In this video, I break down AI agents in the simplest way possible—and then we go deep, fast. My favorite way to think about an AI agent is: it’s like passive income, but for your productivity. While you’re in meetings or focused on higher-leverage work, an agent can quietly clean data, update dashboards, draft follow-ups, and send the email. The big idea is that agents aren’t just chatbots. They’re built on top of LLMs (like GPT‑4, Claude, or Gemini), but what makes them “agents” is autonomy, memory, and tools. I walk you through how agents work behind the scenes using a five-stage workflow: perception, reasoning (often using the ReAct loop), planning, memory, and action. Then I show two ways to build them: (1) no-code/low-code using tools like ChatGPT custom agents (I share my own “SK admin” that checks my inbox/calendar/drive and sends me a daily 7am briefing), and (2) coding your own agent in Python using frameworks/SDKs. I also cover real-world use cases across data science, engineering, marketing, and ops—plus the limitations you need to respect (context drift, overconfidence, and privacy). Finally, I share what’s next: AI browsers and multi-agent systems, and why scaling agents inside real organizations is the real challenge.