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Azure AI Foundry Agent Service

Azure AI Foundry Agent Service is a managed service in Azure that is designed to provide a framework for creating, managing, and using AI agents within Azure AI Foundry. The service is based on the OpenAI Assistants API but with increased choice of models, data integration, and enterprise security; enabling you to use both the OpenAI SDK and the Azure Foundry SDK to develop agentic solutions.

OpenAI Assistants API

The OpenAI Assistants API provides a subset of the features in Foundry Agent Service, and can only be used with OpenAI models. In Azure, you can use the Assistants API with Azure OpenAI, though in practice the Foundry Agent Service provides greater flexibility and functionality for agent development on Azure.

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AI agents are smart software services that combine generative AI models with contextual data and the ability to automate tasks based on user input and environmental factors that they perceive.

For example, an organization might build an AI agent to help employees manage expense claims. The agent might use a generative model combined with corporate expenses policy documentation to answer employee questions about what expenses can be claimed and what limits apply. Additionally, the agent could use a programmatic function to automatically submit expense claims for regularly repeated expenses (such as a monthly cellphone bill) or intelligently route expenses to the appropriate approver based on claim amounts.

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It’s important for software engineers to consider the impact of their software on users, and society in general; including considerations for its responsible use. When the application is imbued with artificial intelligence, these considerations are particularly important due to the nature of how AI systems work and inform decisions; often based on probabilistic models, which are in turn dependent on the data with which they were trained.

The human-like nature of AI solutions is a significant benefit in making applications user-friendly, but it can also lead users to place a great deal of trust in the application’s ability to make correct decisions. The potential for harm to individuals or groups through incorrect predictions or misuse of AI capabilities is a major concern, and software engineers building AI-enabled solutions should apply due consideration to mitigate risks and ensure fairness, reliability, and adequate protection from harm or discrimination.

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While you can perform many of the tasks needed to develop an AI solution directly in the Azure AI Foundry portal, developers also need to write, test, and deploy code.

Development tools and environments

There are many development tools and environments available, and developers should choose one that supports the languages, SDKs, and APIs they need to work with and with which they’re most comfortable. For example, a developer who focuses strongly on building applications for Windows using the .NET Framework might prefer to work in an integrated development environment (IDE) like Microsoft Visual Studio. Conversely, a web application developer who works with a wide range of open-source languages and libraries might prefer to use a code editor like Visual Studio Code (VS Code). Both of these products are suitable for developing AI applications on Azure.

The Azure AI Foundry for Visual Studio Code extension

When developing Azure AI Foundry based generative AI applications in Visual Studio Code, you can use the Azure AI Foundry for Visual Studio Code extension to simplify key tasks in the workflow, including:

• Creating a project.
• Selecting and deploying a model.
• Testing a model in the playground.
• Creating an agent.

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Azure AI Foundry is a platform for AI development on Microsoft Azure. While you can provision individual Azure AI services resources and build applications that consume them without it, the project organization, resource management, and AI development capabilities of Azure AI Foundry makes it the recommended way to build all but the most simple solutions.

Azure AI Foundry provides the Azure AI Foundry portal, a web-based visual interface for working with AI projects. It also provides the Azure AI Foundry SDK, which you can use to build AI solutions programmatically.

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Microsoft Azure provides a wide range of cloud services that you can use to develop, deploy, and manage an AI solution. The most obvious starting point for considering AI development on Azure is Azure AI services; a set of out-of-the-box prebuilt APIs and models that you can integrate into your applications. The following table lists some commonly used Azure AI services.

Considerations for Azure AI services resources

To use Azure AI services, you create one or more Azure AI resources in an Azure subscription and implement code in client applications to consume them. In some cases, AI services include web-based visual interfaces that you can use to configure and test your resources – for example to train a custom image classification model using the Custom Vision service you can use the visual interface to upload training images, manage training jobs, and deploy the resulting model.

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The term “Artificial Intelligence” (AI) covers a wide range of software capabilities that enable applications to exhibit human-like behavior. AI has been around for many years, and its definition has varied as the technology and use cases associated with it have evolved. In today’s technological landscape, AI solutions are built on machine learning models that encapsulate semantic relationships found in huge quantities of data; enabling applications to appear to interpret input in various formats, reason over the input data, and generate appropriate responses and predictions.

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In this module, you’ll be introduced to general cloud concepts. You’ll start with an introduction to the cloud in general. Then you’ll dive into concepts like shared responsibility, different cloud models, and explore the unique pricing method for the cloud.

If you’re already familiar with cloud computing, this module may be largely review for you.

Learning objectives

After completing this module, you’ll be able to:

• Define cloud computing.
• Describe the shared responsibility model.
• Define cloud models, including public, private, and hybrid.
• Identify appropriate use cases for each cloud model.
• Describe the consumption-based model.
• Compare cloud pricing models.

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A major benefit of cloud computing is the manageability options. There are two types of manageability for cloud computing that you’ll learn about in this series, and both are excellent benefits.

Management of the cloud

Management of the cloud speaks to managing your cloud resources. In the cloud, you can:

• Automatically scale resource deployment based on need.
• Deploy resources based on a preconfigured template, removing the need for manual configuration.
• Monitor the health of resources and automatically replace failing resources.
• Receive automatic alerts based on configured metrics, so you’re aware of performance in real time.

Management in the cloud

Management in the cloud speaks to how you’re able to manage your cloud environment and resources. You can manage these:

• Through a web portal.
• Using a command line interface.
• Using APIs.
• Using PowerShell.

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Whether you’re deploying infrastructure as a service or software as a service, cloud features support governance and compliance. Things like set templates help ensure that all your deployed resources meet corporate standards and government regulatory requirements. Plus, you can update all your deployed resources to new standards as standards change. Cloud-based auditing helps flag any resource that’s out of compliance with your corporate standards and provides mitigation strategies. Depending on your operating model, software patches and updates may also automatically be applied, which helps with both governance and security.

On the security side, you can find a cloud solution that matches your security needs. If you want maximum control of security, infrastructure as a service provides you with physical resources but lets you manage the operating systems and installed software, including patches and maintenance. If you want patches and maintenance taken care of automatically, platform as a service or software as a service deployments may be the best cloud strategies for you.

And because the cloud is intended as an over-the-internet delivery of IT resources, cloud providers are typically well suited to handle things like distributed denial of service (DDoS) attacks, making your network more robust and secure.

By establishing a good governance footprint early, you can keep your cloud footprint updated, secure, and well managed.

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