What the Heck is Agentic?

It’s everywhere.

Job postings. Tech articles. LinkedIn thought leaders are doing their thing. Every other sentence has “agentic AI” in it, like everyone already knows what it means, and you’re the only one in the room who missed the memo.

I was definitely the one who missed the memo.

So I just… asked Claude to explain it. Not the research paper version. The “I’m an engineer who needs to actually understand and use this” version.

Here’s what clicked for me.

The Difference Nobody Explains Clearly

Normal AI interaction looks like this:

You ask → AI answers → done.

That’s it. You’re driving every step. The AI is a very smart response machine.

Agentic AI looks like this:

You give a goal → AI figures out the steps → takes action → evaluates the result → takes the next action → evaluates again → keeps going until it’s done.

The keyword is autonomously. It’s making its own decisions about what to do next instead of waiting for you to guide every single move.

The Example That Made It Click

Here’s the contrast that landed for me:

Non-agentic: “Search for Python jobs in Sacramento” → returns results, done.

Agentic: “Research the Sacramento Python job market and tell me what skills are most in demand.” → searches, reads results, decides it needs more data, searches again from a different angle, cross-references findings, synthesizes everything, then gives you a report.

Same general topic. Completely different relationship between you and the tool.

One you’re operating like a calculator — punch in exactly what you want computed, get the answer back. The other is more like handing a task to a capable intern and saying, “Figure it out and bring me something useful.”

You tell the calculator exactly what to compute. You tell the intern what outcome you want, and they figure out how to get there.

Why This Actually Matters

I’ll be honest — when I first read “agentic AI experience preferred” on job postings, I filed it away as buzzword noise. The kind of thing recruiters copy-paste without knowing what it means.

But it’s not noise. It’s pointing at something real.

The engineers who are going to be most valuable in the next few years aren’t just the ones who know how to prompt an AI. They’re the ones who know how to build systems where AI can operate with some degree of autonomy — calling tools, making decisions, looping until a goal is reached.

That’s a fundamentally different kind of engineering than what most of us learned. And it’s moving fast.

Where I’m Landing With This

For me specifically — coming from 7 years of building internal workflow automation tools at CalPERS — the agentic model actually maps onto something familiar. Automated workflows that chain steps together, evaluate conditions, and keep moving without a human approving every action. That’s not a foreign concept.

What’s new is that the decision-making inside those steps is now… surprisingly capable. And that changes what’s possible pretty significantly.

I’m still early in figuring out how to build with this rather than just understanding it conceptually. But that’s the next chapter.