How can I deploy an AI Agent created in Microsoft AI Foundry to a website for public access?

Question

How can I deploy an AI Agent created in Microsoft AI Foundry to a website for public access?

saurabh pophare 0

Hi,

I created an AI agent using Microsoft AI Foundry, and it works perfectly using the thread and agent ID when tested within the Azure portal.

Now I want to deploy this agent on a website so that anyone (public users) can access it via a frontend . Here's what I've done so far:

I used Azure AI Foundry to build and test the agent.

I implemented it earlier using Streamlit and Python, where i implemented it in local host but used az login and default credentials
now i want created a flask where i want to deploy it on a hosting service but due to being on server i cannot use the default credentials (from azure.identity import DefaultAzureCredential)
i tried using from azure.identity import ClientSecretCredential but not understanding,how to do it plz help

Pavankumar Purilla 6,290 Reputation points Microsoft External Staff

2025-04-14T04:21:35.8+00:00
Hi saurabh pophare,

To deploy your AI Agent created in Microsoft AI Foundry to a public-facing website using Flask, you'll need to authenticate your application securely, as server environments don't support interactive login methods like DefaultAzureCredential. Instead, you should use ClientSecretCredential, which is designed for non-interactive environments.

First, register an application in Azure Active Directory via the Azure Portal. This process will provide you with the Tenant ID, Client ID, and allow you to create a Client Secret—credentials necessary for ClientSecretCredential. In your Flask application, use these credentials to instantiate ClientSecretCredential, which will authenticate your app with Azure services. This credential will enable your Flask backend to obtain access tokens required for making API calls to your AI Agent deployed in Azure AI Foundry.

Ensure that you store these credentials securely, preferably using environment variables, to prevent exposing sensitive information in your codebase. Your Flask app should expose an endpoint (e.g., /chat) that accepts user messages, forwards them to the AI Agent using the appropriate agent_id and thread_id, and returns the agent's response to the user. Finally, deploy your Flask application to a hosting service such as Azure App Service and configure your environment variables securely on the platform. This setup will allow public users to interact with your AI Agent through a web interface.

For detailed guidance, you can refer to the following resources:

Deploy Your First Azure AI Agent Service on Azure App Service

Azure Identity Client Library for Python

ClientSecretCredential Class Documentation

I hope this information helps. Thank you!
Pavankumar Purilla 6,290 Reputation points Microsoft External Staff

2025-04-15T05:51:18.9366667+00:00

Hi saurabh pophare,
Did you get any chance to check the response. Thank you!
Pavankumar Purilla 6,290 Reputation points Microsoft External Staff

2025-04-16T05:51:12.1033333+00:00

Hi saurabh pophare,
Just following up to see if you had a chance to review the above response. Thank you!

Your answer

Pavankumar Purilla 6,290 Reputation points Microsoft External Staff

2025-04-14T04:21:35.8+00:00

Hi saurabh pophare,

To deploy your AI Agent created in Microsoft AI Foundry to a public-facing website using Flask, you'll need to authenticate your application securely, as server environments don't support interactive login methods like DefaultAzureCredential. Instead, you should use ClientSecretCredential, which is designed for non-interactive environments.

First, register an application in Azure Active Directory via the Azure Portal. This process will provide you with the Tenant ID, Client ID, and allow you to create a Client Secret—credentials necessary for ClientSecretCredential. In your Flask application, use these credentials to instantiate ClientSecretCredential, which will authenticate your app with Azure services. This credential will enable your Flask backend to obtain access tokens required for making API calls to your AI Agent deployed in Azure AI Foundry.

Ensure that you store these credentials securely, preferably using environment variables, to prevent exposing sensitive information in your codebase. Your Flask app should expose an endpoint (e.g., /chat) that accepts user messages, forwards them to the AI Agent using the appropriate agent_id and thread_id, and returns the agent's response to the user. Finally, deploy your Flask application to a hosting service such as Azure App Service and configure your environment variables securely on the platform. This setup will allow public users to interact with your AI Agent through a web interface.

For detailed guidance, you can refer to the following resources:

Deploy Your First Azure AI Agent Service on Azure App Service

Azure Identity Client Library for Python

ClientSecretCredential Class Documentation

I hope this information helps. Thank you!
Pavankumar Purilla 6,290 Reputation points Microsoft External Staff

2025-04-15T05:51:18.9366667+00:00

Hi saurabh pophare,
Did you get any chance to check the response. Thank you!
Pavankumar Purilla 6,290 Reputation points Microsoft External Staff

2025-04-16T05:51:12.1033333+00:00

Hi saurabh pophare,
Just following up to see if you had a chance to review the above response. Thank you!

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How can I deploy an AI Agent created in Microsoft AI Foundry to a website for public access?

Your answer