How to build your first AI agent - Autogen Tutorial

It's time to learn how to build your first AI agent in AutoGen Studio. In this video, we will cover the key components of an AI agent, the kinds of agents that we can build, and the steps involved. This will be theoretical and our next video will focus on practical application. Before we dive into the creation steps, let's quickly break down the key components that make up an AI agent in AutoGen Studio. Skills. These are the Python functions that define what an agent can do. For example, skill may generate text, fetch data, or even perform calculations. It's important to craft skills that are reusable and flexible for different tasks. Models. The second key component is the large language model that the agent will use to interpret and generate text or other media. This can be an OpenAI model like GPT-4 or a locally hosted model. Then agents. Finally, an agent is where you bring everything together. Agents are the personas that you equip with the skills and the models to perform specific tasks. Each agent is customizable from its system message and how it behaves in different conversations. AutoGen Studio provides several types of agents designed to handle specific tasks in multi-agent systems. Let's break them down. First, we have the conversable agent. This is the parent of all agents, which provides them the common functionality. We will not be using this one. Then we have the user proxy agent. This acts, as the name says, like a bridge between you, the user, and the system. It communicates your instructions to the other agents and can even execute code on your behalf. Next is the assistant agent. This is the main workhorse. It handles the core tasks like generating content, executing plans, or providing suggestions. You can customize it with different skills and models to get the job done autonomously. And finally, we have the group chat agent. This is also referred as group chat manager. This agent is designed for more complex collaboration, bringing multiple agents together. But basically, its main task is to manage a group chat composed of different agents and letting each agent what has to do and what turn he has to take. It's mainly a coordination task. Each of these agents plays a unique role, helping you to create powerful multi-agent systems easily in AutoGen Studio. Now let's talk about the steps to create a simple agent and run a conversation in AutoGen Studio. The process begins with creating skills where we can create individual skills for our agents, akin to functions like retrieving data or generating content. This step is optional as agents can run without the skill. Next is configuring a model. Here we will pick a large language model for being used as the agent's brain, such as GPT-4o, for example. With skills and a model, we can proceed to building the agent, which involves combining these components, giving the agent a set of tasks and a foundational understanding of its role. Also called instructions or meta prompt. Next, to use the agent, we should assign it to a workflow which sets the framework for interactions where they end up in a collaborative environment that we can execute. Finally, we should try the workflow in the playground where we can observe the agent's behavior and responses within this setup called workflow. We can also think of the workflow like a sandbox to view and refine it. With these, you already know the fundamentals of AutoGen Studio and are completely ready for the next step, creating an agent workflow.