I wrote this quick response to explain Agentic AI to a friend. Posting it here since I’ll probably end up linking to it a lot—and because, let’s be honest, it’s been ages since I’ve posted anything. For extra fun, I took my first draft and asked ChatGPT to add punch. And repeated that step a few more times. You can see the evolution here

Imagine chatting with a college student about their major. You ask a question—they fire back instantly. Confident. Usually right. Sometimes dead wrong. Ask them to solve something tricky? They’ll blurt out the first idea that pops into their head.

That’s what it’s like using a basic LLM like ChatGPT. No planning. No second-guessing. Just speed and surface-level smarts. They’ll charge ahead on whatever half-baked “fact” comes to mind.

Now enter Agentic AI—a power suit for LLMs. Suddenly your chatty student has tools. Real ones. They can search, calculate, buy stuff, run scripts, plan, test, backtrack, and try again. It’s no longer just blurting answers—it’s reasoning, experimenting, adjusting. It’s not just talking—it’s doing.

The student’s out of the dorm and into the workplace. They’ve got a desk, a deadline, and a job to do. And they work 1000x faster, no coffee, no distractions.

They’ll still mess up. Misread instructions. Miss the goal. Just like any new hire. But now they’re not just throwing out ideas—they can execute.

Before, you had a keyboard warrior: fast with replies, full of opinions, but stuck in the comment section. Now? You’ve got an activist. Still figuring things out, still a little reckless—but out in the world, making moves, trying things, taking action.

What they still can’t do

  • Invent meaningful goals from scratch (and maybe they never will)
  • Turn vague projects into step-by-step action plans

They still need a manager. A map. Someone to say, “Here’s what matters. Here’s where to start.”

The big frontier? Teaching AI to handle abstract, messy, open-ended problems.

That’s what researchers—and the people writing the checks—are racing toward.