Summary
Highlights
This first lesson of the 'AI agents for beginners' course introduces AI agents, covering their definition, use cases, and how to build them. The course aims to take learners from concept to code, and this video accompanies a written lesson with translations and a code sample.
The first core component of an AI agent is a Large Language Model (LLM), which provides the reasoning capabilities. Reasoning involves identifying a user's requested task, creating a plan to complete it, and then executing that plan.
Memory is another crucial component. It can be short-term, referring to the context of the current conversation between the user and the agent, or long-term, which is a collection of data that allows the agent to improve its task completion over time.
Tools are the third component, enabling agents to perform actions. These can include different services accessed via APIs, data sources to help determine actions, or functions that send information to the agent.
An AI agent utilizes its LLM to recognize user tasks, identifies necessary available tools, and uses memory to gather information and data required to complete the task. A relatable example is brushing teeth, involving planning, tools (toothbrush/toothpaste), and memory (current status, preferences).
The video demonstrates a code example using Semantic Kernel and GitHub models. The setup involves a Jupyter Notebook and focuses on an agent planning vacation trips. It includes a 'Destinations plugin' that lists available destinations and a function to randomly select a city.
The example simulates user interaction where the agent, using its LLM, interprets natural language requests like 'plan me a day trip'. It intelligently calls the random destinations function. The agent also demonstrates memory by avoiding repeating a suggested destination if the user dislikes it, showcasing its contextual awareness.
The video concludes by reiterating how the agent effectively uses its functions and contextual memory to provide alternative suggestions. The next lesson will delve into different agentic frameworks available.