uild a strong foundation in AI agent development with this comprehensive course covering the core technologies behind modern intelligent systems. Designed for developers, data professionals, AI enthusiasts, and beginners, this course takes you from fundamental concepts to building production-ready AI applications.

You will start by understanding how Large Language Models (LLMs) work, including tokens, context windows, embeddings, and the mechanisms that enable modern AI systems to generate intelligent responses. The course explains key concepts in an accessible and practical way, making it suitable even for learners with limited AI experience.

Next, you will explore prompt engineering techniques such as zero-shot prompting, few-shot prompting, and chain-of-thought reasoning. You will learn how effective prompting improves AI performance and helps create more reliable agent behavior.

The course also covers vector databases, semantic search, and Retrieval-Augmented Generation (RAG), teaching you how AI systems retrieve and use external knowledge to generate more accurate responses. Practical projects include building search systems and document retrieval applications.

Additionally, you will learn to develop AI workflows using LangChain and LangGraph, enabling the creation of complex agent-based systems. The curriculum concludes with Model Context Protocol (MCP), showing how AI agents can securely connect to external tools, applications, and services.

By the end of the course, you will understand the complete AI agent ecosystem and possess the skills needed to build modern AI-powered applications and autonomous agent workflows.