Okay.
And now just to do some quick clicking around within the product itself.
And I'll define things as we go.
We press Create Workflow again to create one of these.
Bring up the editor and create a new workflow which represents automating a business process.
That's what we're looking at.
The button here.
That is where we start to add a first step.
The thing we're going to add the step is called a node.
A node I think I'd use that word yesterday already.
A node is one of these these building blocks, one of these little pieces that makes up a workflow.
And we can add that.
And you can see that, that these, these nodes, they have they come in a couple of different flavors.
These are things that's called the the node operations.
It can be something which is uh, these things can be either triggers reasons that that a whole workflow
gets kicked off or they can be actions nodes that take an action.
And so we're going to we're going to do this whole on chat message.
Again that is an example of a trigger.
It it's something which which initiates a workflow.
And I can press escape.
Remember that escape is the thing you can either you can either just press escape or you can click behind
it and it's gone.
And this is showing us this trigger right here.
Um, and now we're going to, we're going to just I'm just going to press the plus button here.
And that plus button opens this, this panel of nodes over here.
And I can go to AI and I can go to AI agent.
And I press escape again and here we go.
We have an AI agent right here.
Um, and what you'll see this thing here, this line is something that is connecting.
I can I can press this little bin here and it gets rid of that line.
That line is now gone.
And I can now take that plus and I can drag it and drop it.
This thing here is called, shockingly, a connector because it connects.
It links to nodes together.
Nodes are linked by connectors.
And this trigger node is connected to this action, this AI agent right here and it has hanging off
it a bunch of things that we can connect with.
Uh, a bunch of connections waiting to be formed.
And we can add in more nodes to bring this to life.
And you're already pretty familiar with this, but I can press the plus button right there.
We're going to add another node.
In this point we're searching for nodes.
And it's already showing us that the language models within that.
And you can pick you pick any one of these different nodes.
We we used open router and open AI yesterday.
And we set up the keys.
So they should already be be ready to work.
You can pick whichever one you set up.
I'm going to pick OpenAI right now.
It comes up.
It already remembers my credentials.
We're using GPT four one mini.
That's done.
And now to go back you remember I just press escape.
There it is.
It doesn't look too pretty, but I can just organize it like, so, uh, and, uh, we can now go to
Add Memory.
Remember this, click on this and just click Simple Memory and press escape.
And let's move that there.
And then we're going to add we won't add a tool for now.
That's good enough.
This is set up.
And now I come in here and I can say hi there.
And off it goes and does its thing.
And you can see because I've just upgraded to version two, it's got a slightly more fancy animation.
You probably had that yesterday and you're like, how come adds animation doesn't look as fancy as mine?
Well, now I'm just as fancy as you.
Uh, so we get this nice, good looking diagram with lots of green, showing that the agent used its
memory.
Hello.
How can I assist you today?
Let's move this down a bit so we get a little bit more room.
We can move this all around.
Uh, and I'm going to say my name is Ed.
And then we will say, nice to meet you.
How can I help you today?
What's my name?
And we hope to see that this memory, which is being used to give context to the LM calls that's being
used to populate the prompts, means that, uh, that it says, nice to meet you, Ed.
How can I help you today?
And just to show you a couple more details, if I select this agent and I.
I double click to bring it up.
Uh, down here you can see that that it's got some information about what's going on in due course.
We will talk about, uh, various curly braces sort of stuff and what's going on in the left and what's
going on on the right.
But what I want to do right now is go down to here, add option.
I'm going to pick system message.
You remember the system messages.
What comes at the beginning of the prompt to the LM that sets the tone so I can change it from you're
a helpful assistant to you, or a snarky, humorous assistant that makes fun fun of whoever you are
chatting with.
So let's go with that.
Uh, that's our new system prompt.
So now actually, uh, I think yeah, escape doesn't always sometimes you have to click and then press
escape to make sure that you've got the right thing selected.
So now let's let's refresh this and let's say hi there, uh, and see what comes back.
And it says well, well, well look who decided to show up.
What's on your mind?
Oh, mighty keyboard warrior.
Ha ha.
Um, I'm gonna say my name's Ed.
I'm slightly nervous about what's what it's going to say.
Uh, we'll find out.
Ed.
Huh?
Classic.
Short, simple and no nonsense.
Just like your attention span, I bet.
Ha, ha.
So, uh, obviously, I use this both to entertain you, but also to show how easy it is to use the
system prompt as your way to set the context.
Tell the the agent what it's doing and and give it that, that that sense of of its objective and its
tone and any background information.
So that's how it works to set a system prompt.
You can see what's happening there.
And you can also open up this, the chat model to see exactly.
For people that know this stuff, this will show you all of the details.
You'll see the system message.
You'll see the entire conversation so far passed in every time because a call to an LLM is stateless.
So the full message each time has the system prompt, and then the full conversation in order that it
can produce an output consistent with that input.
That's how all of this comes together.
Uh, and so yeah, that's a really good illustration of, of looking at an AI agent.
It's, it's using it's got memory and it's got the context of the conversation and it's steered by the
system prompt.
And just to finish off the example, we will add in another node.
And this time it will be a tool.
It's going to be a the market stack tool that we did before.
It should already have your credentials saved.
But but I'm in a different instance here.
So I paste in the key that we've got.
I save that and it should now be connected to the market stack tool.
Press escape to come back here.
No thank you.
And now there we go.
Oh and but we also just like before, I forgot to press this blue button to say that that is automatically
defined by the model which stock ticker it wants.
And we will add in this filter to say we want the latest press that to.
Yes.
And we've now set up this tool.
we'll reset the chat and we'll say hi there and we'll let it do its thinking and let it be snarky at
me again.
Well, look who decided to show up.
Uh, are you just here to perfectly good virtual time?
What's the, uh, stock price of Google?
Let's see how it handles this now using its tool.
Off it goes.
It does its thing.
Uh, the stock price of Google closed at on the most recent trading day.
Not bad for a tech giant, huh?
Anything else you want me to fetch?
Or are we done while you're ahead?
There you have it.
There is a model that's using a system prompt.
It's using conversation history, and it's using a tool.
And all of this is achieved just through clever use of prompting.
You can see in here all of the stuff, if you look through this, you can see everything that I promised
you.
We've got the system prompt.
We've got the user and assistant response.
And you see here it says calling get many end of day and market data stack with that input.
That is the output.
This is all going in the set of messages to the LM, and that is the response that comes back.
And you can use this now yourself to experiment.
Try different system prompts.
Try using the tool and not using the tool.
Come back into the OpenAI chat model or the open router chat model to see the messages and get that
first hand experience for how you are able to call the make the different calls to the LM to achieve
a genetic results.
And you'll notice what we're looking at this editor.
And we are seeing this here with with our chat down here, that here we can flip between editor and
executions.
And if we press this we now see the runs that happened.
And we see the inputs and outputs against the different runs that we've been doing that's showing up
here under executions.
And we can use this screen to flip between editor and executions.
I can also click here to rename the whole workflow to like stock.
Uh, we'll say snarky.
Snarky stock price.
Look up.
That's the name of our workflow.
Here it is.
And we can we can flip between the executions, as I showed you and the editor.
We can also go back to the overview.
And on the overview page you'll see here that we've got workflows.
There's an executions tab at the at this level at the home screen.
And this is showing us all of the executions across any of our workflows.
This is like the summary level executions page.
So those are the two places you can go for executions either within the workflow itself.
Um, the snarky stock price lookup here it is coming in.
Go to the executions or you can go back to your home page, back to your overview, go to executions
to see it right there.
Okay.
And with that, I'm just going to wrap up by recapping some of the terminology.
Generally speaking it's not I'm not big on terminology and learning the names of things.
We do it so that you know what's going on.
And we can we can talk about where we're going and we'll pick it up as we go.
But a node you saw that a node is something which is like one step, one building block in defining
a business process.
We saw that it could be a trigger like like like starting a chat.
Or it could be an action, like actually calling an AI agent.
A connection is simply a link between two nodes.
It's connecting the output from one node to the input to another.
And a workflow is a collection of nodes with connections between them.
It's it's nodes, plus connections together to automate a business process.
That is a workflow.
Execution is a run of a workflow.
And we'll find out later that this can be done in a couple of modes.
It can be done manually, which is how we did it just then.
And you can also make a workflow active.
And that means it's now like in production mode.
You're not testing it like we just did.
You've got it running for reals.
And that's something that we'll we'll do later.
And that is when it is active in production and then templates.
We touched on these are pre-built workflows.
You can you can use these as a starting point, as a template to build your own.
And it's a good way to see a whole lot of stuff already pre-built.
But we'll be mostly starting from scratch so that we we really build things from the ground up.
So here's some of the the basic terminology.
And probably you're already getting familiar with this.
And that is a wrap on week one day two.
And that concludes a couple of days of purple.
Some theory we've done today.
But it has it has set the stage for a lot of building.
We're now going to be able to take this as our foundation for building lots of workflows starting tomorrow
with our first yellow day, which is all about integrations.
And we're going to do a bunch of them.
It's what makes N810 so very powerful, and we're going to use it to the max starting tomorrow.
You don't want to miss it.
Take a moment to celebrate.
You are 13% of the way through this already, and tomorrow is going to be a really great step forwards.
I will see you then.