenant-id <tenant-id>
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```
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> **Finding Tenant ID:**
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> 1. Open the [Azure Portal](https://portal.azure.com/).
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> 2. Navigate to **Microsoft Entra ID** from the left-hand menu.
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> 3. Under the **Overview** section, locate the **Tenant ID** field. Copy the value displayed.
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**Finding Tenant ID:**
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1. Open the [Azure Portal](https://portal.azure.com/)
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2. Navigate to **Microsoft Entra ID** from the left-hand menu
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3. Under the **Overview** section, locate the **Tenant ID** field. Copy the value displayed
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### 4.2 Start Deployment
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```
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**During deployment, you'll be prompted for:**
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1.**Environment name** (e.g., "macae1") - Must be 3-16 characters long, alphanumeric only
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1.**Environment name** (e.g., "macaedev") - Must be 3-16 characters long, alphanumeric only
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2.**Azure subscription** selection
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3.**Azure AI Foundry deployment region** - Select a region with available model quota for AI operations
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4.**Primary location** - Select the region where your infrastructure resources will be deployed
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5.**Resource group** selection (create new or use existing)
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**Expected Duration:**9-10 minutes for default configuration
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**Expected Duration:**4-6 minutes for default configuration
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-**Upon successful completion**, you will see a success message indicating that all resources have been deployed, along with the application URL and next steps for uploading team configurations and sample data.
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After successful deployment:
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1. Open [Azure Portal](https://portal.azure.com/)
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2. Navigate to your resource group
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3. Find the Frontend App Service
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4. Copy the **Default domain**
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3. Find the Container App
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4. Copy the **Application URI**
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⚠️ **Important:** Complete [Post-Deployment Steps](#step-5-post-deployment-configuration) before accessing the application.
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1. Access your application using the URL from Step 4.3
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2. Confirm the application loads successfully
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<!-- 3. Verify you can sign in with your authenticated account -->
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3. Verify you can sign in with your authenticated account
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### 5.4 Test the Application
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Follow the detailed workflow to test the Multi Agent functionality:
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**Quick Test Steps:**
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1. **Access the application** using the URL from Step 4.3
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2. **Sign in** with your authenticated account
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3. **Select a use case** from the available scenarios you uploaded in Step 5.1
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4. **Ask a sample question** relevant to the selected use case
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5. **Verify the response** includes appropriate multi-agent collaboration
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6. **Check the logs** in Azure Portal to confirm backend processing
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📖 **Detailed Instructions:** See the complete [Sample Workflow](./SampleQuestions.md) guide for step-by-step testing procedures.
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📖 **Detailed Instructions:** See the complete [Sample Workflow](./SampleQuestions.md) guide for step-by-step testing procedures and sample questions for each use case.
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## Step 6: Clean Up (Optional)
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### Remove All Resources
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To purge resources and clean up after deployment, use the `azd down`command or follow the Delete Resource Group Guide for manual cleanup through Azure Portal.
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```shell
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azd down
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```
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📚 **Learn More:**
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- [Local Development Setup](./LocalDevelopmentSetup.md) - Set up your local development environment
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- [Sample Questions](./SampleQuestions.md) - Explore sample questions and workflows
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- [MCP Server Documentation](./mcp_server.md) - Learn about Model Context Protocol server integration
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