Who Put the “Agent” in Agentic? Clarifying the Confusion Behind Modern AI Models
From Chatbots to AI Agents: Demystifying the Evolution of Artificial Intelligence
I recently attended a tech conference and realized something: the most whispered question in the room wasn’t “what’s next” or “who’s launching when”—it was “wait… what actually is an agent?” And, fair. It’s a mess.
If you’re a Silicon Valley native, your internal definitions might be just as fuzzy as a Midwest 5th grader’s. Learning how to use this tech is already a lift. Trying to walk your mom through why her prompting sucks when she’s trying to tweak her peach cobbler recipe? Another level. So let’s walk it back… all the way back to the 1950s.
In 1955, computer scientist John McCarthy proposed something wild: that machines might someday replicate the way humans learn and reason. This was the birthplace of both the term artificial intelligence and the first recorded use of the word agent in a tech context.
We’re starting there.
Let’s unpack this in order of easiest to explain to most eye-crossing.
Chatbots: The ones we all know. Talk, respond. Talk, respond. They simulate conversation, usually over the internet. The first of their kind? ELIZA in 1966, which mimicked a Rogerian therapist—and weirdly, kind of fooled people.
LLMs (Large Language Models): These are trained on absurd amounts of text data (like, billions of terabytes) to produce human-like language. Think of them as ultra-advanced parrots: they mimic language, answer questions, and do it convincingly, but they’re not “thinking” in a traditional sense. They’re just very, very good at text.
Generative AI: Now we get spicy. Gen AI goes beyond repeating. It creates. Text, audio, video, images—it’s all fair game. It pulls from massive data sets and generates something new that feels like the source material but isn’t a copy. You may have heard this term in 2022 or 2023, but it traces back to 2014 with Ian Goodfellow’s framework for Generative Adversarial Networks (GANs).
Here’s how I like to explain GANs:
Imagine you’re playing Monopoly. Every time you pass GO, the banker hands you fake money. But each time, the fake money looks more and more like real USD. Eventually, you can’t tell the difference between the game cash and what’s in your wallet. That’s GAN. One side generates fake data, the other side tries to detect it. The whole system keeps getting better at both until it’s nearly indistinguishable.
That subtle mimicry? That’s the artistry behind Gen AI.
Agentic AI: Think of this as your chatbot going off to college. It can take initiative. It can critique, plan, and act (sometimes without you even asking). It emerged around 2023, offering a new level of independence from models before it.
Here’s a quick rule of thumb:
If I want to write a screenplay with no rules, just vibes? I want Gen AI.
If I want to write that same screenplay but also want an AI that flags overused tropes (“the last 12 teacher/barista romcoms flopped. Want to rethink that premise?”) and swaps out every overused “like” with smarter word choices? That’s Agentic AI.
Of course, Agentic AI is rooted in McCarthy’s original “agent” idea. This idea, though, has evolved significantly, leading us to today’s more dynamic “Agent”ic AI, so let’s hit the apex of this whole rabbit hole:
What even is an AI Agent?
Do we know? Right now, it's a crowded floor, but no one wants to be the first to dance.
An AI Agent can create data or solutions in contexts where it hasn't been explicitly trained. It solves tasks through inference rather than replication. It acts on its own volition without prior examples (within reason, it's not off researching breakdancing). It tries to solve your problems for you, sometimes even after you've closed the app. You don't need to babysit it.
Now, historically, we've had a few different takes on what an "agent" could be. Simple Reflex, Model-based, Goal-based, Utility-based, and Learning Agents – and based on McCarthy's original definition—an agent is a system that can perceive its environment, make decisions, and act accordingly. We could argue those five still count as the OG agents.
But today's Agents are different. And frankly, a whole lot more exciting.
They operate with higher autonomy and independent discovery. Those five earlier types? They function more like enhanced LLMs – helpful, but they need a lot of hand-holding.
The Agents of 2025 and beyond? They're independent copilots. They're the overachieving coworker who anticipated slide 17 before you even opened the deck. They become the source as opposed to just pointing you toward sources. And in a workplace setting, that's transformative. It means human teams can focus on quality control and oversight, while the AI handles the heavy lifting.
So, where are we?
Chatbots = talk/respond.
LLMs = ultra parrots.
Gen AI = probabilistic poetry.
Agentic AI = your creative co-writer with notes.
AI Agents = the teammate who already did your part of the project, ran the numbers, flagged the risk, and ordered lunch.
In other words: if you’re still confused, you’re not alone. But if nothing else, at least the fog’s a little thinner.
