AI Engineering: Build Autonomous Agents and Full-Stack AI Apps

The rise of large language models has transformed software development, shifting the focus from writing boilerplate code to orchestrating intelligent systems. To stay ahead, developers must learn how to design, build, and deploy autonomous AI agents and integrate them into full-stack applications. This text-based course guides you through the core paradigms of modern AI engineering. You will transition from understanding basic prompt engineering to designing multi-agent workflows, implementing Retrieval-Augmented Generation (RAG), and leveraging AI-assisted development to build robust, production-ready AI applications. What you will learn: 1. Understand the foundational architecture of Large Language Models (LLMs) and how they power autonomous agents. 2. Design and configure multi-agent systems that can plan, execute tasks, and use external tools. 3. Implement Retrieval-Augmented Generation (RAG) to connect AI agents with external vector databases. 4. Apply modern AI-assisted development workflows to rapidly prototype full-stack applications. 5. Configure evaluation and observability frameworks to monitor and optimize agent performance. 6. Deploy full-stack AI applications safely using modern cloud integration practices. You will start with key terminology and the mechanics of LLMs before moving step-by-step through agent design patterns, database integration, and deployment strategies. Through structured written explanations and detailed code walkthroughs, you will learn how to architect end-to-end intelligent systems. This course is designed for software developers, web engineers, and aspiring AI engineers who want to transition into full-stack AI development. No prior experience with machine learning is required. Start reading today to master the next generation of software engineering.