They're eating here.
And in this video we're going to talk about the definition and what are agents.
AI agents.
Now if you're going to ask two different people what are AI agents.
You're going to get ten different answers.
So within those ten different answers, they all have some common grounds.
And this is what I'm going to cover in this video.
And I'm going to give you my $0.02 and how I view agents.
It's pretty aligned with the LinkedIn ecosystem.
An agent is a software system that uses Llms as a reasoning engine to decide what actions to take,
and then execute those actions.
So unlike simple chains in renewables, where the sequence of actions are hard coded agents dynamically
determined which tools or steps need to be taken to solve a specific task, or to answer specific questions.
The important thing to note here is that the LLM in an agent is deciding what to do next.
Where?
In chains.
We might use an LLM, maybe to summarize something or to generate some text.
However, we as developers, we define the entire control flow.
We defined what is going to happen next.
And the LLM is maybe used in one of those steps, but it's not deciding what to do next.
So this is the main difference between an agent and a regular chain.
Now the core manifestation of agents in the real world is the example of taking an LLM and equipping
it with tools so we can give it, maybe a tool to make an API call, to do a search, to read from a
database, or to simply write and execute some code.
And this means that we can basically give our llms superpowers to do anything, because we can connect
them to do anything with agents.
And every time I think about this idea, it's simply mind blowing because we know how powerful large
language models.
If we give them tools and ability to do stuff, then we're going to see lots of innovation, a lot of
complex stuff we couldn't do before, and we can automate a lot of stuff that we couldn't because now
we have this LLM, which is a powerful tool which is going to mimic a human right.
So let's talk in a very high level.
What is a react agent.
And it's a specific type of an agent architecture that follows the react paradigm.
Now the name react comes from reasoning in acting.
And this was a paper, um, that was published and was really the stepping stone of implementing agents.
And in this approach, we combine the reasoning power of llms to think through the problem with chain
of thought prompting, and I cover both of them in the theoretical section.
So if you want, I elaborate on them.
And after the LLM reasons about the problem, it Who decides what we need to do?
Right.
So he decides about the acting.
And then we simply go and do what the Lord tells us to do.
So we take those actions.
Now those actions are going to be manifested through tools.
What are tools?
The gist of it is that there are the abilities that we give the LMS, and they can be making an API
call, calling a database, or simply running some Python code that we wrote beforehand because it's
functions that we pre-write before running everything.
Then we have the freedom to to really write anything we want, and we can equip our LM with any capability
that we want to.
Now this is going to happen in an iterative loop until the task is complete.
And link chain and link graph provides us with pre-built react agents that we can create and customize
easily.
And these agents can invoke tools.
They can handle complex workflows and maintain state over long running tasks.
Okay, now I know I was really waving my hands in this video.
I didn't show you anything concrete.
I didn't show you any code or any demo.
This was really an intro to the intro.
Okay, so in the next video I will show you a demo of a search agent examples.
And in this section we're going to be implementing this search agent with Link chain.
Now we're going to be implementing via the react architecture which is the most important architecture
in agents.
And it's the basis for everything.
And in this section we are simply going to learn the interface.
So how do we equip our LM with tools.
This is the entire goal of this section.
Just how to do it with link chain.
And in the section afterwards we're going to see really how it's built under the hood.
And we're really going to understand every bits and bytes of this.
Now this is super interesting and this is the most important topic in LM development applications in
my opinion.