AI Agents Explained: Fundamentals, Architecture, RAG & MCP

About this course

This course contains the use of artificial intelligence.

AI agents are everywhere in the news, but most explanations are either too vague or too technical. This course gives you a clear, visual, step-by-step understanding of how AI agents really work, how they differ from the chatbots you already use, and how modern agent systems are designed and used in real applications. Then you build one yourself.

The first nine sections need no coding. Every idea is explained with animated diagrams and plain language, then tied to practical case studies. In the final section, a hands-on project, you build a working AI agent in Python one small step at a time, with all code files provided in the course resources.

What you will explore:

• How large language models work, how they generate text, and where they fail
• What turns an LLM into an agent: goals, reasoning, tools, and memory
• The agent loop: reason, act, observe, and decide whether to stop or try again
• Tool calling and external APIs, with short-term and long-term memory
• Retrieval-Augmented Generation (RAG) and vector databases
• The Model Context Protocol (MCP) and how it connects agents to tools
• Workflows vs autonomous agents vs multi-agent systems
• Security, reliability, and production safety challenges

Hands-on project: build your own AI agent (Python)Step by step: a plain LLM call, tool calling, the agent loop, memory, RAG with a Chroma vector database, an MCP server and client, and safety guardrails

Runs offline with a mock model, free with a local model (Ollama), or with any OpenAI-compatible APIPinned requirements and offline tests, so the code works on your machine

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