Well, hello and welcome to week three.
We are here.
We are at Crew Week, and this is the time that I get to unveil the world of crew A, and I want to
say that you might find it a little bit painful to make this mental change now, because we've just
fallen in love with OpenAI agents SDK.
We've got used to it, we know all about it.
And suddenly there's going to be a change new terminology, new constructs.
And the thing is, I would say there's a lot in common.
There's going to be some differences too, but you're going to find you're quickly going to fall in
love with AI as well.
And this is going to be a repeating pattern in the next few weeks that we're going to get comfortable
with something and love it, and then we're going to have to put it to one side and move on to the next
and just keep it in mind.
Keep keep in mind the differences.
What's similar, what's unique, what's better, what's worse.
Because different projects will lend themselves more to different frameworks.
And you're going to find that you'll fall in love with one.
And it may not be the same as me, and you should be able to make their own determination, and you'll
learn something from each of these experiments.
Plus all of our commercial projects will be a bit different.
Okay, that's enough preamble.
Let's get to it.
So crew is actually several different things.
We're gonna we're gonna have this a couple of times.
Autogen is much the same.
But crew in particular, when you when you hear people talk about crew, they might be talking about
something called crew I enterprise, which is crews platform for deploying agents, for running them,
for monitoring and managing them through a number of different screens.
They sometimes just call it the career AI platform.
Sometimes I think officially it's branded crew AI enterprise.
And if you go to their landing page at currycomb, not com, you'll see that this is what sort of presented
first and foremost.
Secondly, there is a product called crew crew I, UI studio, which is one of these low code, no code
platforms for piecing together agent interactions.
A bit like an addendum that we looked at at the very, very beginning.
But it's a it's a nice, elegant end user tool.
And then thirdly, it is something called the framework, the crew AI framework, which is an open source
framework for.
And I quote from, from the site orchestrating high performing AI agents with ease and scale.
So these are the three different offerings that they have.
And hopefully no surprise to you, we are going to be focusing on the open source framework because
we're here to build agents ourselves.
We're writing the code.
We're not going to be using the low code tooling, which is there, and we're also not going to be necessarily
needing to do something where we're deploying them and paying for a hosting platform, which is really
crew AI enterprise.
But it's perhaps worth pointing out that when we think of the differences between these different platforms,
OpenAI and anthropic have the great benefit that they already have a reason for being.
They they have their models and that is their source of revenue when it comes to groups like cry.
Of course, they they need to be very mindful of a monetization strategy.
And the open source framework is very popular, very successful.
But of course, they also need ways to monetize and cry enterprise.
And their their tooling is their path to doing that which makes complete sense and which I don't I don't
hold it against them for a moment.
But it does, of course, mean that when you go to things like their website, there is a lot of upselling
going on that they want to try and win people over so that not only will they use the open source toolkit,
but then they'll end up paying for hosting and deployment within the broader cry platform.
And so now for the rest of what we talk about, it will always be the open source framework that we
will be working with.
And when you work with the framework, there are in fact two different flavors, two different approaches
that you can use for all of your work with the framework.
And one of them is called crew I Crews, which is when you have autonomous solutions with teams working
together, agents of different roles.
Crew is crews word for a sort of team of agents, a crew of agents.
And then there's also something called crew I flows.
And this I think this is actually a newer part of crew because at least I wasn't aware of it about six
months ago when I last used crew.
It may have been there, but.
And I just didn't notice it, but but it's certainly now quite prominently in the documentation.
Cry flows are more, um, prescribed workflows, fixed workflows where you divide a problem into multiple
steps and you have workflows that lead through it with decision points and outcomes and so on.
And their their documentation suggests that you should choose crews when you're looking for autonomous
problem solving, creative collaboration, or exploratory tasks versus flows, which is more about deterministic
outcomes, auditability or precise control.
And I probably I imagine and this is just speculation, but I'm imagining that flows has come out as
a result of some of the concerns people have about running crews in production, where there is this
greater level of uncertainty and lack of auditability and sometimes a tighter defined flow, a work
flow is what's required rather than a fully agentic autonomous solution.
So that hopefully gives you a bit of context, and again, no surprise to know that we're going to be
focusing on the crews, because this is all about agents.
This course and building workflows is something that you can also do, but it's a bit more straightforward.
And of course, you could also do that simply by calling LMS directly and by interpreting their responses.
What we're interested in is the more autonomous aspect of this.
It is about when we allow different LMS to choose their own adventure and to go about solving their
problems in an agentic way.
So that will be our focus for this week.