AI Agent Fundamentals: Building with LLMs and Essential Components

Large language models are powerful, but building truly intelligent systems requires more than just prompting. This course introduces the foundational concepts for creating autonomous AI agents. By the end of this course, you will grasp the architectural principles behind AI agents and be equipped to design systems that leverage LLMs for complex tasks, incorporating external knowledge and dynamic interactions. What you'll learn: Understand the fundamental architecture and lifecycle of AI agents. Learn to integrate large language models as the reasoning core of agents. Apply retrieval augmented generation (RAG) patterns to enable agents to access external knowledge. Configure agents with external tools to perform actions and interact with environments. Implement memory mechanisms for agents to retain context and learn over time. Practice basic prompt engineering strategies for guiding agent behavior and decision-making. Explore foundational patterns for orchestrating multi-step agentic workflows. The course begins with core definitions and the anatomy of an agent, then systematically explores how to imbue agents with memory, external tool use, and access to dynamic information through practical examples. This course is designed for beginners with a basic understanding of programming concepts and an interest in artificial intelligence, with no prior experience in AI agent development required. Start your journey into building intelligent AI agents today.