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. Read more