Artificial intelligence is rapidly evolving beyond simple chatbots and single prompt systems into autonomous AI agents capable of reasoning, planning, using tools, and collaborating with other systems to solve complex problems. This course, Agentic AI Engineering, is designed to help you understand and build these next-generation AI systems. You will learn how modern Agentic AI architectures work and how developers are building intelligent agents that can perform tasks independently, interact with external tools, retrieve knowledge, and execute multi-step workflows.
The course begins with the foundations of Agentic AI, exploring how AI has evolved from rule-based automation and traditional machine learning pipelines to intelligent agent-based systems. You will understand what defines an AI agent, the key components that power them, and how modern LLM-driven reasoning engines enable autonomous decision making. From there, we dive into the core technologies behind agent systems, including Large Language Models (LLMs), transformer architectures, tokenization, embeddings, and context windows. You will also learn how to design effective prompt engineering strategies specifically for AI agents, including system prompts, structured prompts, and chain-of-thought reasoning.
As the course progresses, you will learn how agents interact with the outside world using tool calling, function execution APIs, and structured outputs. You will build systems that integrate with external tools, databases, and APIs while enabling agents to execute real tasks. The course also introduces Retrieval-Augmented Generation (RAG), where agents retrieve knowledge from vector databases such as FAISS, Pinecone, and Weaviate. You will learn how embedding pipelines, context injection, and knowledge retrieval allow AI agents to work with large knowledge bases and dynamic data sources.
A major focus of the course is building real agent workflows using frameworks such as LangChain and LangGraph. You will explore how modern agent architectures like ReAct, Plan-and-Execute, and Planner-Executor patterns enable agents to break down complex tasks and execute them step by step. The course provides a deep dive into LangGraph, which enables developers to create graph-based agent workflows, manage stateful agents, and design deterministic execution pipelines. You will learn how nodes, edges, and state management allow developers to build structured and reliable AI systems while avoiding common issues like prompt instability or uncontrolled agent loops.
