So we saw previously how cool agents are.
They take as input text and they're able to select the correct tools and execute them, and then curate
an elaborate answer.
They equipped with llms with the capability to do anything.
So up until now we treated them as a black box.
We don't know how this tool selection happens and what's happening under the hood.
And for us it was like magic.
And in this section we're going to dive deep into the internals of the react agent framework.
And we're going to see how this is implemented.
And we're going to implement our own version of the react agent.
And by doing this, we'll understand all the moving parts that are happening in react algorithm.
And we'll be able to build complex technologies with this information.
So in my opinion, this is the most beautiful part in LLM application development.
And I hope you enjoy this section.
So remember, in the earlier videos we defined an agent by using the Create React agent which received
the list of tools Dlrm and the react prompt.
We then invoke the agent with the input of what we want to process and the agent when we run.
It was smart enough to select the correct tool that we had in our toolbox.
And to execute that tool and from that to curate an answer.
And in the next couple of videos, we're going to implement all of that from zero.
Now I think it's super important to know how to implement this kind of logic from zero, from the ground
up.
And this is going to be the basis for every Agentic application.
And this is the core concept that you need to know when developing generative AI agents.
This section of the course is going to provide you with depth and really understanding.
What are generative AI agents.
Now, you might have heard about Landgraf and Lange.
Jane is pushing Landgraf for developing generative AI agents, and the LinkedIn team is very bullish
on Landgraf.
And I agree, it's an amazing technology.
Now, what we're going to do in this section is actually a prerequisite to understand why Landgraf is
important and why do we need Landgraf.
So regardless, if you're going to implement your agents within LinkedIn or within Landgraf or with
any other framework, I really recommend you watching this section because it will really give you the
depth of understanding what our agents.
All right.
So the implementation I'm going to show you is going to be based with the link chain framework.
Now there is a version of the same implementation but with the graph framework.
And this is the official recommendation from the link chain team to use graph.
However, I have to say that in order to understand JNI, I think it's very important to understand
its history and to understand how things evolved, what they are today.
Because if I were to show you right now the graph implementation, then you wouldn't have gotten that
much depth out of it, because a lot of abstraction is going to be made.
And I think building things from the ground up really gives you the sense and idea of why things are
implemented the way they are.
And if you want to support me in the course, I would appreciate it very much if you could leave me
a Udemy rating.
This really motivates me to continue and create new videos for this course and reach the content, and
it also helps future students to decide whether this course is a right fit for them or not.
So if you don't mind, I'd appreciate if you can pause, go to the ratings section and leave me a Udemy
review.
Thank you so much and see you in the next video.