AI Agents, Automation: Build Your Own AI Workflows

About this course

This course contains the use of artificial intelligence.

You have been using AI for a year. It answers well. It has never once done anything while you were away from the keyboard.

Where the gap isBetween writing a good prompt and having something that runs on its own there are four steps, and almost everyone stops at the first. You get a useful answer, paste it somewhere, and do the same thing again next week. The vocabulary does not help: agent, workflow, MCP, skill, plugin and orchestration all get used interchangeably by people selling things, so it is genuinely unclear what you are supposed to build, in what order, or whether any of it needs a developer. It does not. But it does need the four steps in sequence, and the gap between them is where the time goes.

What this course covers

Thirty-three lessons through all four. The prompt as a construction first: seven blocks including the validation block most people skip, four classic frameworks, chain-of-thought and self-consistency, tree-of-thoughts for multi-criteria decisions, ReAct where the model chooses its own steps, then the shift to context engineering — projects, custom instructions, retrieval without a developer, and memory across sessions. Then what agents currently do: the components of one, the platforms, real products across six categories with their limitations stated, and building one from scratch. Then the platform layer in depth: where projects, skills, plugins and connectors sit, writing SKILL file, adapting official commands, and packing everything you built into a single distributable plugin with a manifest. Then production: auditing your own week to find the leverage, the augment-automate-architect cycle, data boundaries, building a skill live, connector security, scheduled tasks, cross-application workflows, Computer Use, and proving the time saved. Finally code-level agents: browser work, working across your applications, report agents, unattended automations wit

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