Updates to agent framework module

Author: buzahid

learn-pr/wwl-data-ai/develop-ai-agent-with-semantic-kernel/includes/2-understand-semantic-kernel-agents.md

@@ -6,13 +6,15 @@ AI Agents can be developed using many different tools and platforms, including t
 
 The Microsoft Agent Framework offers different components that can be used individually or combined.
 
-- **Chat clients** - provide abstractions for connecting to AI services from different providers under a common interface. Supported providers include Azure OpenAI, OpenAI, Anthropic, and more through the `BaseChatClient` abstraction.
+- **Agents** - provides a consistent interface and enables different features like multi-agent orchestration. Out of the box, agents support function calling, multi-turn conversations with chat history, service-provided tools, structured outputs, and streaming responses.
+
+- **Chat providers** - provide abstractions for connecting to AI services from different providers under a common interface. Supported providers include Azure OpenAI, OpenAI, Anthropic, Copilot, and more through the `BaseAgent` abstraction.
 
 - **Function tools** - containers for custom functions that extend agent capabilities. Agents can automatically invoke functions to integrate with external APIs and services.
 
 - **Built-in tools** - prebuilt capabilities including Code Interpreter for Python execution, File Search for document analysis, and Web Search for internet access.
 
-- **Conversation management** - structured message system with roles (USER, ASSISTANT, SYSTEM, TOOL) and `AgentThread` for persistent conversation context across interactions.
+- **Conversation management** - structured message system with roles (USER, ASSISTANT, SYSTEM, TOOL) and `AgentSession` for persistent conversation context across interactions.
 
 - **Workflow orchestration** - supports sequential workflows, concurrent execution, group chat, and handoff patterns for complex multi-agent collaboration.
 
@@ -36,7 +38,7 @@ When you use Microsoft Foundry Agents, you get the full power of enterprise Azur
 
 - **BaseAgent** - the foundation for all agents with consistent methods, providing a unified interface across all agent types.
 
-- **Agent threads** - manage persistent conversation context and store conversation history across sessions using the `AgentThread` class.
+- **Agent session** - manage persistent conversation context and store conversation history across sessions using the `AgentSession` class.
 
 - **Chat messages** - organized structure for agent communication using role-based messaging (USER, ASSISTANT, SYSTEM, TOOL) that enables smooth communication and integration.
 

learn-pr/wwl-data-ai/develop-ai-agent-with-semantic-kernel/includes/3-create-azure-ai-agent.md

@@ -8,56 +8,20 @@ To use a Microsoft Foundry Agent:
 1. Create a Microsoft Foundry project.
 1. Add the project connection string to your Microsoft Agent Framework application code.
 1. Set up authentication credentials.
-1. Create a `ChatAgent` with an `AzureAIAgentClient`.
-1. Define tools and instructions for your agent.
+1. Connect to your project client with the `AzureAIAgentClient` class.
+1. Create an `Agent` instance with the client, instructions, and tools you want to use.
 
-Here's the code that shows how to create a Microsoft Foundry Agent:
-
-```python
-from agent_framework import AgentThread, ChatAgent
-from agent_framework.azure import AzureAIAgentClient
-from azure.identity.aio import AzureCliCredential
-
-def get_weather(
-    location: Annotated[str, Field(description="The location to get the weather for.")],
-) -> str:
-    """Get the weather for a given location."""
-    return f"The weather in {location} is sunny with a high of 25°C."
-
-# Create a ChatAgent with Azure AI client
-async with (
-    AzureCliCredential() as credential,
-    ChatAgent(
-        chat_client=AzureAIAgentClient(async_credential=credential),
-        instructions="You are a helpful weather agent.",
-        tools=get_weather,
-    ) as agent,
-):
-    # Agent is now ready to use
-```
-
-Once your agent is created, you can create a thread to interact with your agent and get responses to your questions. For example:
-
-```python
-# Create the agent thread for ongoing conversation
-thread = agent.get_new_thread()
-
-# Ask questions and get responses
-first_query = "What's the weather like in Seattle?"
-print(f"User: {first_query}")
-first_result = await agent.run(first_query, thread=thread)
-print(f"Agent: {first_result.text}")
-```
+Once your agent is created, you can create a conversation session to interact with your agent and get responses to your questions. 
 
 ### Microsoft Foundry Agent key components
 
 The Microsoft Agent Framework Microsoft Foundry Agent uses the following components to work:
 
 - **AzureAIAgentClient** - manages the connection to your Microsoft Foundry project. This client lets you access the services and models associated with your project and provides enterprise-level authentication and security features.
 
-- **ChatAgent** - the main agent class that combines the client, instructions, and tools to create a working AI agent that can handle conversations and complete tasks.
+- **Agent** - the main agent class that combines the client, instructions, and tools to create a working AI agent that can handle conversations and complete tasks.
 
-- **AgentThread** - automatically keeps track of conversation history between agents and users, and manages the conversation state. You can create new threads or reuse existing ones to maintain context across interactions.
+- **AgentSession** - automatically keeps track of conversation history between agents and users, and manages the conversation state. You can create new threads or reuse existing ones to maintain context across interactions.
 
 - **Tools integration** - support for custom functions that extend agent capabilities. Functions are automatically registered and can be called by agents to connect with external APIs and services.
 

learn-pr/wwl-data-ai/develop-ai-agent-with-semantic-kernel/includes/5-exercise.md

@@ -4,7 +4,7 @@ Now you're ready to build an agent with the Microsoft Agent Framework. In this e
 
 Launch the exercise and follow the instructions.
 
-[![Button to launch exercise.](../media/launch-exercise.png)](https://go.microsoft.com/fwlink/?linkid=2313222&azure-portal=true)
+[![Button to launch exercise.](../media/launch-exercise.png)](https://go.microsoft.com/fwlink/?linkid=2353605&azure-portal=true)
 
 > [!TIP]
 > After completing the exercise, if you're finished exploring Azure AI Agents, delete the Azure resources that you created during the exercise.

learn-pr/wwl-data-ai/orchestrate-semantic-kernel-multi-agent-solution/includes/2-understand-agent-framework.md

@@ -28,19 +28,7 @@ The framework includes several core features that power agent functionality:
 
 - **Conversation management**
 
-    Agents can maintain conversation history across multiple interactions using `AgentThread`, allowing them to track previous interactions and adapt responses accordingly. The structured message system uses roles (USER, ASSISTANT, SYSTEM, TOOL) for persistent conversation context.
-
-### Types of agents
-
-The Microsoft Agent Framework supports several different types of agents from multiple providers:
-
-- **Microsoft Foundry Agent** - a specialized agent within the Microsoft Agent Framework designed to provide enterprise-grade conversational capabilities with seamless tool integration. It automatically handles tool calling and securely manages conversation history using threads, reducing the overhead of maintaining state. Microsoft Foundry Agents support built-in tools and provide integration capabilities for Azure AI Search, Azure Functions, and other Azure services.
-
-- **ChatAgent**: designed for general conversation and task completion interfaces. The `ChatAgent` type provides natural language processing, contextual understanding, and dialogue management with support for custom tools and instructions.
-
-- **OpenAI Assistant Agent**: designed for advanced capabilities using OpenAI's Assistant API. This agent type supports goal-driven interactions with features like code interpretation and file search through the OpenAI platform.
-
-- **Anthropic Agent**: provides access to Anthropic's Claude models with the framework's unified interface, supporting advanced reasoning and conversation capabilities.
+    Agents can maintain conversation history across multiple interactions using `AgentSession`, allowing them to track previous interactions and adapt responses accordingly. The structured message system uses roles (USER, ASSISTANT, SYSTEM, TOOL) for persistent conversation context.
 
 ## Why you should use the Microsoft Agent Framework
 

learn-pr/wwl-data-ai/orchestrate-semantic-kernel-multi-agent-solution/includes/9-exercise.md

@@ -2,7 +2,7 @@ Now it's your opportunity to build a multi agent solution with the Microsoft Age
 
 Launch the exercise and follow the instructions.
 
-[![Button to launch exercise.](../media/launch-exercise.png)](https://go.microsoft.com/fwlink/?linkid=2310729&azure-portal=true)
+[![Button to launch exercise.](../media/launch-exercise.png)](https://go.microsoft.com/fwlink/?linkid=2353606&azure-portal=true)
 
 > [!TIP]
 > After completing the exercise, if you're finished exploring multi-agents with Microsoft Agent Framework, delete the Azure resources that you created during the exercise.