Understanding AI Agents - From Basic Concepts to Multi-Agent Systems

Learn about AI agents in this comprehensive video lecture that progresses from basic concepts to advanced implementations. Explore the fundamental components of AI agents, including their need for Large Language Models (LLMs), memory systems, reasoning capabilities, and planning mechanisms. Discover how to build a simple AI agent using an LLM with basic Python list memory, and advance to understanding unified function calling for tool use across various models like Mistral and LLama. Master the distinctions between single-agent and multi-agent configurations, examining their respective use cases and advantages. Follow along with practical code examples demonstrating tool integration, function calling, and multi-agent systems. Gain insights into advanced concepts like Retrieval-Augmented Generation (RAG), self-improving agents, and the importance of complex training datasets. Experience real-time human-machine interaction as concepts are explained using both Gemini-Pro and GPT-4, with practical demonstrations of how AI agents learn, reason, and execute tasks in their environment.

Syllabus

Two AI will help me to understand AI Agents
First Definition of an AI Agent mem, plan, ..
We build the simplest AI Agent possible
Planning w/ an LLM and Python list as memory
Tool use in AI Agents Code example
Function Calling to enhance tool use capabilities
Decompose complex tasks into Multi-Agent config
Self-improve, self-reflect & self-learn AI Agents
Multiple interconnected self improving AI Agents
Advantages of multi-agent systems
Advanced Definition of an AI Agent
RAG in the Agent Framework
Python Code of simple AI Agent all functions
Complex training data for a complex task
Process knowledge is a blueprint of how to plan
Key concept in multi-agent sys and distributed AI
Code example with multi-agents explained
GPT-4o evaluates my learning w/ Gemini-Pro