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Copy file name to clipboardExpand all lines: learn-pr/wwl-data-ai/orchestrate-sk-multi-agent-solution/includes/2-understand-agent-framework.md
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The Semantic Kernel Agent Framework supports several different types of agents, including:
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- Azure AI Agent
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- Chat Completion Agent
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- OpenAI Assistant Agent
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-**Azure AI Agent** - a specialized agent within the Semantic Kernel Agent Framework. The `AsureAIAgent` type is designed to provide advanced conversational capabilities with seamless tool integration. It automates tool calling and securely manages conversation history using threads, reducing the overhead of maintaining state. The `AzureAIAgent` also supports a variety of built-in tools, including file retrieval, code execution, and data interaction via Bing, Azure AI Search, Azure Functions, and OpenAPI.
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## Why you should use the Semantic Kernel Agent Framework
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-**Chat Completion Agent**: designed for chat completion and conversation interfaces. The `ChatCompletionAgent` type mirrors the features and patterns in the underlying AI Service to support natural language processing, contextual understanding, and dialogue management.
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-**OpenAI Assistant Agent**: designed for more advanced capabilities and multi-step tasks. The `OpenAIAssistantAgent` type supports goal-driven interactions with additional features like code interpretation and file search.
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The Semantic Kernel Agent Framework offers a robust platform for building intelligent, autonomous, and collaborative AI agents. By leveraging this framework, you can create modular AI components that seamlessly integrate into your applications, enabling them to perform complex tasks with minimal manual intervention. The framework's design emphasizes flexibility, allowing developers to define agents tailored to specific needs, such as data analysis, API interactions, or natural language processing. This modularity ensures that your application remains adaptable as requirements evolve or new technologies emerge.
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## Why you should use the Semantic Kernel Agent Framework
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The Semantic Kernel Agent Framework can integrate agents from multiple sources, including Azure AI Agent Service, and supports both multi-agent collaboration and human-agent interaction. Agents can work together to orchestrate sophisticated workflows, where each agent specializes in a specific task, such as data collection, analysis, or decision-making. Additionally, the framework facilitates human-in-the-loop processes, enabling agents to augment human decision-making by providing insights or automating repetitive tasks. This combination of autonomy, collaboration, and interactivity makes the Semantic Kernel Agent Framework an ideal choice for applications requiring dynamic, goal-oriented behavior.
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The Semantic Kernel Agent Framework offers a robust platform for building intelligent, autonomous, and collaborative AI agents. The framework can integrate agents from multiple sources, including Azure AI Agent Service, and supports both multi-agent collaboration and human-agent interaction. Agents can work together to orchestrate sophisticated workflows, where each agent specializes in a specific task, such as data collection, analysis, or decision-making. The framework also facilitates human-in-the-loop processes, enabling agents to augment human decision-making by providing insights or automating repetitive tasks. This combination of autonomy, collaboration, and interactivity makes the Semantic Kernel Agent Framework an ideal choice for applications requiring dynamic, goal-oriented behavior.
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A key part of the Semantic Kernel Agent Framework is a system designed to facilitate intelligent, multi-agent interactions. Agent collaboration, called `AgentGroupChat`, has critical components to consider that aren't necessary with single agents or non-agentic Semantic Kernel applications.
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Each of these sections discusses an example multi-agent solution, where we have two agents:
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The following units discuss an example multi-agent solution, where we have two agents in a writer-reviewer scenario:
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- A copywriter agent who writes online content, called _CopywriterAgent_.
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- A creative director only reviewing the proposals, called _ReviewingDirectorAgent_.
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Multi-turn conversations have responses returned asynchronously, so the conversation can develop naturally. However, the agents need to know when to stop a conversation, which is determined by the **termination strategy**
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This unit uses the example multi-agent solution where we have two agents:
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- A copywriter agent who writes online content, called _CopywriterAgent_.
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- A creative director only reviewing the proposals, called _ReviewingDirectorAgent_.
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## Termination strategy
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A termination strategy ensures that conversations or tasks conclude appropriately. This strategy prevents unnecessary messages to the user, limits resource usage, and improves the overall user experience.
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For example, once the _ReviewingDirectorAgent_ reviews and approves our scrubbing brush slogan from the _CopywriterAgent_, us humans know the conversation should be over. However, if we don't define when to terminate the conversation, the _CopywriterAgent_ is going to keep submitting slogans unnecessarily.
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For example, in the writer-reviewer agent scenario, once the _ReviewingDirectorAgent_ reviews and approves our scrubbing brush slogan from the _CopywriterAgent_, us humans know the conversation should be over. However, if we don't define when to terminate the conversation, the _CopywriterAgent_ is going to keep submitting slogans unnecessarily.
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