Introduction to Agentic AI: Designing Autonomous Workflows

Traditional AI models simply respond to prompts, but agentic AI represents a massive shift toward autonomous systems that can plan, use tools, and execute complex tasks independently. This course introduces you to the core principles of designing, structuring, and implementing self-directing AI agents. You will transition from writing simple prompts to understanding how multi-agent systems coordinate to solve real-world problems. Through clear explanations and written step-by-step design exercises, you will gain a strong conceptual and practical foundation in how autonomous systems operate, make decisions, and scale. What you'll learn: - Understand the fundamental terminology, architectures, and planning loops that define agentic systems. - Explore how AI agents leverage tool calling and APIs to interact with external databases and services. - Learn how agents manage state, memory, and Retrieval-Augmented Generation (RAG) to maintain context. - Analyze multi-agent orchestration patterns to coordinate specialized agents toward a shared goal. - Design robust error-handling and human-in-the-loop validation strategies for reliable autonomous workflows. - Practice structuring agent-based solutions for common business and development challenges. You will start with the absolute basics, defining what makes an AI system "agentic" versus "passive," before moving step-by-step into core architectures, memory systems, and multi-agent collaboration patterns. This course is designed for software developers, product managers, and technology enthusiasts who are new to autonomous AI systems and want a solid foundation without needing advanced machine learning prerequisites. Start reading today to unlock the potential of autonomous AI orchestration.