Implementing GenAI Agents

Generative AI is shifting from passive chat interfaces to active, autonomous agents capable of executing complex workflows. If you want to build systems that don't just answer questions but actually perform tasks, understanding agentic architecture is the essential next step. This text-based course takes you from the core concepts of agent design to implementing fully functional decision-making systems. You will learn how to structure reasoning loops, manage state, and connect large language models to external data and APIs. Through clear written explanations and practical code walkthroughs, you will develop the skills to build reliable, goal-driven AI systems. What you'll learn: - Understand the core architecture of GenAI agents, including planning, memory, and execution frameworks - Implement function calling to let your agents safely interact with external APIs and databases - Design robust reasoning loops using industry-standard patterns like ReAct (Reason and Act) - Integrate Retrieval-Augmented Generation (RAG) to ground your agent's decisions in private data sources - Apply modern observability concepts to debug, monitor, and evaluate agent behavior The course begins with fundamental agent terminology and architectural patterns before guiding you through step-by-step code implementations of memory, tool usage, and multi-step planning. It is designed for software developers, data analysts, and tech-savvy builders looking to transition from basic prompting to advanced agentic engineering. No prior experience with AI agents is required. Start reading today to master the foundations of autonomous AI engineering.