Track 7 · Intermediate → Advanced

From Chatbots to AI Agents

How a model uses tools, works in a loop of thinking and acting, remembers, connects through MCP, works in teams, and stays under your control.

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Modules

Each module explains one idea in plain words, lets you try it, then checks what stuck.

  1. 01

    What Makes an AI Agent Different From a Chatbot?

    Understand what turns a chatbot into an agent: tools, a loop, and the freedom to decide its own next step.

  2. 02

    How AI Can Use Tools and Call Functions

    See exactly how a model “uses” a tool: by writing a request that the app runs on its behalf.

  3. 03

    How an LLM Chooses the Right Tool

    Learn how a model picks between tools, and why clear names and descriptions decide whether it picks well.

  4. 04

    The Agent Loop: Think, Act, Observe, Repeat

    See the loop at the heart of every agent, how it ends, and why limits and error handling matter.

  5. 05

    ReAct: Combining Reasoning With Actions

    See why writing down reasoning between actions makes agents more reliable, and what ReAct looks like.

  6. 06

    How Agents Remember Information

    Learn the kinds of memory an agent can have, and how long-term memory works without retraining anything.

  7. 07

    Managing an Agent's Context

    See why long agent runs overflow their context, and the techniques that keep an agent focused and affordable.

  8. 08

    MCP: A Universal Way to Connect AI to Tools

    Understand the problem MCP solves: connecting many AI apps to many tools without building every connection by hand.

  9. 09

    Inside MCP Clients and Servers

    See the parts of an MCP connection, what a server can offer, and where the human stays in control.

  10. 10

    What Is a Custom AI Agent Made Of?

    See the parts every custom agent is assembled from, and how each one shapes what it does.

  11. 11

    Skills: Giving an Agent Know-How on Demand

    Learn how skills give an agent detailed know-how only when a task needs it, keeping its context lean.

  12. 12

    Multi-Agent Systems: When Multiple AIs Work Together

    See when splitting work across several agents helps, what it costs, and the common ways teams of agents are organised.

  13. 13

    Keeping AI Agents Under Control

    Learn the layers of protection that let agents act usefully without acting dangerously.