"This course contains the use of artificial intelligence."

Build AI agents that reason, use tools, look things up and run safely in production.

Most AI agent tutorials stop at a chatbot that calls one function. Real projects need more than that. They need an agent that plans across several steps, talks to other systems through the Model Context Protocol (MCP), pulls the right facts from your own documents, remembers what users told it, and stays safe when someone tries to trick it.

This course takes you through all of it, in a sensible order. You start with how large language models (LLMs) and generative AI really work, then move on to agentic AI: agents that reason, plan and act in a loop. From there you build agents in Python, compare the main frameworks, connect MCP servers and clients, and finish with a capstone project you can add to your portfolio.

What's inside

• AI and LLM fundamentals: tokens, context windows, embeddings and prompt engineering, explained without the hype.

• Agentic AI concepts: reasoning, planning, agent loops and memory, and how they fit together.

• AI agents in Python: calling LLM APIs with the OpenAI Python SDK, writing agent functions, using external tools and fixing common bugs.

• LangChain, LangGraph and the OpenAI Agents SDK: what each framework is for, how to build a workflow in each, and how to choose between them.

• Model Context Protocol (MCP): hosts, clients and servers, tools, resources and prompts, JSON-RPC messages and transports, taught to the current official specification.

• MCP servers and clients: building both with the official Python SDK, and connecting agents to APIs and databases.

• Multi-agent systems: architectures, task delegation, orchestration strategies and human-in-the-loop workflows.

• RAG and agent memory: embeddings, vector databases, semantic search, retrieval pipelines, and short- and long-term memory.

• Security, evaluation and deployment: guardrails, prompt-injection defences, access control, testing, monitoring, Docker, CI/CD and scaling.

• Capstone project: plan and build a production-ready AI assistant that brings everything together.

How the course works

Each lesson is an animated video of 12 to 22 minutes, with clear diagrams, code walkthroughs, flashcard recaps and short knowledge checks that pause so you can think before the answer appears. Every video has accurate English captions, and section quizzes plus a final quiz help the ideas stick.

The MCP and OpenAI material has been checked against the current official documentation, so you won't be learning patterns that stopped working last year.

By the end, you'll understand how modern AI agents are designed, and you'll have a clear plan for building, securing and shipping your own. Start with Lesson 1 and build your first agent this week.

What you'll learn

• Explain how LLMs and generative AI power agentic AI, and design agents that reason, plan and act in loops

• Build AI agents in Python with the OpenAI SDK, tool calling, external APIs and robust error handling

• Orchestrate single and multi-agent workflows with LangChain, LangGraph and the OpenAI Agents SDK

• Build MCP servers and clients with the Model Context Protocol Python SDK and connect agents to real tools

• Add retrieval-augmented generation (RAG) with embeddings, vector databases and long-term agent memory

• Secure, test and deploy production-ready AI agents with guardrails, human approval, monitoring and CI/CD

Requirements

• Basic Python: variables, functions, loops and installing packages with pip

• A Windows, macOS or Linux computer that can run a recent version of Python 3

• An API key from an LLM provider such as OpenAI if you want to run the examples (small usage charges may apply)

• Some familiarity with JSON and web APIs is helpful, but not essential

• No previous experience with AI, LLMs or agent frameworks: Lesson 1 starts from the basics

Who this course is for

• Python developers who want to move from simple chatbots to AI agents that plan, use tools and act

• Software engineers adding LLM features, RAG or MCP integrations to real products

• Data scientists and machine learning engineers moving into generative AI and agentic AI engineering

• Technical leads and architects who need to choose between LangChain, LangGraph and the OpenAI Agents SDK

• Computer science students and career changers building a portfolio project for AI engineering roles

• Not for complete beginners to programming: you'll need basic Python to follow the code

Where this course can take you

The skills in this course map directly onto roles that companies are hiring for right now:

• AI Engineer or Agentic AI Engineer: design and build agents that plan, use tools and act, from first prototype to production.

• LLM Engineer: integrate large language models into real products, with prompt design, RAG pipelines and evaluation.

• AI Integration Developer: connect AI agents to company APIs, databases and internal tools through MCP servers and clients.

• Machine Learning Engineer moving into generative AI: add agent orchestration, vector databases and memory to the skills you already have.

• AI Solutions Architect: choose the right frameworks, design multi-agent systems and plan secure, scalable deployments.

• Backend or Full-Stack Developer with AI skills: add intelligent features, automation and AI-powered search to the products you already build.