WEBVTT

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Okay.

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And now just to do some quick clicking around within the product itself.

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And I'll define things as we go.

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We press Create Workflow again to create one of these.

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Bring up the editor and create a new workflow which represents automating a business process.

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That's what we're looking at.

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The button here.

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That is where we start to add a first step.

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The thing we're going to add the step is called a node.

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A node I think I'd use that word yesterday already.

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A node is one of these these building blocks, one of these little pieces that makes up a workflow.

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And we can add that.

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And you can see that, that these, these nodes, they have they come in a couple of different flavors.

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These are things that's called the the node operations.

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It can be something which is uh, these things can be either triggers reasons that that a whole workflow

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gets kicked off or they can be actions nodes that take an action.

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And so we're going to we're going to do this whole on chat message.

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Again that is an example of a trigger.

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It it's something which which initiates a workflow.

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And I can press escape.

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Remember that escape is the thing you can either you can either just press escape or you can click behind

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it and it's gone.

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And this is showing us this trigger right here.

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Um, and now we're going to, we're going to just I'm just going to press the plus button here.

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And that plus button opens this, this panel of nodes over here.

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And I can go to AI and I can go to AI agent.

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And I press escape again and here we go.

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We have an AI agent right here.

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Um, and what you'll see this thing here, this line is something that is connecting.

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I can I can press this little bin here and it gets rid of that line.

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That line is now gone.

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And I can now take that plus and I can drag it and drop it.

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This thing here is called, shockingly, a connector because it connects.

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It links to nodes together.

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Nodes are linked by connectors.

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And this trigger node is connected to this action, this AI agent right here and it has hanging off

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it a bunch of things that we can connect with.

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Uh, a bunch of connections waiting to be formed.

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And we can add in more nodes to bring this to life.

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And you're already pretty familiar with this, but I can press the plus button right there.

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We're going to add another node.

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In this point we're searching for nodes.

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And it's already showing us that the language models within that.

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And you can pick you pick any one of these different nodes.

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We we used open router and open AI yesterday.

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And we set up the keys.

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So they should already be be ready to work.

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You can pick whichever one you set up.

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I'm going to pick OpenAI right now.

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It comes up.

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It already remembers my credentials.

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We're using GPT four one mini.

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That's done.

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And now to go back you remember I just press escape.

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There it is.

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It doesn't look too pretty, but I can just organize it like, so, uh, and, uh, we can now go to

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Add Memory.

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Remember this, click on this and just click Simple Memory and press escape.

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And let's move that there.

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And then we're going to add we won't add a tool for now.

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That's good enough.

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This is set up.

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And now I come in here and I can say hi there.

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And off it goes and does its thing.

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And you can see because I've just upgraded to version two, it's got a slightly more fancy animation.

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You probably had that yesterday and you're like, how come adds animation doesn't look as fancy as mine?

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Well, now I'm just as fancy as you.

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Uh, so we get this nice, good looking diagram with lots of green, showing that the agent used its

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memory.

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Hello.

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How can I assist you today?

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Let's move this down a bit so we get a little bit more room.

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We can move this all around.

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Uh, and I'm going to say my name is Ed.

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And then we will say, nice to meet you.

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How can I help you today?

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What's my name?

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And we hope to see that this memory, which is being used to give context to the LM calls that's being

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used to populate the prompts, means that, uh, that it says, nice to meet you, Ed.

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How can I help you today?

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And just to show you a couple more details, if I select this agent and I.

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I double click to bring it up.

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Uh, down here you can see that that it's got some information about what's going on in due course.

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We will talk about, uh, various curly braces sort of stuff and what's going on in the left and what's

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going on on the right.

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But what I want to do right now is go down to here, add option.

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I'm going to pick system message.

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You remember the system messages.

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What comes at the beginning of the prompt to the LM that sets the tone so I can change it from you're

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a helpful assistant to you, or a snarky, humorous assistant that makes fun fun of whoever you are

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chatting with.

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So let's go with that.

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Uh, that's our new system prompt.

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So now actually, uh, I think yeah, escape doesn't always sometimes you have to click and then press

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escape to make sure that you've got the right thing selected.

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So now let's let's refresh this and let's say hi there, uh, and see what comes back.

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And it says well, well, well look who decided to show up.

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What's on your mind?

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Oh, mighty keyboard warrior.

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Ha ha.

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Um, I'm gonna say my name's Ed.

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I'm slightly nervous about what's what it's going to say.

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Uh, we'll find out.

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Ed.

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Huh?

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Classic.

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Short, simple and no nonsense.

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Just like your attention span, I bet.

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Ha, ha.

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So, uh, obviously, I use this both to entertain you, but also to show how easy it is to use the

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system prompt as your way to set the context.

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Tell the the agent what it's doing and and give it that, that that sense of of its objective and its

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tone and any background information.

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So that's how it works to set a system prompt.

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You can see what's happening there.

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And you can also open up this, the chat model to see exactly.

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For people that know this stuff, this will show you all of the details.

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You'll see the system message.

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You'll see the entire conversation so far passed in every time because a call to an LLM is stateless.

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So the full message each time has the system prompt, and then the full conversation in order that it

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can produce an output consistent with that input.

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That's how all of this comes together.

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Uh, and so yeah, that's a really good illustration of, of looking at an AI agent.

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It's, it's using it's got memory and it's got the context of the conversation and it's steered by the

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system prompt.

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And just to finish off the example, we will add in another node.

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And this time it will be a tool.

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It's going to be a the market stack tool that we did before.

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It should already have your credentials saved.

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But but I'm in a different instance here.

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So I paste in the key that we've got.

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I save that and it should now be connected to the market stack tool.

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Press escape to come back here.

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No thank you.

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And now there we go.

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Oh and but we also just like before, I forgot to press this blue button to say that that is automatically

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defined by the model which stock ticker it wants.

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And we will add in this filter to say we want the latest press that to.

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Yes.

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And we've now set up this tool.

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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

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me again.

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Well, look who decided to show up.

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Uh, are you just here to perfectly good virtual time?

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What's the, uh, stock price of Google?

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Let's see how it handles this now using its tool.

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Off it goes.

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It does its thing.

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Uh, the stock price of Google closed at on the most recent trading day.

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Not bad for a tech giant, huh?

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Anything else you want me to fetch?

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Or are we done while you're ahead?

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There you have it.

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There is a model that's using a system prompt.

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It's using conversation history, and it's using a tool.

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And all of this is achieved just through clever use of prompting.

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You can see in here all of the stuff, if you look through this, you can see everything that I promised

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you.

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We've got the system prompt.

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We've got the user and assistant response.

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And you see here it says calling get many end of day and market data stack with that input.

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That is the output.

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This is all going in the set of messages to the LM, and that is the response that comes back.

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And you can use this now yourself to experiment.

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Try different system prompts.

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Try using the tool and not using the tool.

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Come back into the OpenAI chat model or the open router chat model to see the messages and get that

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first hand experience for how you are able to call the make the different calls to the LM to achieve

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a genetic results.

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And you'll notice what we're looking at this editor.

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And we are seeing this here with with our chat down here, that here we can flip between editor and

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executions.

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And if we press this we now see the runs that happened.

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And we see the inputs and outputs against the different runs that we've been doing that's showing up

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here under executions.

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And we can use this screen to flip between editor and executions.

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I can also click here to rename the whole workflow to like stock.

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Uh, we'll say snarky.

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Snarky stock price.

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Look up.

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That's the name of our workflow.

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Here it is.

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And we can we can flip between the executions, as I showed you and the editor.

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We can also go back to the overview.

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And on the overview page you'll see here that we've got workflows.

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There's an executions tab at the at this level at the home screen.

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And this is showing us all of the executions across any of our workflows.

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This is like the summary level executions page.

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So those are the two places you can go for executions either within the workflow itself.

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Um, the snarky stock price lookup here it is coming in.

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Go to the executions or you can go back to your home page, back to your overview, go to executions

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to see it right there.

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Okay.

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And with that, I'm just going to wrap up by recapping some of the terminology.

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Generally speaking it's not I'm not big on terminology and learning the names of things.

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We do it so that you know what's going on.

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And we can we can talk about where we're going and we'll pick it up as we go.

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But a node you saw that a node is something which is like one step, one building block in defining

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a business process.

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We saw that it could be a trigger like like like starting a chat.

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Or it could be an action, like actually calling an AI agent.

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A connection is simply a link between two nodes.

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It's connecting the output from one node to the input to another.

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And a workflow is a collection of nodes with connections between them.

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It's it's nodes, plus connections together to automate a business process.

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That is a workflow.

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Execution is a run of a workflow.

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And we'll find out later that this can be done in a couple of modes.

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It can be done manually, which is how we did it just then.

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And you can also make a workflow active.

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And that means it's now like in production mode.

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You're not testing it like we just did.

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You've got it running for reals.

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And that's something that we'll we'll do later.

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And that is when it is active in production and then templates.

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We touched on these are pre-built workflows.

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You can you can use these as a starting point, as a template to build your own.

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And it's a good way to see a whole lot of stuff already pre-built.

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But we'll be mostly starting from scratch so that we we really build things from the ground up.

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So here's some of the the basic terminology.

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And probably you're already getting familiar with this.

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And that is a wrap on week one day two.

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And that concludes a couple of days of purple.

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Some theory we've done today.

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But it has it has set the stage for a lot of building.

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We're now going to be able to take this as our foundation for building lots of workflows starting tomorrow

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with our first yellow day, which is all about integrations.

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And we're going to do a bunch of them.

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It's what makes N810 so very powerful, and we're going to use it to the max starting tomorrow.

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You don't want to miss it.

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Take a moment to celebrate.

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You are 13% of the way through this already, and tomorrow is going to be a really great step forwards.

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I will see you then.
