What's the difference between generative AI and agentic AI?
Well, they're two distinct approaches to artificial intelligence, and I think we're all familiar
with generative AI, things like chatbots and image generators and the like.
And they are really fundamentally reactive systems.
They wait for you to do something, specifically, they wait for you to prompt them.
And once you prompt them, their job is to generate some kind of content based upon what
you provided in the prompt.
And they're using patterns they learned during training.
Now, the things that it can generate, well, that might be some text, or it might be an
image, or it might be a piece of code, or it might be some audio.
These are all sorts of things that we can generate with generative AI.
And they're essentially sophisticated pattern matching machines.
They've learned the statistical relationships between words and between pixels and between
sound waves.
And they've learned that from massive data sets.
So when you provide a prompt, gen AI predicts what should come next based on its training.
But its work does end at generation.
It doesn't take further steps without your input.
Now agentic AI systems, by contrast, those are not reactive.
They are proactive systems.
Now like generative AI, they often start with a user prompt.
But that prompt is then used to pursue goals through a series of actions.
And an agentic system basically goes through a bit of a lifecycle.
So the way this works is it kind of, first of all, perceives its environment, if you
like.
And once it's done that, it can decide an action to take.
Once it's decided that action, it can then execute that action.
And then once that action has been executed, it can kind of learn from the output.
And then round and round we go, all with minimal human intervention.
Now both of these AI approaches often share a common foundation.
And that common foundation is large language models, or LLMs.
LLMs serve as the backbone for chatbots.
And yet there's actually other tools that are used for some of these other generative
things, diffusion models typically for images and audio.
But for chatbots, we use LLMs.
And LLMs also provide the reasoning engine that powers agentic systems.
But before we go any deeper into that, let's talk about some real world applications and
use cases.
Now maybe this doesn't put me in the best of lights, but I don't think I'm the only
one using generative AI to help with the task of content creation, and especially creative
content creation.
Now before work this morning, and this is completely true, I used a chatbot to help
write the next chapter of my Nelson DeMille fan fiction novel.
And right now you're probably thinking how profoundly cool and absolutely non-nerdy this
guy is.
But for many of us, gen AI does help with daily tasks.
Like let's consider how a YouTuber might use a generative AI system to review scripts and
suggest thumbnail concepts, and maybe even generate background music.
But at each step, there is a human.
There is a human creator.
And that human creator is looking at this generated content, and they are reviewing
it.
Check it's what they want.
Probably isn't.
So then they are refining it as well.
And they are really going through and directing this whole process.
The AI generates possibilities, but the human curates them.
Now agentic AI, that kind of thrives in scenarios that require ongoing management and consist
of multi-step processes.
So not just one thing at a time.
So consider a personal shopping agent.
Given a product to purchase as input, it actively hunts for availability across platforms.
It might monitor price fluctuations, it might handle checkout processes, and it might even
coordinate delivery.
Largely by itself, seeking input only from you, only when it's needed.
But how does it do that?
Well it turns out that the LLMs that are behind much of generative AI can also be used to
provide reasoning capabilities to AI agents.
So this essentially here, we're using gen-AI's ability to kind of think, in inverted commas
there, and it's thinking through problems.
And this has a name.
It's called chain of thought reasoning.
And this is what LLMs are so very good at.
It's a process where the agent basically breaks down a complex task into smaller logical steps.
I like how humans tackle difficult problems as well.
So let's imagine one.
Let's imagine that we want to have an agent that is planning a complex task, like organizing
a conference.
So what it's going to do is it's going to use gen-AI to generate an internal dialogue.
And that dialogue might go something like this.
It might say, first I need to understand the conference requirements, so the size, the
duration, the budget, that sort of thing.
Then I should research available venues matching those parameters.
Then it might think, well, for those venues that meet those requirements, I now need to
check availability and so on.
It's effectively the agent really kind of talking to itself to explore the problem space
before taking action.
Gen-AI is basically the cognitive engine driving an agent's decision making.
Now looking ahead, the most powerful AI systems probably won't be purely generative or purely
agentic.
They're going to be intelligent collaborators.
They'll understand when to explore options through generation and when to commit to causes
of action through agentic action.
Like an agent that would know when to generate the next chapter of fan fiction so it's ready
after, oh I don't know, a video shoot.
Maybe it's ready right now.