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Azure AI Agent Service

Azure AI Agent Service allows for the development and deployment of AI agents with flexible models, such as directly calling open-source LLMs like Llama 3, Mistral, and Cohere.

Azure AI Agent Service provides stronger enterprise security mechanisms and data storage methods, making it suitable for enterprise applications.

It works out-of-the-box with multi-agent orchestration frameworks like AutoGen, Semantic Kernel and Microsoft Agent Framework. Using Semantic Kernel Python, we can create an Azure AI Agent with a user-defined plugin:

When to choose Azure AI Agent Service​

Azure AI Agent Service is best when you want a managed, service-hosted runtime in Foundry with enterprise operations built in.

  • Use it when you need centralized agent lifecycle management in the Foundry portal.
  • Use it when you want service-managed threads, runs, and message history for long-running or auditable workflows.
  • Use it when your team needs governance features such as RBAC, enterprise integrations, and operational observability.

Valuable in practice​

  • Regulated enterprise assistants: Customer support or operations copilots that require auditable, server-managed conversation history.
  • Platform team managed agents: Shared agents that multiple app teams can consume while a central team controls deployment and policy.
  • Tool-heavy enterprise workflows: Agents that need tight integration with Azure resources and managed tool execution.

Create AI Agent Service Agent​

  • navigate to labs/40-AIAgents folder, open game_agent_v3_aiagent.py file.
cd labs/40-AIAgents
  • run the agent and see the console output.
python game_agent_v3_aiagent.py

AI Agent

  • navigate to Microsoft Foundry portal, you should see the agent created in the portal.

  • you can also interact with the agent in the portal playground.

AI Agent Service Core Concepts​

Azure AI Agent Service has the following core concepts:

Agent​

Azure AI Agent Service integrates with Microsoft Foundry. Within AI Foundry, an AI Agent acts as a "smart" microservice that can be used to answer questions (RAG), perform actions, or completely automate workflows. It achieves this by combining the power of generative AI models with tools that allow it to access and interact with real-world data sources. Here's an example of an agent:

agent = project_client.agents.create_agent(
    model="gpt-4.1-mini",
    name="my-agent",
    instructions="You are helpful agent",
    tools=code_interpreter.definitions,
    tool_resources=code_interpreter.resources,
)

In this example, an agent is created with the model gpt-4o-mini, a name my-agent, and instructions You are helpful agent. The agent is equipped with tools and resources to perform code interpretation tasks.

Thread and messages​

The thread is another important concept. It represents a conversation or interaction between an agent and a user. Threads can be used to track the progress of a conversation, store context information, and manage the state of the interaction. Here's an example of a thread:

thread = project_client.agents.create_thread()
message = project_client.agents.create_message(
    thread_id=thread.id,
    role="user",
    content="Could you please create a bar chart for the operating profit using the following data and provide the file to me? Company A: $1.2 million, Company B: $2.5 million, Company C: $3.0 million, Company D: $1.8 million",
)

# Ask the agent to perform work on the thread
run = project_client.agents.create_and_process_run(thread_id=thread.id, agent_id=agent.id)

# Fetch and log all messages to see the agent's response
messages = project_client.agents.list_messages(thread_id=thread.id)
print(f"Messages: {messages}")

In the previous code, a thread is created. Thereafter, a message is sent to the thread. By calling create_and_process_run, the agent is asked to perform work on the thread. Finally, the messages are fetched and logged to see the agent's response. The messages indicate the progress of the conversation between the user and the agent. It's also important to understand that the messages can be of different types such as text, image, or file, that is the agents work has resulted in for example an image or a text response for example. As a developer, you can then use this information to further process the response or present it to the user.

Integrates with other AI frameworks​

Azure AI Agent Service works seamlessly with AutoGen, Semantic Kernel, and Microsoft Agent Framework, enabling flexible architecture options: use it as an orchestrator, combine it with other frameworks, or build your entire application within it.

Use Cases: Azure AI Agent Service is designed for enterprise applications that require secure, scalable, and flexible AI agent deployment.