Agentic AI Explained: From LLMs to Intelligent Agents

Large language models have revolutionized text generation, but their standalone capabilities often fall short when facing complex, multi-step problems. This course introduces you to agentic AI, the next evolution in building truly intelligent and autonomous systems. By the end of this course, you will understand the fundamental limitations of standalone LLMs and gain the knowledge to design and conceptualize AI agents that can reason, plan, use tools, and interact with dynamic environments. What you'll learn: * Understand the core differences between standalone LLMs and agentic AI architectures * Learn the key components of an AI agent, including memory, planning, and action execution * Apply principles of tool integration to enable agents to interact with external systems and APIs * Practice designing basic agentic workflows for common problem-solving scenarios * Explore strategies for managing agent memory and maintaining context over extended interactions * Grasp the foundational concepts of prompt engineering for agent control * Understand how to evaluate agent performance and identify areas for improvement The course begins with an exploration of LLM capabilities and their inherent limitations, then progresses to introduce the core components and design patterns of agentic AI. You will learn about different agent architectures, how agents can use external tools, manage memory, and engage in multi-step reasoning. This course is designed for beginners with a basic understanding of AI concepts who are eager to explore the next generation of intelligent systems. No prior experience with agentic AI development is required. Begin your journey into building more capable and autonomous AI today.