4-Week AI Agents & Agentic Workflows Certification
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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.
No prior experience with AI agents or agentic workflows is required
Basic familiarity with using a computer and browsing the internet is helpful
Beginner-level understanding of AI tools like ChatGPT is useful, but not required
Basic Python knowledge is helpful for hands-on labs, but the course explains concepts step by step
No advanced machine learning, data science, or deep learning background is required
No prior experience with RAG, vector databases, embeddings, or multi-agent systems is needed
A laptop or desktop computer with internet access is recommended
Willingness to follow hands-on exercises and build practical AI projects
Curiosity about how modern AI agents, tools, memory, and automation workflows work
This course is designed to lower the barrier for beginners while still helping learners build portfolio-ready AI agent systems
Understand the difference between basic LLM prompting and real AI agent systems
Explain the core components of an AI agent, including input, reasoning, action, observation, and output
Build a working single-agent system using the Think → Act → Observe agent loop
Connect AI agents to tools, APIs, functions, and external systems to complete real tasks
Use memory to create stateful agents that can store and reuse information across interactions
Understand embeddings, vector databases, and retrieval-augmented generation at a practical level
Build a RAG-powered agent that can retrieve external knowledge and generate more accurate responses
Design and build multi-agent workflows using roles such as Planner, Executor, Reviewer, and Manager
Understand how agents communicate, coordinate tasks, and pass context through a workflow
Add basic guardrails, validation, logging, debugging, and reliability checks to agent systems
Complete a portfolio-ready capstone project that combines tools, memory, RAG, and agentic workflows
Beginners who want to understand AI agents beyond basic prompting
Professionals who want to learn how agentic workflows are designed and used in real-world systems
Developers who want to build practical single-agent, RAG, and multi-agent systems
AI enthusiasts who want hands-on experience with tools, memory, retrieval, and automation
Business and technology professionals who want to understand how AI agents can improve productivity and workflows
Students and career changers who want to build portfolio-ready AI projects in the growing field of agentic AI
Product managers, analysts, consultants, and team leads who want to understand how AI agents can support business processes
Anyone interested in learning how modern AI systems can reason, act, use tools, retrieve knowledge, and coordinate tasks
Learners who want a structured, beginner-friendly path from AI agent fundamentals to a final capstone project
