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description: "TODO this field is for search engine optimization and is not user-visible; use 2-3 complete, grammatically correct sentences to describe the unit; include relevant search keywords."
### If your content is related to a product or service, apply one value from the either the ms.prod allowlist
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### or the ms.service allowlist. You can’t use both ms.prod and ms.service.
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### If your content isn't about a product or service, you can omit both ms.prod and ms.service.
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###
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### The list of approved ms.prod values is here: https://review.learn.microsoft.com/help/platform/metadata-taxonomies?branch=main#msprod
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### The list of approved ms.service values is here: https://review.learn.microsoft.com/help/platform/metadata-taxonomies?branch=main#msservice
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### If you need to request new values, follow the process here: https://review.learn.microsoft.com/en-us/help/platform/metadata-allowlist-requests?branch=main
AI agents offer a powerful combination of technologies, able to complete tasks with the use of generative AI. However, in some situations, the task required might be larger than is realistic for a single agent. For those scenarios, consider a **multi-agent** solution. A multi-agent solution allows agents to collaborate within the same conversation.
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In this module, you'll learn how to use the Semantic Kernel SDK to develop your own AI agents that can collaborate for a multi-agent solution.
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Imagine you are trying to address common DevOps challenges such as monitoring application performance, identifying issues, and deploying fixes. A multi-agent system could consist of four specialized agents working collaboratively:
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1. The Monitoring Agent continuously ingests logs and metrics, detects anomalies using natural language processing (NLP), and triggers alerts when issues arise.
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1. The Root Cause Analysis Agent then correlates these anomalies with recent system changes, leveraging machine learning models or predefined rules to pinpoint the root cause of the problem.
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1. Once the root cause is identified, the Automated Deployment Agent takes over to implement fixes or roll back problematic changes by interacting with CI/CD pipelines and executing deployment scripts.
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1. Finally, the Reporting Agent generates detailed reports summarizing the anomalies, root causes, and resolutions, and notifies stakeholders via email or other communication channels.
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This modular, scalable, and intelligent multi-agent system streamlines the DevOps process, reducing manual intervention and improving efficiency while ensuring timely communication and resolution of issues.
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In this module, we will explore how to leverage the powerful capabilities of Semantic Kernel to design, implement, and orchestrate intelligent agents that work collaboratively to solve complex problems.
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In this module, you'll learn how to use the Semantic Kernel Agents Framework to develop your own AI agents that can collaborate for a multi-agent solution.
Semantic Kernel is an open-source SDK that enables developers to integrate AI models into their applications. Using the SDK, developers can create "plugins" to interface with the LLMs and perform various tasks. Semantic Kernel bridges the gap between AI and software development by offering tools to create AI-driven agents that can execute tasks, interact with APIs, and perform complex workflows.
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Semantic Kernel is an open-source SDK that enables developers to integrate AI models into their applications. Part of that SDK is the *Semantic Kernel Agents Framework*, enabling the creation of agents using the same features that exist in the core Semantic Kernel framework.
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## What is the Semantic Kernel Agent Framework?
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@@ -11,35 +11,31 @@ The Semantic Kernel Agent Framework is a framework designed to help developers b
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The kernel is the central component of the Semantic Kernel. The kernel acts as the execution engine, managing AI interactions, function orchestration, and memory.
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-**Agents**
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Agents are intelligent, AI-driven entities capable of reasoning and executing tasks. They use language models, functions, and memory to make decisions dynamically.
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-**Plugins**
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-**Tools and Plugins**
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Agents use plugins to perform specific tasks. Plugin functions enable agents to dynamically interact with external services or execute complex tasks.
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Agents use tools and plugins to perform specific tasks. Tools provide additional functionality to your agents, such as file searching or code interpreter. Plugins enable agents to dynamically interact with external services or execute complex tasks through function calling.
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-**History**
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Agents can maintain chat history across multiple interactions, allowing them to track previous interactions and adapt responses accordingly.
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-**Agent Collaboration**
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Agents can collaborate together through an **agent group chat**, which enables multiple agents to join the same chat. Agent group chats determine which agent should respond and how to determine if the conversation is finished.
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### Types of Agents
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The Semantic Kernel Agent Framework supports several different types of agents.
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The Semantic Kernel Agent Framework supports several different types of agents, including:
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- Chat Completion Agent
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- OpenAI Assistant Agent
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- Azure AI Agent
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## Why Use the Semantic Kernel Agent Framework?
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TODO
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### Getting Started
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TODO - Create AI Foundry Project
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## Why you should use the Semantic Kernel Agent Framework
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To use the Semantic Kernel Agent Framework, install the necessary package with dependencies:
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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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```sh
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pip install semantic-kernel[azure]
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```
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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.
Now it's your opportunity to build a multi agent solution with the Semantic Kerenl SDK. In this exercise, you'll create an application that automatically triages and resolves issues presented in log files of a system. Using Azure AI Agents, you'll create an incident manager agent an a devops agent that will collaborate to fix the issues.
Now it's your opportunity to build a multi agent solution with the Semantic Kernel Agent Framework. In this exercise, you'll create an application that automatically triages and resolves issues presented in log files of a system. Using Azure AI Agents, you'll create an incident manager agent an a devops agent that will collaborate to fix the issues.
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Launch the exercise and follow the instructions.
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[](https://go.microsoft.com/fwlink/?linkid=2305092&azure-portal=true)
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[](https://go.microsoft.com/fwlink/?linkid=2310729&azure-portal=true)
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> [!TIP]
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> After completing the exercise, if you're finished exploring Azure AI Agents, delete the Azure resources that you created during the exercise.
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> After completing the exercise, if you're finished exploring multi-agents with Semantic Kernel Agent Framework, delete the Azure resources that you created during the exercise.
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