Unleashing Innovation: AI Agent Development with Azure AI Foundry

Creating AI agents using Azure AI Foundry is a game-changer for businesses and developers looking to harness the power of artificial intelligence. These AI agents can automate complex tasks, provide insightful data analysis, and enhance customer interactions, leading to increased efficiency and productivity. By leveraging Azure AI Foundry, organizations can build, deploy, and manage AI solutions with ease, ensuring they stay competitive in an ever-evolving technological landscape. The importance of creating AI agents lies in their ability to transform operations, drive innovation, and deliver personalized experiences, making them an invaluable asset in today's digital age.

Let's take a look at how to create an agent on Azure AI Foundry. We'll explore some of the features and experiment with its capabilities in the playground. I recommend by creating a new resource group with a new Azure OpenAI resource. Once the Azure OpenAI Resource is created,  follow these steps to get started with Azure AI Foundry Agents.

Implementation Overview

1. Open Azure AI Foundry and click on the Azure AI Foundry link at the top right to get to the home page where you'll see all your projects.
2. Click on + Create project then click on Create new hub
3. Give it a name then click Next and Create

    

4. New resources will be created with your new project. Once inside your new project you should see the Agents preview option on the left menu

    

5. Select your Azure OpenAI Service resource and click Let's go

We can now get started with implementation. A model needs to be deployed. However, it's important to consider which models can be used and their regions for creating these agents. Below is a quick summary of what's currently available.

Current supported models for Agent development from Azure OpenAI Supported models in Azure AI Agent Service - Azure AI services | Microsoft Learn

Other models supported include Meta-Llama-405B-Instruct, Mistral-large-2407, Cohere-command-r-plus, and Cohere-command-r.

I've deployed gpt-4 as Global Standard and can now create a new agent. Click on +New agent.

A new Agent will be created and details such as the agent instructions, model deployment, Knowledge and Action configurations, and model settings are shown. Incorporating knowledge into AI agents is to enhance their ability to provide accurate, relevant, and context-specific responses. This makes them more effective in automating tasks, answering complex queries, and supporting decision-making processes. Actions enable AI agents to perform specific tasks and interact with various service and data sources. Here we can leverage these abilities by adding a Custom Function, OpenAPI 3.0 specified tool, or an Azure function to help run tasks. The Code Interpreter feature within Actions empowers the agent to read and analyze datasets, generate code, and create visualizations such as graphs and charts. In the next section we'll go deeper with code interpreters' abilities.

Code Interpreter

For this next step I'll leverage weatherHistory.csv file from Weather Dataset for code interpreter to perform on. Next Actions click on + Add then click on Code interpreter and add the csv file.

Update the Instructions to "You are a Weather Data Expert Agent, designed to provide accurate, up-to-date, and detailed weather information." Lets explore what Code interpreter can do. Click on Try in playground on the top right. I'll start by asking "can you tell me which month had the most rain?", code interpreter already knows that I'm asking a question in reference to the data file I just gave it and will breakdown the question into multiple steps to provide the best possible answer. We can see that based on the dataset, August 2010 has the most where 768 instances of rainfall were recorded.

We'll take it a step further and create a graph using a different question. Let's ask the agent "ok, can you create a bar chart that shows the amount of rain fall from each year using the provided dataset?" in which the agent will respond with the following: 

This is just a quick demonstration of how powerful code interpreter can be. Code interpreter allows for efficient data interpretation and presentation as shown above, making it easier to derive insights and make informed decisions. We'll create and add a Bing Grounding Resource which will allow an agent to include real-time public web data into their responses.

Bing Grounding Resource

A Bing Grounding Resource is a powerful tool that enables AI agents to access and incorporate real-time data from the web into their responses and also ensures that the information provided by the agents is accurate, current, and relevant. An agent will be able to perform Bing searches when needed, fetching up-to-date information and enhancing the overall reliability and transparency of its responses. By leveraging Bing Grounding, AI agents can deliver more precise and contextually appropriate answers, significantly improving user satisfaction and trust.

To add a Bing Ground Resource to the agent:

1. Create the Resource: Navigate to the Azure AI Foundry portal and create a new Bing Grounding resource.
2. Add Knowledge: Go to your agent in Azure AI Foundry, click on + Add next to Knowledge on the right side, select Grounding with Big Search, + Create connection. Add connection with API key. The Bing Grounding resource is now added to your agent. 

    

   In the playground I'll add first ask "Is it raining over downtown New York today?". I will get a live response that also includes the links to the sources where the information was retrieved from. The agent responds as shown below:Next i'll ask the agent "How's should I prepare for the weather in New York this week? Any clothing recommendations?" in which the agent responds:

   The agent is able to breakdown the question using gpt-4 in detail by leveraging the source information from Bing and providing appropriate information to the user. Other the capabilities of custom functions, OpenAPI 3.0 specified tools, and Azure Functions significantly enhance the versatility and power of Azure AI agents. Custom functions allow agents to perform specialized tasks tailored to specific business needs, while OpenAPI 3.0 specified tools enable seamless integration with a wide range of external services and APIs. Azure Functions further extend the agent's capabilities by allowing it to execute serverless code, automating complex workflows and processes. Together, these features empower developers to build highly functional and adaptable AI agents that can efficiently handle diverse tasks, drive innovation, and deliver exceptional value to users.

Conclusion

Developing an AI Agent on Azure AI Foundry is a swift and efficient process, thanks to its robust features and comprehensive tools. The platform's Bing Grounding Resource ensures that your AI models are well-informed and contextually accurate, leveraging vast amounts real-time of data to enhance performance. Additionally, the Code Interpreter simplifies the integration and execution of solving complex tasks involving data analysis.

By utilizing these powerful resources, you can accelerate the development of intelligent agents that are not only capable of understanding and responding to user inputs but also continuously improving through iterative learning. Azure AI Foundry provides a solid foundation for creating innovative AI solutions that can drive significant value across various applications.

Additional Resources:

Quickstart - Create a new Azure AI Agent Service project - Azure AI services | Microsoft Learn

How to use Grounding with Bing Search in Azure AI Agent Service - Azure OpenAI | Microsoft Learn