What are AI agents?

Summary

In this first lesson of the AI Agents for Beginners course, viewers learn about the core components of AI agents: Large Language Models (LLMs), memory, and tools. The lesson explains how these elements work together to help an AI agent recognize tasks, plan actions, and improve over time. A real-world example, such as brushing your teeth, is used to illustrate how an agent combines these components. The lesson also includes a coding example using Semantic Kernel, showing how agents interact with APIs and memory to offer personalized solutions, like suggesting vacation destinations. The lesson sets the stage for deeper exploration in upcoming chapters.

Takeaways

• 😀 AI agents use large language models (LLMs) to understand and perform tasks requested by users.
• 😀 Memory is crucial for AI agents, both short-term (conversation context) and long-term (preferences and learned behaviors).
• 😀 Tools or external APIs allow AI agents to perform specific actions or access data to help complete tasks.
• 😀 An AI agent uses the LLM to identify tasks, select available tools, and leverage memory to carry out user requests.
• 😀 A real-world example of an AI agent: planning a day trip by suggesting a random city from a list of destinations.
• 😀 The agent uses short-term memory to remember the current conversation context (e.g., user requests a day trip).
• 😀 Long-term memory helps the agent recall user preferences, such as favorite toothpaste brands or travel preferences.
• 😀 The agent adapts based on user feedback, ensuring it doesn’t repeat previous suggestions (e.g., suggesting a new vacation destination after rejection).
• 😀 Semantic Kernel and agentic frameworks are used to build AI agents that leverage LLMs and external tools to perform tasks.
• 😀 The code example demonstrates how to integrate an agentic framework, including setting up a function for choosing random vacation destinations.
• 😀 Building AI agents involves combining reasoning, memory, and external functions to create flexible and responsive systems.

Q & A

What are AI agents?

AI agents are intelligent systems designed to perform tasks by interpreting user requests, planning how to execute them, and taking the necessary actions using available tools, memory, and reasoning abilities.

What are the key components of an AI agent?

The key components of an AI agent are: 1) Large Language Model (LLM) for interpreting tasks, 2) Memory for storing short-term and long-term information, and 3) Tools that are APIs or services used to perform actions and gather data.

What does reasoning mean in the context of AI agents?

Reasoning refers to the agent's ability to identify a user-requested task, create a plan to complete it, and perform the required actions based on the available information.

What is the role of memory in AI agents?

Memory in AI agents helps in storing both short-term context of conversations and long-term data. Short-term memory allows the agent to track the ongoing interaction, while long-term memory helps the agent improve its task execution over time.

How do tools function within an AI agent?

Tools within an AI agent are external services or APIs that the agent accesses to perform specific tasks. These tools help the agent make informed decisions and take actions required to fulfill user requests.

Can you provide an example of a real-world task AI agents can perform?

A real-world example is an AI travel agent that helps plan a day trip by suggesting vacation destinations. The agent uses a list of destinations and its memory to tailor suggestions based on the user's preferences and past interactions.

How does the AI agent use its memory to improve its responses?

The AI agent uses memory to remember prior interactions, preferences, and decisions. For example, if a user rejects a suggested vacation destination, the agent can remember this and offer a different suggestion, avoiding repetition.

What is the semantic kernel, and how is it used in AI agent development?

The semantic kernel is a framework from Microsoft and GitHub that helps create AI agents by providing tools to integrate large language models, memory, and APIs. It facilitates the building and customization of AI agents that can handle dynamic tasks and interact naturally with users.

Why is it important for AI agents to use large language models like GPT?

Large language models, such as GPT, are crucial for AI agents because they enable the agent to understand and generate human-like responses in natural language. This allows the agent to effectively interpret user requests and provide relevant, accurate answers.

How does the agent handle a situation where a user rejects a suggestion?

When a user rejects a suggestion, the agent uses its memory to avoid repeating the same suggestion. Instead, it offers an alternative, demonstrating its ability to adapt based on user feedback and ongoing context.