Learn to build effective Agentic AI systems with Andrew Ng

Building effective agentic AI systems represents a critical shift from single LLM calls to iterative workflows capable of reasoning, tool use, and complex task decomposition. While the term has faced marketing hype, the practical deployment of these systems is growing rapidly, requiring a transition from simple prompt engineering to sophisticated multi-step execution. Success in developing these workflows depends on a disciplined error analysis process guided by evaluation data rather than intuition, allowing developers to identify exactly which components require improvement. By mastering four key design patterns and implementing them in raw Python, developers gain a vendor-neutral understanding of the underlying mechanics of agentic AI. This systematic approach to building and refining autonomous workflows serves as a highly in-demand skill for creating advanced applications in the current job market.