And welcome to day four.
On this day we are going to look at tools and autonomy.
But before we get there, I want to talk about Agentic AI frameworks.
Maybe something that's front of mind for you.
There are a lot of these frameworks to pick from, and these frameworks are designed to give you kind
of glue code or abstraction code that takes away some of the detail of interacting with Llms and gives
you a nice, elegant framework for building agentic solutions and focusing on the business problem that
you're solving.
And there are a lot of them and new ones come up all the time, so it's quite hard to stay on top of
everything.
But they are happening and I want to just quickly orient you, tell you about the landscape and show
you how the ones we're going to tackle on this course kind of fit into the bigger picture.
So it's worth pointing out that there's sort of different levels of complexity of these frameworks,
and they have pros and cons.
And perhaps the simplest, the bottom layer of the complexity hierarchy, the bottom is actually to
have no framework at all.
Don't use an AI framework.
And the kind of abstractions that come with it Simply connect to LMS directly using the APIs, just
like we did in the last lab, and use that to orchestrate amongst LMS.
And it's probably no surprise to you to learn that that's what we'll be doing this week.
We're not going to be using any genetic framework.
We'll be connecting direct, in fact, anthropic.
In that blog post I mentioned called Building Effective agents make a very compelling case for just
always using no framework connect directly to LMS.
The APIs are relatively simple and straightforward, and the benefit is you get to see exactly what's
going on under the hood.
You control the prompts in detail, and so it's very compelling.
And you'll see this week will be quite successful with it.
Now, alongside having no framework, I've put something called MCP and that stands for the Model Context
Protocol.
And it's something created by anthropic who are believers in no frameworks.
And it's not a framework, it is a protocol.
So it's a way that things can connect together.
It's open source.
And the idea is it allows models to be connected to sources of data and tools in a way that's sort of
open source.
Agreed, established.
So you don't need to use any glue code.
As long as you conform to this protocol.
You can stitch together models and their providers in this very elegant, simple way.
And so for that reason, because although there is a bit of code associated with it, it's really more
about having a protocol.
I've grouped it along with having no framework at all.
And something tells me that anthropic would like to be grouped that way.
So on the next level up in terms of complexity, come these two frameworks.
The first of them, OpenAI agents SDK.
I love it.
It's one of my very favorites.
It's super lightweight and simple and clean and flexible.
And I just really enjoy working with it.
We're going to be using that next week and I'm looking forward to it.
And it's relatively new.
In fact, it's so new that when I was building some of the projects, the API sort of changed.
They released a version like an hour after I'd made something that broke it.
I was I was really caught off guard by that.
So it is very new, but it's really great and I can't wait to show it to you and crew.
I is also one of my favorites.
I really love crew.
It's been around for longer.
It's very easy to use.
It's also quite lightweight.
One difference is that it has a kind of low code angle to it that you can do a lot of putting together
agents to work on a problem through only configuration or through mostly configuration through YAML
files.
So you'll see that that's, that's a little bit more in that direction.
And there is it's a bit heavier weight than, than OpenAI agents SDK.
And then on top of these not built on top of them, but but a sort of next level of complexity, the
top level of complexity for what I want to talk about, these two landgraaf from the people that brought
you Lange chain and Autogen from Microsoft.
And Autogen, as you'll discover, is really a couple of different things, but these two are relatively
heavyweight compared to the others.
They both have a steeper learning curve, particularly Landgraaf, which is quite complex.
And of course, with this kind of extra complexity comes great power.
It's really the idea of of landgraaf that you're building a kind of computational graph out of your,
your agents and their tools.
It's very powerful and it means you can build quite sophisticated things, but that also comes at a
cost in terms of there being quite a heavy learning curve and you're sort of signing up for the ecosystem.
You're signing up for a lot of terminology and concepts and abstractions which you need to buy into.
And so it really sort of that ecosystem takes over your project in a big way.
It becomes much less of a sort of agent agentic AI project and more of a landgraaf project.
That's the thing.
And and I think that's how it really both Landgraaf and Autogen are different to OpenAI agents, SDK
and crew, where even though you're using those frameworks, you still feel like you're just sort of
interacting with Llms.
Whereas for these two at the top, it's very much being part of that ecosystem.
And so I just bring this up to really orient you for the next few weeks.
These are the ones that we're going to cover.
I want to mention that there are many, many more.
I do think that this will give you a good representative understanding.
I think these are also the most popular.
There are a few others that are quite popular that if there's time, I might add in extras at some point
so that you can see some of them at work, but generally speaking, there are a lot to pick from.
And which one you pick depends on a few things.
It depends on the use case, because different types of platform will fit better for different business
objectives, and a lot of it comes down to personal preference.
There are some trade offs here in terms of how much you want to be using existing abstractions.
I have to tell you that my bias is towards downwards.
I like the the ones that stay out of your way and that are lightweight and simple and flexible.
You can probably tell that what I was saying, but I do appreciate the power that you get from from
the top.
And I had a lot of fun with both Landgraf and Autogen on the projects, I have to say.
So you know, I'm not, I'm not.
I don't feel strongly either way.
I definitely believe they have pros and cons, and I'm excited to show you all of them.
I do think that in this course, we're going to get to see quite the spectrum of different frameworks,
and it will really equip you well to be able to pick the one that works best for your work, for your
skill sets, and for your team skill sets, and for the kinds of business problems that you're looking
to tackle.