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What this is about

I made a full series about AI agents because the term is used for almost everything now. Sometimes people mean a chatbot, sometimes a workflow, sometimes a system that can actually use tools and work through several steps.

This page collects the videos in the order that makes the topic easier to understand. Start with the foundation, then tool connections, then context and knowledge, then multi-agent systems. OpenClaw and safety come after that, because agents become serious once they can touch real systems.

The order I would watch them in

  • 1. First define what an agent actually isStart with goals, tools and loops. Without those three parts, it is usually just a chatbot with a better label.
  • 2. Then understand the tool layerMCP and function calling explain how a model can reach data, APIs and actions.
  • 3. Sort out context and knowledge nextRAG and context windows show why more text does not automatically mean better answers.
  • 4. Only then add multiple agentsMulti-agent systems are useful when the task needs division of labour. Not because more agents automatically mean more intelligence.

Relevant videos

Some videos intentionally appear in more than one hub when the context is useful there too.

FAQ

An AI agent is a system that pursues a concrete goal, can use tools and runs in a loop: plan, act, check the result, continue or stop.

A chatbot answers a question. An agent works toward an outcome. The difference is not magic, it is tool access and a defined loop.

When a process has multiple steps, cannot be mapped as a rigid workflow and needs decisions along the way. For simple routine work, normal automation is often better.

OpenClaw shows what happens once an agent gets real tool access. That is exactly when permissions, monitoring and prompt injection start to matter.

All videos

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