Agentic AI
Build AI agents that plan, use tools, and complete multi-step tasks on their own.
Course overview
A chatbot answers one question at a time. An AI agent is given a goal and works towards it: it plans the steps, searches the web, reads files, calls APIs, checks its own work, and keeps going until the job is done. Agentic AI is the next big shift after Generative AI, and companies everywhere are hiring people who can build and manage agents.
This course explains how agents work — the reasoning loop, tool use, memory, and planning — and then teaches you to build them. You'll connect a large language model to tools, give it memory with a vector database, coordinate several agents as a team, and use no-code tools like n8n to automate real business workflows.
By the end, you'll be able to design an agent for a real task, build it with frameworks like LangChain or CrewAI or with no-code tools, add the guardrails that keep it safe, and judge when an agent is the right solution and when a simple chatbot is enough.
Learning objectives
By the end of this course, you'll be able to:
- Explain the difference between a chatbot, an AI assistant, and an AI agent
- Describe the agent loop: perceive, reason, act, and observe
- Connect a large language model to tools and APIs using function calling
- Give an agent memory with conversation history and a vector database (RAG)
- Build multi-agent systems where agents with different roles work together
- Automate business workflows with no-code agent tools such as n8n
- Connect agents to apps and data through the Model Context Protocol (MCP)
- Add guardrails, human approval steps, and testing to keep agents safe and reliable
Complete syllabus
0 / 10 modules complete
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1. From Generative AI to Agentic AI
What makes an AI system an agent, how agents differ from chatbots, and where they're used today.
2. How Agents Think: The Reasoning Loop
The perceive-reason-act cycle, the ReAct pattern, planning, and self-reflection.
3. Tool Use and Function Calling
Letting a language model search the web, run code, read files, and call APIs to get real work done.
4. Memory and Knowledge: RAG and Vector Databases
Short-term and long-term memory, embeddings, and retrieval-augmented generation (RAG) so agents can use your own documents.
5. Building Agents with Python Frameworks
Building your first agent step by step with LangChain and LangGraph.
6. Multi-Agent Systems
Teams of agents with roles — researcher, writer, reviewer — coordinated with CrewAI and similar frameworks.
7. No-Code Agents and Workflow Automation
Building agents without code in n8n and similar tools to handle emails, leads, reports, and support.
8. Connecting Agents to the World: MCP and APIs
The Model Context Protocol (MCP), connecting agents to apps like Google Drive, Slack, and databases, and coding agents like Claude Code.
9. Safety, Guardrails, and Evaluation
Human-in-the-loop approval, limiting what an agent may do, prompt-injection risks, cost control, and testing agent output.
10. Practical Projects and Final Assessment
Build and demo your own working agent, then take the final quiz.
Required skills & prerequisites
- Comfortable using ChatGPT, Claude, or another AI chatbot
- Basic Python helps for the framework modules — the no-code modules need none
- Our Generative AI course is a helpful starting point but not required
- A laptop with an internet connection
Tools & technologies
Practical projects
Research agent
Build an agent that searches the web on a topic, reads the top sources, and writes a short report with links.
Chat with your documents
Build a RAG agent that answers questions from a set of PDFs, such as a course syllabus or a company handbook.
No-code email assistant
Use n8n to build an agent that sorts incoming emails, drafts replies, and waits for your approval before sending.
Capstone: multi-agent team
Build a small team of agents that plans, writes, and reviews a piece of work together — such as a lesson plan or a marketing post.
Instructor
AI Tutor Spark
Course design & AI tutoring
This course was built by the team at AI Tutor Spark, behind its step-by-step tutoring approach. Every module is paired with an AI tutor you can question directly — ask it to explain an agent pattern again, or help you debug your own agent.
Frequently asked questions
Basic Python helps for the framework modules, but the no-code modules with n8n need no programming at all, so you can build working agents either way.
Generative AI teaches how AI creates content from a prompt. Agentic AI teaches how to make AI take actions — plan, use tools, and finish multi-step tasks on its own.
Not to complete the course. Groq and several other providers have free tiers, and the projects are small enough to stay within them.
Yes — every module links to the AI Tutor Spark AI tutor chat, so you can ask follow-up questions any time.
Final quiz
Score 70% or higher to pass. You can retake the quiz as many times as you like.
1. What best describes an AI agent?
2. Function calling lets a language model:
3. RAG (retrieval-augmented generation) helps an agent by:
4. In the ReAct pattern, the agent repeatedly:
5. Why add a human approval step before an agent sends an email or makes a payment?
6. MCP (Model Context Protocol) is used to:
Ready to start Agentic AI?
Free to start. Enroll, then jump straight into module one.