AI Agents: From Foundations to Applications

This three-course specialization takes AI practitioners, developers, and researchers through the full lifecycle of AI agent development — from understanding intelligent agent theory to implementing and deploying autonomous, learning-driven systems. You will start by mastering core agent principles including perception, reasoning, action, decision-making, and planning across reactive, goal-based, and learning agent architectures, with hands-on implementation in Python. As you progress, you will build autonomous agents using reinforcement learning, exploring exploration vs. exploitation strategies, reward shaping, and policy optimization through Q-Learning, DQN, and policy gradient methods. The final course brings everything together by guiding you through designing task-oriented and conversational AI agents using LLMs, integrating reasoning, memory, and tool use with LangChain and OpenAI APIs, and orchestrating multi-agent collaborative workflows. By the end, you will be able to design, train, and deploy AI agents capable of reasoning, planning, and collaborating with humans and other agents across various domains.