This course contains the use of artificial intelligence.

The 4-Week AI Agents & Agentic Workflows Certification is a hands-on, practical program designed to help you move beyond basic prompting and learn how to build real AI agent systems that can reason, take action, use tools, remember information, retrieve knowledge, and coordinate with other agents.

Most people use AI by typing prompts into a chatbot. But modern AI development is quickly moving toward agentic systems — AI-powered workflows that can break down tasks, make decisions, call external tools, use APIs, search knowledge bases, and complete multi-step processes. This course teaches you how those systems work and how to design them from the ground up.

In Week 1, you will begin with the fundamentals of AI agents. You will learn the difference between simple LLM usage and a true agent system. You will explore the core anatomy of an agent, including input, reasoning, action, and output. You will also learn the popular Think → Act → Observe loop and understand how the ReAct pattern helps agents work through tasks step by step. By the end of the week, you will design and build your first working single-agent system.

In Week 2, you will expand your agent with tools, memory, and RAG. You will learn why memory matters, how stateless agents differ from stateful agents, and how short-term and long-term memory improve agent behavior. You will also understand the basics of embeddings, vector databases, and vector search. Then you will learn how Retrieval-Augmented Generation helps agents produce more accurate, grounded, and context-aware responses. The weekly lab guides you through building a working RAG agent that can use external knowledge.

In Week 3, you will move into multi-agent systems. You will learn when one agent is not enough and how multiple agents can work together through specialized roles such as Planner, Executor, Reviewer, and Manager–Worker patterns. You will explore agent communication, workflow coordination, orchestration tools like LangGraph, CrewAI, and AutoGen, and how to design systems that pass context between agents reliably. The weekly lab focuses on building a coordinated multi-agent workflow.

In Week 4, you will bring everything together in a portfolio-ready capstone project. You will plan your architecture, build the core agent system, integrate tools, add memory, apply guardrails, validate outputs, and improve reliability. You will also learn the basics of observability, testing, debugging, performance optimization, and production thinking.

By the end of this certification, you will have built practical agent systems and gained a clear understanding of how to design agentic workflows for real-world use cases across business, productivity, automation, research, operations, and enterprise AI.