Repo sync for protected branch

README.md

@@ -1,12 +1,12 @@
 # Microsoft Semantic Kernel Documentation
 
-This is the GitHub repository for the technical product documentation for **Semantic Kernel**. This documentation is published at [Microsoft Semantic Kernel documentation](https://learn.microsoft.com/semantic-kernel).
+This is the GitHub repository for the technical product documentation for **Semantic Kernel**. This documentation is published at [Microsoft Semantic Kernel documentation](/semantic-kernel).
 
 ## How to contribute
 
-Thanks for your interest in [contributing](https://learn.microsoft.com/), home of technical content for Microsoft products and services.
+Thanks for your interest in contributing to the home of technical content for Microsoft products and services.
 
-To learn how to make contributions to the content in this repository, start with our [Docs contributor guide](https://learn.microsoft.com/contribute).
+To learn how to make contributions to the content in this repository, start with our [Docs contributor guide](/contribute).
 
 ## Code of conduct
 

semantic-kernel/Frameworks/agent/TOC.yml

@@ -4,7 +4,7 @@
   href: agent-architecture.md
 - name: Chat Completion Agent
   href: chat-completion-agent.md
-- name: Open AI Assistant Agent
+- name: OpenAI Assistant Agent
   href: assistant-agent.md
 - name: Agent Collaboration
   href: agent-chat.md

semantic-kernel/Frameworks/agent/agent-architecture.md

@@ -1,5 +1,5 @@
 ---
-title: Semantic Kernel Agent Architecture (Experimental)
+title: Semantic Kernel Agent Architecture
 description: An overview of the architecture of the Semantic Kernel Agent Framework and how it aligns with core Semantic Kernel features.
 zone_pivot_groups: programming-languages
 author: crickman
@@ -10,15 +10,15 @@ ms.service: semantic-kernel
 ---
 # An Overview of the Agent Architecture
 
-> [!WARNING]
-> The *Semantic Kernel Agent Framework* is in preview and is subject to change.
+> [!IMPORTANT]
+> Single-agent features, such as ChatCompletionAgent and OpenAIAssistantAgent, are in the release candidate stage. These features are nearly complete and generally stable, though they may undergo minor refinements or optimizations before reaching full general availability. However, agent chat patterns are still in the experimental stage. These patterns are under active development and may change significantly before advancing to the preview or release candidate stage.
 
 This article covers key concepts in the architecture of the Agent Framework, including foundational principles, design objectives, and strategic goals.
 
 
 ## Goals
 
-The _Agent Framework_ was developed with the following key priorities in mind:
+The `Agent Framework` was developed with the following key priorities in mind:
 
 - The _Semantic Kernel_ framework serves as the core foundation for implementing agent functionalities.
 - Multiple agents can collaborate within a single conversation, while integrating human input.
@@ -28,7 +28,7 @@ The _Agent Framework_ was developed with the following key priorities in mind:
 
 ## Agent
 
-The abstract _Agent_ class serves as the core abstraction for all types of agents, providing a foundational structure that can be extended to create more specialized agents. One key subclass is _Kernel Agent_, which establishes a direct association with a [_Kernel_](../../concepts/kernel.md) object. This relationship forms the basis for more specific agent implementations, such as the [_Chat Completion Agent_](./chat-completion-agent.md) and the [_Open AI Assistant Agent_](./assistant-agent.md), both of which leverage the Kernel's capabilities to execute their respective functions.
+The abstract `Agent` class serves as the core abstraction for all types of agents, providing a foundational structure that can be extended to create more specialized agents. One key subclass is _Kernel Agent_, which establishes a direct association with a [`Kernel`](../../concepts/kernel.md) object. This relationship forms the basis for more specific agent implementations, such as the [`ChatCompletionAgent`](./chat-completion-agent.md) and the [`OpenAIAssistantAgent`](./assistant-agent.md), both of which leverage the Kernel's capabilities to execute their respective functions.
 
 ::: zone pivot="programming-language-csharp"
 
@@ -49,7 +49,7 @@ The abstract _Agent_ class serves as the core abstraction for all types of agent
 
 ::: zone-end
 
-Agents can either be invoked directly to perform tasks or orchestrated within an [_Agent Chat_](./agent-chat.md), where multiple agents may collaborate or interact dynamically with user inputs. This flexible structure allows agents to adapt to various conversational or task-driven scenarios, providing developers with robust tools for building intelligent, multi-agent systems.
+Agents can either be invoked directly to perform tasks or orchestrated within an [`AgentChat`](./agent-chat.md), where multiple agents may collaborate or interact dynamically with user inputs. This flexible structure allows agents to adapt to various conversational or task-driven scenarios, providing developers with robust tools for building intelligent, multi-agent systems.
 
 #### Deep Dive:
 
@@ -64,7 +64,7 @@ Agents can either be invoked directly to perform tasks or orchestrated within an
 
 ## Agent Chat
 
-The  [_Agent Chat_](./agent-chat.md) class serves as the foundational component that enables agents of any type to engage in a specific conversation. This class provides the essential capabilities for managing agent interactions within a chat environment. Building on this, the [_Agent Group Chat_](./agent-chat.md#creating-an-agent-group-chat) class extends these capabilities by offering a stategy-based container, which allows multiple agents to collaborate across numerous interactions within the same conversation. 
+The  [`AgentChat`](./agent-chat.md) class serves as the foundational component that enables agents of any type to engage in a specific conversation. This class provides the essential capabilities for managing agent interactions within a chat environment. Building on this, the [`AgentGroupChat`](./agent-chat.md#creating-an-agentgroupchat) class extends these capabilities by offering a stategy-based container, which allows multiple agents to collaborate across numerous interactions within the same conversation. 
 
 This structure facilitates more complex, multi-agent scenarios where different agents can work together, share information, and dynamically respond to evolving conversations, making it an ideal solution for advanced use cases such as customer support, multi-faceted task management, or collaborative problem-solving environments.
 
@@ -74,7 +74,7 @@ This structure facilitates more complex, multi-agent scenarios where different a
 
 ## Agent Channel
 
-The _Agent Channel_ class enables agents of various types to participate in an [_Agent Chat_](./agent-chat.md). This functionality is completely hidden from users of the _Agent Framework_ and only needs to be considered by developers creating a custom [_Agent_](#agent).
+The _Agent Channel_ class enables agents of various types to participate in an [`AgentChat`](./agent-chat.md). This functionality is completely hidden from users of the `Agent Framework` and only needs to be considered by developers creating a custom [`Agent`](#agent).
 
 ::: zone pivot="programming-language-csharp"
 
@@ -96,14 +96,14 @@ The _Agent Channel_ class enables agents of various types to participate in an [
 
 ## Agent Alignment with _Semantic Kernel_ Features
 
-The _Agent Framework_ is built on the foundational concepts and features that many developers have come to know within the _Semantic Kernel_ ecosystem. These core principles serve as the building blocks for the Agent Framework’s design. By leveraging the familiar structure and capabilities of the _Semantic Kernel_, the Agent Framework extends its functionality to enable more advanced, autonomous agent behaviors, while maintaining consistency with the broader _Semantic Kernel_ architecture. This ensures a smooth transition for developers, allowing them to apply their existing knowledge to create intelligent, adaptable agents within the framework.
+The `Agent Framework` is built on the foundational concepts and features that many developers have come to know within the _Semantic Kernel_ ecosystem. These core principles serve as the building blocks for the Agent Framework’s design. By leveraging the familiar structure and capabilities of the _Semantic Kernel_, the Agent Framework extends its functionality to enable more advanced, autonomous agent behaviors, while maintaining consistency with the broader _Semantic Kernel_ architecture. This ensures a smooth transition for developers, allowing them to apply their existing knowledge to create intelligent, adaptable agents within the framework.
 
 
-### The _Kernel_
+### The `Kernel`
 
-At the heart of the _Semantic Kernel_ ecosystem is the [_Kernel_](../../concepts/kernel.md), which serves as the core object that drives AI operations and interactions. To create any agent within this framework, a _Kernel instance_ is required as it provides the foundational context and capabilities for the agent’s functionality. The _Kernel_ acts as the engine for processing instructions, managing state, and invoking the necessary AI services that power the agent's behavior.
+At the heart of the Semantic Kernel ecosystem is the [`Kernel`](../../concepts/kernel.md), which serves as the core object that drives AI operations and interactions. To create any agent within this framework, a _Kernel instance_ is required as it provides the foundational context and capabilities for the agent’s functionality. The `Kernel` acts as the engine for processing instructions, managing state, and invoking the necessary AI services that power the agent's behavior.
 
-The [_Chat Completion Agent_](./chat-completion-agent.md) and [_Open AI Assistant Agent_](./assistant-agent.md) articles provide specific details on how to create each type of agent.
+The [`ChatCompletionAgent`](./chat-completion-agent.md) and [`OpenAIAssistantAgent`](./assistant-agent.md) articles provide specific details on how to create each type of agent.
  These resources offer step-by-step instructions and highlight the key configurations needed to tailor the agents to different conversational or task-based applications, demonstrating how the Kernel enables dynamic and intelligent agent behaviors across diverse use cases.
 
 #### Related API's:
@@ -136,7 +136,7 @@ Plugins are a fundamental aspect of the _Semantic Kernel_, enabling developers t
 
 #### Example:
 
-- [How-To: _Chat Completion Agent_](./examples/example-chat-agent.md)
+- [How-To: `ChatCompletionAgent`](./examples/example-chat-agent.md)
 
 #### Related API's:
 
@@ -169,7 +169,7 @@ Plugins are a fundamental aspect of the _Semantic Kernel_, enabling developers t
 
 Agent messaging, including both input and response, is built upon the core content types of the _Semantic Kernel_, providing a unified structure for communication. This design choice simplifies the process of transitioning from traditional chat-completion patterns to more advanced agent-driven patterns in your application development. By leveraging familiar _Semantic Kernel_ content types, developers can seamlessly integrate agent capabilities into their applications without needing to overhaul existing systems. This streamlining ensures that as you evolve from basic conversational AI to more autonomous, task-oriented agents, the underlying framework remains consistent, making development faster and more efficient.
 
-> Note: The [_Open AI Assistant Agent_`_](./assistant-agent.md) introduced content types specific to its usage for _File References_ and _Content Annotation_: 
+> Note: The [`OpenAIAssistantAgent`](./assistant-agent.md) introduced content types specific to its usage for _File References_ and _Content Annotation_: 
 
 #### Related API's:
 
@@ -205,13 +205,13 @@ Agent messaging, including both input and response, is built upon the core conte
 
 ### [Templating](./agent-templates.md)
 
-An agent's role is primarily shaped by the instructions it receives, which dictate its behavior and actions. Similar to invoking a _Kernel_ [prompt](../../concepts/prompts/index.md), an agent's instructions can include templated parameters—both values and functions—that are dynamically substituted during execution. This enables flexible, context-aware responses, allowing the agent to adjust its output based on real-time input.
+An agent's role is primarily shaped by the instructions it receives, which dictate its behavior and actions. Similar to invoking a `Kernel` [prompt](../../concepts/prompts/index.md), an agent's instructions can include templated parameters—both values and functions—that are dynamically substituted during execution. This enables flexible, context-aware responses, allowing the agent to adjust its output based on real-time input.
 
 Additionally, an agent can be configured directly using a _Prompt Template Configuration_, providing developers with a structured and reusable way to define its behavior. This approach offers a powerful tool for standardizing and customizing agent instructions, ensuring consistency across various use cases while still maintaining dynamic adaptability.
 
 #### Example:
 
-- [How-To: _Chat Completion Agent_](./examples/example-chat-agent.md)
+- [How-To: `ChatCompletionAgent`](./examples/example-chat-agent.md)
 
 #### Related API's:
 
@@ -245,7 +245,7 @@ Additionally, an agent can be configured directly using a _Prompt Template Confi
 
 ### [Chat Completion](./chat-completion-agent.md)
 
-The [_Chat Completion Agent_](./chat-completion-agent.md) is designed around any _Semantic Kernel_ [AI service](../../concepts/ai-services/chat-completion/index.md), offering a flexible and convenient persona encapsulation that can be seamlessly integrated into a wide range of applications. This agent allows developers to easily bring conversational AI capabilities into their systems without having to deal with complex implementation details. It mirrors the features and patterns found in the underlying [AI service](../../concepts/ai-services/chat-completion/index.md), ensuring that all functionalities—such as natural language processing, dialogue management, and contextual understanding—are fully supported within the [_Chat Completion Agent_](./chat-completion-agent.md), making it a powerful tool for building conversational interfaces.
+The [`ChatCompletionAgent`](./chat-completion-agent.md) is designed around any _Semantic Kernel_ [AI service](../../concepts/ai-services/chat-completion/index.md), offering a flexible and convenient persona encapsulation that can be seamlessly integrated into a wide range of applications. This agent allows developers to easily bring conversational AI capabilities into their systems without having to deal with complex implementation details. It mirrors the features and patterns found in the underlying [AI service](../../concepts/ai-services/chat-completion/index.md), ensuring that all functionalities—such as natural language processing, dialogue management, and contextual understanding—are fully supported within the [`ChatCompletionAgent`](./chat-completion-agent.md), making it a powerful tool for building conversational interfaces.
 
 #### Related API's: