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Problem: Agentic AI systems are set to transform the way businesses operate, however it can be fairly complex to build an initial MVP to demonstrate this value.
Solution: The Multi-Agent-Custom Automation Engine Solution Accelerator provides a ready to go application to use as the base of the MVP, or as a reference, allowing you to hit the ground running.
This accelerator uses the AutoGen framework from Microsoft Research. This is an open source project that is maintained by Microsoft Research’s AI Frontiers Lab. Please see this blog post for the latest information on using the AutoGen framework in production solutions.
The multi-agent approach allows users to utilize multiple AI agents simultaneously for repeatable tasks, ensuring consistency and efficiency. The agents collaborate with a manager on various assignments for onboarding a new employee, such as HR and tech support AI working together to set up software accounts, configure hardware, schedule onboarding meetings, register employees for benefits, and send welcome emails. Additionally, these agents can handle tasks like procurement and drafting press releases.
Multi-agent systems represent the next wave of Generative AI use cases, offering entirely new opportunities to drive efficiencies in your business. The Multi-Agent-Custom-Automation-Engine Solution Accelerator demonstrates several key benefits:
- Allows people to focus on what matters: by doing the heavy lifting involved with coordinating activities across an organization, peoples’ time is freed up to focus on their specializations.
- Enabling GenAI to scale: by not needing to build one application after another, organizations are able to reduce the friction of adopting GenAI across their entire organization. One capability can unlock almost unlimited use cases.
- Applicable to most industries: these are common challenges that most organizations face, across most industries.
Whilst still an emerging area, investing in agentic use cases, digitization and developing tools will be key to ensuring you are able to leverage these new technologies and seize the GenAI moment.
This application is an AI-driven orchestration system that manages a group of AI agents to accomplish tasks based on user input. It uses a FastAPI backend to handle HTTP requests, processes them through various specialized agents, and stores stateful information using Azure Cosmos DB. The system is designed to:
- Receive input tasks from users.
- Generate a detailed plan to accomplish the task using a Planner agent.
- Execute the plan by delegating steps to specialized agents (e.g., HR, Procurement, Marketing).
- Incorporate human feedback into the workflow.
- Maintain state across sessions with persistent storage.
This system is intended for developing and deploying custom AI solutions for specific customers. This code has not been tested as an end-to-end, reliable production application- it is a foundation to help accelerate building out multi-agent systems. You are encouraged to add your own data and functions to the agents, and then you must apply your own performance and safety evaluation testing frameworks to this system before deploying it.
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Azure Container Application
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Azure OpenAI
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Azure Cosmos DB
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The user deploying the template must have permission to create resources and resource groups.
To deploy this solution accelerator, ensure you have access to an Azure subscription with the necessary permissions to create resource groups and resources. Follow the steps in Azure Account Set Up
Check the Azure Products by Region page and select a region where the following services are available:
- Azure OpenAI Service
- Azure AI Search
- Azure Semantic Search
➡️ To ensure sufficient quota is available in your subscription, please follow Quota check instructions guide before you deploy the solution.
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When you start the deployment, most parameters will have default values, but you can update the below settings by following the steps here:
| Setting | Description | Default value |
|---|---|---|
| Environment Name | A 3-20 character alphanumeric value used to generate a unique ID to prefix the resources. | macaetemplate |
| Cosmos Location | A less busy region for CosmosDB, useful in case of availability constraints. | eastus2 |
| Deployment Type | Select from a drop-down list. | Global Standard |
| GPT Model | Choose from gpt-4o | gpt-4o |
| GPT Model Deployment Capacity | Configure capacity for GPT models. | 50k |
By default, the Gpt-4o model capacity in deployment is set to 50k tokens, so we recommend
For Global Standard | GPT-4o - the capacity to at least 50k tokens for optimal performance.
To adjust quota settings if required, follow these steps
Pick from the options below to see step-by-step instructions for: GitHub Codespaces, VS Code Dev Containers, Local Environments, and Bicep deployments.
Quick Deploy
You can one-click deploy this solution into your environment by clicking the button below, and filling in the deployment template.
Deploy in GitHub Codespaces
You can run this solution using GitHub Codespaces. The button will open a web-based VS Code instance in your browser:
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Open the solution accelerator (this may take several minutes):
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Accept the default values on the create Codespaces page
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Open a terminal window if it is not already open
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Continue with the deploying steps
Deploy in VS Code
You can run this solution in VS Code Dev Containers, which will open the project in your local VS Code using the Dev Containers extension:
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Start Docker Desktop (install it if not already installed)
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Open the project:
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In the VS Code window that opens, once the project files show up (this may take several minutes), open a terminal window.
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Continue with the deploying steps
Deploy in your local environment
To run the solution site and API backend only locally for development and debugging purposes, See the local deployment guide.
Once you've opened the project in Codespaces or in Dev Containers or locally, you can deploy it to Azure following the following steps.
To change the azd parameters from the default values, follow the steps here.
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Login to Azure:
azd auth login
azd auth login --tenant-id <tenant-id>
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Provision and deploy all the resources:
azd up
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Provide an
azdenvironment name (like "macaeapp") -
Select a subscription from your Azure account, and select a location which has quota for all the resources.
- 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.
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Open the Azure Portal, go to the deployed resource group, find the App Service and get the app URL from
Default domain. -
You can now delete the resources by running
azd down, if you are done trying out the application.
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Add App Authentication
Follow steps in App Authentication to configure authentication in app service.
Note: Authentication changes can take up to 10 minutes
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Deleting Resources After a Failed Deployment
Follow steps in Delete Resource Group If your deployment fails and you need to clean up the resources.
To debug the solution, you can use the Cosmos and OpenAI services you have manually deployed. To do this, you need to ensure that your Azure identity has the required permissions on the Cosmos and OpenAI services.
- For OpenAI service, you can add yourself to the ‘Cognitive Services OpenAI User’ permission in the Access Control (IAM) pane of the Azure portal.
- Cosmos is a little more difficult as it requires permissions be added through script. See these examples for more information:
Add the appropriate endpoints from Cosmos and OpenAI services to your .env file.
Note that you can configure the name of the Cosmos database in the configuration. This can be helpful if you wish to separate the data messages generated in local debugging from those associated with the cloud based solution. If you choose to use a different database, you will need to create that database in the Cosmos instance as this is not done automatically.
If you are using VSCode, you can use the debug configuration shown in the local deployment guide.
To help you get started, here are some Sample Questions you can follow once your application is up and running.
Please refer to Transparency FAQ for responsible AI transparency details of this solution accelerator.
This solution is designed to be easily customizable. You can modify the front end site, or even build your own front end and attach to the backend API. You can further customize the backend by adding your own agents with their own specific capabilities. Deeper technical information to aid in this customization can be found in this document.
Pricing varies per region and usage, so it isn't possible to predict exact costs for your usage. The majority of the Azure resources used in this infrastructure are on usage-based pricing tiers. However, Azure Container Registry has a fixed cost per registry per day.
You can try the Azure pricing calculator for the resources:
- Azure AI Foundry: Free tier. Pricing
- Azure AI Services: S0 tier, defaults to gpt-4o. Pricing is based on token count. Pricing
- 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
- Azure Container Registry: Basic tier. Pricing
- Azure Cosmos DB: Pricing
azd down.
This template uses Azure Key Vault to store all connections to communicate between resources.
This template also uses Managed Identity for local development and deployment.
To ensure continued best practices in your own repository, we recommend that anyone creating solutions based on our templates ensure that the Github secret scanning setting is enabled.
You may want to consider additional security measures, such as:
- Enabling Microsoft Defender for Cloud to secure your Azure resources.
- Protecting the Azure Container Apps instance with a firewall and/or Virtual Network.
- Python FastAPI documentation
- AutoGen Framework Documentation
- Azure Container App documentation
- Azure OpenAI Service - Documentation, quickstarts, API reference - Azure AI services | Microsoft Learn
- Azure Cosmos DB documentation
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