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Problem: Agentic AI systems are set to transform customers' organizations, 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 customers to hit the ground running and save 2-4 weeks of development time.
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.
A contact center manager reviews contact center performance to ensure resources are being used efficiently. To identify areas for improvement, they need to understand the correlation between conversational and operational data.
The contact center manager uses their dashboard to identify how LLM-generated conversational analytics and insights are impacting operations to make an informed decision about how to improve their center’s performance.
There are many agents inside the box, spanning marketing, HR, procurement, technology, invoice auditing and more. Each of these agents has their own specialized set of knowledge and tools, making them experts in their domain. This makes the solution widely applicable to all roles facing these kinds of challenges.
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, digitatization 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, Legal, 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, RAI compliant 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.
This guide provides step-by-step instructions for deploying your application using Azure Container Registry (ACR) and Azure Container Apps.
There are several ways to deploy the solution. You can deploy to run in Azure in one click, or manually, or you can deploy locally.
When Deployment is complete, follow steps in Set Up Authentication in Azure App Service to add app authentication to your web app running on Azure App Service
To run the solution site and API backend locally, See the local deployment guide.
- Azure CLI installed
- Azure account with appropriate permissions
- Docker installed
- Azure Container Registry installed
Retrieve the admin credentials for your Azure Container Registry (ACR):
az acr credential show \
--name acrcontoso7wx5mg43sbnl4 \
--resource-group rg-ssattirajuLogin to your Azure Container Registry:
az acr login --name acrcontoso7wx5mg43sbnl4Build the Docker image and push it to your Azure Container Registry:
az acr build \
--registry acrcontoso7wx5mg43sbnl4 \
--resource-group rg-name \
--image macae:latest .List the images in your Azure Container Registry:
az acr repository list --name acrcontoso7wx5mg43sbnl4Ensure you have the latest version of the Azure Container Apps extension:
az extension add --name containerapp --upgrade
Retrieve a list of available Azure locations:
az account list-locations -o table
Create an environment for your Azure Container Apps:
az containerapp env create \
--name python-container-env \
--resource-group rg-name \
--location southeastasiaaz acr credential show -n acrcontoso7wx5mg43sbnl4create the container app with the config
az containerapp create \
--name python-container-app \
--resource-group rg-name \
--image acrcontoso7wx5mg43sbnl4.azurecr.io/macae:latest \
--environment python-container-env \
--ingress external --target-port 8000 \
--registry-server acrcontoso7wx5mg43sbnl4.azurecr.io \
--registry-username password \
--registry-password REGISTRY_PASSWORD \
--query properties.configuration.ingress.fqdn
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.
<Add Troubleshooting doc links here>
- 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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