All right, here we are in Nitn and we're

looking at this chat GBT- like interface

and I'm just going to tell it to create

this workflow. And I said that I need a

workflow that will receive a form

submission via web hook. We need to

process information like the person's

company, their budget, and their project

description. We're going to use that

information to qualify them as a hot,

warm, or a cold lead, and that will be

decided by doing some research on their

company, seeing if they're in the right

industry. We're also looking for budget.

And finally, we want companies that are

looking to implement custom AI solutions

in their business. After it's done that

analysis, I want these leads to be

submitted into our CRM and ClickUp. I

also want a Gmail notification and I

want a Slack notification. So, it

thought about it. It searched through

all the nodes that it has access to and

then it gave us a little outline. So,

I'm just going to go ahead and approve

this plan and now it's building that for

us. Wow. So, not only did it spit out

this workflow that looks correct, on the

right hand side, it gives us a setup

guide. So, it tells us these six things

still need to be configured and then it

also tells us how to use this and

activate it and actually push it into

production. And so if I click into a few

of these nodes here, you can see that

they're actually configured already.

Besides needing to connect to our

ClickUp account, we have variables being

passed through this entire flow. So

we've got the name right here, but then

you can also see we're sending over

content like the lead info as far as

company contact email phone

different information about the lead.

It's giving us an AI analysis and a

qualification level and also a score out

of 100. And then down here, it's

assigning a priority. And then after it

updates that row in our CRM, it uses

this set node to create an email

subject, an email body. All of these

have variables once again, and I didn't

even tell it to do this. And then we

have our message. And then it uses those

variables to send off that Gmail or

Slack alert. And then you can also see

over here, our AI agent has a system

prompt right here, and its HTTP request

is already set up to Google the company

URL. So that was a real quick demo.

We're going to be diving deeper into

this today with a few different

examples. I'm going to talk about what

works well, how to approach using this,

and also talk about some of the

limitations. So, let's get into it.

Okay, so this is what everyone's been

waiting for, which is Naden's native

natural language to workflow or agent

builder. And I'm super excited about

this because it's going to really cut

down the time that it takes to spin up a

PC. And I think that's really exciting,

but I also am a little bit worried about

the the false sense of security that

it's going to bring to a lot of people.

I think there's no problem in using this

kind of technology. I'm definitely going

to leverage it as well. But what's going

to be really important is after you get

your initial workflow generated that you

can dive into it and understand why it's

doing what it's doing and how to fix it,

how to improve it, stuff like that. Kind

of the same idea of you would never just

download a template and push it into

production or sell it to a client. You

need to understand how it's working

fundamentally. And so quick disclaimer,

if you're opening up your NDN and you

don't see this GPT- like interface,

don't worry. It hasn't been officially

rolled out to everyone yet. So anyways,

now that that's out of the way, what

we're going to do today is talk about

how this works and how to make your

results better. So what I'm going to do

today is three examples. One prompt

that's pretty vague, one prompt that is

detailed and specific with text stack

and stuff like that, and then we're

going to do one that's kind of like a

really agentic sub agent type of system,

and we'll just break down like what

worked well and what didn't. All right,

so this first one that we're going to

do, like I said, super vague, which is

create an AI agent that researches news

every morning and emails me a

newsletter. So super vague. So what's

going on right now is NIDN's AI builder

is thinking about what we want and it's

going to search through NAD's

documentation, the nodes and stuff like

that in order to figure out how to build

this workflow. So what happens is it

comes back and says, okay, based on what

you said, this is what I think you want.

Here are the different nodes we're going

to use. And then at this point, we can

either approve it or we can request

changes. So right now, I'm just going to

approve it and I want to see how well it

does with a very vague prompt. And if

you guys have ever played around with

Enidance AI assistant on the right hand

side, which is what you typically would

use like if you want to troubleshoot a

little bit, this is similar. It's just a

builder rather than just an assistant.

But anyways, you can see what's going on

is it put the nodes in the workflow and

then it connected them. And now what I

think it's doing is making sure that all

of the variables actually flow from one

node to the next. And we'll take a look

once this finishes up, which it looks

like it is done. So it gives us a setup

guide, which is basically saying, hey,

here's what's left to configure. And

then it tells us how to actually use the

workflow. So, let's dive in real quick

to see what it did. So, the workflow

starts with our scheduled trigger

because I believe I said every morning

we want this to go off. Yep, we said

every morning. So, it goes off at 7 a.m.

After that trigger goes off, we have

some workflow configuration right here,

which is apparently just setting the

email recipient, which right now is

unknown. The news topics, which

defaulted to technology, AI, and

business, and then newsletter style,

which is professional summary with key

highlights. So, all that looks good. we

would obviously have to configure our

own email recipient right here. But

let's move on to the next node which was

our news research agent. So if I click

into this guy, what we can do is we can

look at the user message as well as the

system message. And another cool thing

in this update once you guys have it is

that you can open these up on the side

if you want to just look at it full

screen. So the user message for this

agent is to research today's most

important news and create a personalized

newsletter for blank, which is the email

recipient. Honestly, that's not super

crucial because it doesn't really matter

the email address it's going to. It

should just be a good newsletter. But

it's cool to see that it's able to use

context and pass variables through.

Anyways, it then said to include five to

seven top stories with brief summaries

and source links make it engaging and

informative. So, one note here,

typically with these sort of system

instructions, that's really defining the

behavior of the agent. I would throw in

the system message rather than the user

message. Meaning, I would put that down

here rather than up here. That's not a

huge deal, but just a little thing I

noticed. But let's check out the actual

system prompt, which is setting the role

and the behavior of the agent itself. It

says, "You are a news research

assistant. Research the latest news on

topics specified by the user and create

a well- formatted newsletter. Use the

web research tool to gather current

information from multiple sources. Focus

on these topics passes over a variable.

Format the newsletter in blank, which is

the newsletter style." And then it says,

"With clear headlines, summaries, and

source links." So, we've noticed that it

says use the web research tool to gather

information. That's nice. Let's see

what's actually going on down below in

this tool. Okay. So, you can see the

tool is not configured at all. It's

still a default get request. There's no

endpoint. There's no description. So,

the tool isn't configured. We would have

to do that ourselves here. Not too bad,

especially for a first pass. And then

what I'm assuming is the output of this

agent gets passed over as a variable in

the body of the email. As you can see

right here, we have the HTML and the

text. And then we do get a daily news

newsletter which is today's date and

it's formatted nicely. So that's a nice

touch as well. Cool. So that is just to

show you guys a very very vague prompt.

It works but then there's going to be a

lot more to configure. So what we're

going to do is we're going to go back.

We're going to build this exact same

system again but I'm going to give it a

much more detailed prompt and we'll

compare how much better it is. All

right. So I'm back with a more detailed

prompt and I'm going to shoot it off. I

said to build an AI newsletter workflow

that runs every morning at 7 a.m. and it

should use Tavi and Perplexity to

research the top five trending AI and

tech news stories. Then I said pass the

results into Enthropic Cloud 3.5 Sonnet

to write a well ststructured email in

HTML format. So what I wanted to do here

was tell it exactly what chat model I

wanted to use and the different tools

that I wanted to use. So it didn't just

try to throw together a random HTTP

request. I said the email should include

a clear title. It should have subheaders

for every story. It should use bold

text. a short two to three sentence

summary for each story. And at the

bottom of the email, we should have a

sources section with the links to all of

the articles that it gathered. So once

again, it searched through the nodes, it

got all the details, and now it has a

bit of a different workflow structure

for us. And we can go ahead and request

changes if we want, but for now, we're

going to test out the oneshot prompting.

And I'm just going to go ahead and

approve this plan. And one thing that's

interesting is Tavi does have a

community node, but maybe in this

instance, I haven't installed it or

maybe because it's searching through NIN

sort of node documentation, maybe it

wasn't able to find that and pull it.

But either way, we'll see how it's able

to set up these requests. Okay, there we

go. So once again, it's done and it gave

us a setup guide. So we'd have to

configure our email address. We'd have

to get Tableau API key, Perplexity,

Enthropic, Gmail, stuff like that. But

let's go take a look at what it did with

the workflow configuration. So once

again, it did a set node up front to set

the email, the newsletter title, Tavly

API key, and the research query. Okay,

interesting. So what it would be doing

is it would have us put our Tavly API

key here. And then because this is an

HTTP request, it would basically just

pass it over somewhere in here. Okay,

right here. It would pass it over in the

actual body request of the HTTP to Tavi.

So, that is pretty cool though because

based on what I'm looking at, this does

look correct. Meaning, if we gave it a

research query and our API key, this

would work. So, I'm actually just going

to go ahead and do that real quick and

throw my Tableau API key in right there.

And then we're going to take a look at

Perplexi real quick. I've connected my

Perplexi account, so we should be good

there. And then our user message is

actually going to be fixed. So, every

day it's just going to search for find

the top five trending AI tech news

stories from today. So, that's all

configured. It's obviously a lot easier

for any to configure native nodes rather

than an HTTP request, but you can see

right here it was able to do a good job

with Tavi. We've got a merge node which

is appending those results. And then we

have our newsletter writer agent. We'll

take a look at how this thing was

prompted. Okay, so first let's look at

the user message which is saying create

an HTML newsletter from the following

research data. And then here is where

we'd get all the research data. And then

once again it's giving us the format

requirements up here. I would personally

put this in the system prompt instead,

but still let's read through it real

quick. It's telling it to start with

headers and then here would be the

newsletter title. We would have header

two tags. We would have strong bold

tags. We would have two to three

sentence summaries. So everything that I

wanted in the initial prompt that I gave

to this agent builder, it has put into

the prompt. And then real quick, let's

check out the actual system prompt,

which is pretty short. I would have

expected this to be a little more

detailed. It says, "You are an expert

newsletter writer specializing in AI and

tech news. Your task is to create a

well- formatted HTML newsletter from

research data." So then after it's able

to write that newsletter, it would go

ahead and send it to the email recipient

that we set earlier. It would have a

subject of the newsletter title plus

today's date and then it would send it

over as HTML formatting. So now that we

have no more red, I've connected all my

credentials. I'm going to go ahead and

fill in the email that it's going to

send this to and then we'll just give it

a run and see how it works. All right,

so I'm going to go ahead and run this

and it's going to do research with Tavi

Perplexity and then once that agent

writes the final newsletter, we'll go to

my Gmail and we'll check it out. Okay,

interesting. So, we already found one

thing that's going wrong, which is it

appended the data rather than merging it

all back together. So, this output two

items, which basically means we got two

different newsletter copies. So, that's

something we'll have to change. But,

real quick, let's go check in to see how

they actually turned out. Okay, so

here's the first one. And at first

glance, it looks great. We've got a

title, we've got different sources.

Maybe we want a little more meat in each

of these sections, but we do have our

sources section down below. And if we

clicked into them, we could see that

they are real links that take us to

articles. And that is how the newsletter

was actually created. So that's the

first one. This is probably the one that

was Tavly's research. And now let's go

look at the perplexity one. Wow. So this

one looks cool. Um, this one was

formatted differently. So that's the

importance of like being specific on how

you want the HTML formatting because it

looks a lot different. And then we've

got our sources down below of course

again, which we can click on and it

takes us to real articles. This one

actually pulled from a YouTube video

which is kind of cool. And we've also

got a TechCrunch AI article. So that's

kind of how the output was coming out.

So, like I said, there's a few things

that we'd want to change about this. And

what I'm curious to see is if we're able

to use the AI builder to actually make

changes. So, at the bottom, it says,

"Let me know if you'd like to adjust

anything." Okay, so I gave it some

constructive criticism. And what's cool

is it's thinking and it's working. So,

we may see this in real time actually

make these changes. Okay, so we got a

little error. Not sure what's going on

there, but it looks like it is still

working. But anyways, what's going on

here is I said the data wasn't merged

correctly. So, I told it we should want

one newsletter for both sources. And

then I wanted it to change the

newsletter prompt to actually have

inline citations as well, not just a

sources section at the bottom. So we

want to know where each sentence is

actually coming from explicitly. All

right, so it looks like it fixed it. It

found the issue, which was the merge

node was set to append rather than sort

of like combining stuff. And now it told

us here are the changes that we made and

how it works. So what I'm going to do is

just go ahead and save. I'm going to

execute the workflow and we'll just see

if it did actually make those changes

the way we wanted. and then we'll take a

look at the final copy. And actually,

just because I'm curious, while it's

doing this, I do want to see what it

added into the system prompt of the AI

agent or into the user prompt. Okay, so

it added it into the user prompt. And it

said to include inline citations using

one, two, etc. after each sentence. So

that's basically the one change it made

to the prompt. And now we'll take a

look. And once again, it didn't actually

change that merge node. Even though it

knew it was wrong, it still has it

appending. So it's still going to create

two different sources. So, this is why

it's really, really important to still

understand what workflows are doing

because not only is having that

knowledge going to make it easier for

you to prompt this AI builder on what to

build, but it's going to actually help

you troubleshoot and figure out, okay,

maybe I'll just have to manually go

change this real quick and then leverage

AI to help me more with the prompting or

with other variables to be passed over.

But anyways, real quick, let's just go

see if those inline sources came out.

All right. Okay, cool. So, we do have

numbers like this is number one. This is

number five. And you can see what they

relate to down here. It would have been

nice if it would have made these

clickable as well. But because I didn't

give it super super detailed

instructions, it didn't do that. Once

again, the importance here is all the

context that you feed into these AI

assistants to help you do your job

better and faster. But obviously, this

system works. All I had to do was put in

my email address and my API keys. And

then we are getting a newsletter that's

formatted, which this was a lot quicker

than if I would have built all this

myself and prompted everything myself.

All right. And so for the last test that

we're going to do today, I'm just going

to try to throw a pretty complex system

at it. So I'm basically having it build

a personal assistant. I said I want to

interact with it through Telegram. I

want to have a Gmail sub agent, a

calendar sub agent, a ClickUp sub agent,

and then it should use the Think Tool

and use Cloud Sonnet 3.7. So, I'm

interested to see how this is able to

use an orchestrator agent with sub

agents and sub tools and what it's going

to do with those system prompts because

in my mind, this type of AI agent

builder natively in NDN is going to be

really really good at workflows and

passing over variables, but it may

struggle a little bit more with like

autonomous systems. So, let's let it

surf through the nodes and then we'll

take a look. All right, so here it is.

It's not great because we asked for like

you know our Gmail sub aent and our

calendar sub aent to have all of the

tools not just one which here it's just

send message and here is just create an

event and ClickUp is just create a task.

But let's take a look at how it's

actually configured. So what's what's

nice is that at least it labels

everything and it tells you like how to

use it and what you need to set up.

We're getting our Telegram input and

then we're setting the system prompt and

the user preferences. And I'm assuming

that those will feed right into the

agent itself. But if we actually click

into the agent, I already see one big

issue, which is that it's looking for

the connected chat trigger node for the

input, which is not actually here right

now. Anyways, from here, the system

message is basically saying you are a

sophisticated personal assistant that

helps users manage their digital life.

You have access to specialized sub aents

for Gmail, Google, and ClickUp. And you

can use the think tool to analyze

requests. Always provide helpful

professional responses. So it may have

just got confused with Telegram because

I don't know if the chat ID and

everything is configured correctly as

well or the memory itself. But let's

take a look at these sub agents and see

how they're working. So we've got our

user message which is the Gmail request.

Up here it says you are a specialized

agent for Gmail operations. And down

here it says you are a Gmail specialist.

Handle all email operations efficiently

even though it only has one tool. And

then I'm assuming that the other sub

aents are set up the same way. This one

actually doesn't even have a user

message. So this agent would error right

away. And same thing with the ClickUp

sub agent. There's no user message here.

The easiest way to fix that is we would

literally just click on this button and

the agent itself would be able to get a

message from the main agent. But

anyways, this kind of proved the point

that I was trying to make, which is you

probably don't want to use this to build

complex multi- aent autonomous systems.

Because if I go back into this workflow

builder, and you can see there's some

pre-built ones right here, right? Let's

just say we're looking at an invoice

processing pipeline. A system like this

is going to be a workflow, meaning we're

going to go from step one to step two,

from step two to step three, and we're

going to be able to keep this AI

workflow on the path that we set for it

very clearly. These types of workflows

are a lot more predictable. They're a

lot easier for a human to build, which

means it's going to be a lot easier for

an AI system to build. And you can see

that these prompts are specific. It's

telling the workflow exactly what to

extract, what type of information. It's

telling it where to store the

information in Air Table and when to

actually trigger this AI analysis and

what to do with it. So, I guess as we

sort of start to wrap up here, what I'm

trying to say is that this is super

super cool. I think it's going to be

great to leverage this to help you with

your prompting, setting up your HTTP

requests, getting a really good idea of

where to start. But what you need to do

in order to actually make that efficient

is you need to know very clearly what

you want to build. And you need to

understand how AI workflows actually

work fundamentally because the more

context you can feed in and the more

information you have about like where

does my data come from, what do I want

to do with the data, what fields am I

looking for, what type of AI analysis am

I going to do? All of that information

is going to make your job a lot easier.

And that's going to be super valuable

because I'm aware of like sometimes you

get in here and you don't know which

nodes to use or where to start. And this

type of technology will give you a

really really good place to start and

from there you can customize it and

learn what's going on. So you guys can

see with a workflow like this, I'm

assuming that all of these variables are

going to be pretty much perfectly mapped

over. We have an AI agent that's given a

user prompt in order to parse the

information, but look at this guys.

There's actually no live variables being

fed into the agent. So this agent knows

it needs to parse an invoice, but it's

not actually receiving any invoice

information. So you still want to be

able to look at this and identify that

all of this is fixed and there's no live

variables being passed through. But then

from there we have a validation check

which is just making sure that this

stuff exists that it's not empty

everything like that. And if it's true

we're going to store the invoice data in

air table which will be the variables

that we were looking for earlier.

Anyways I just wanted to show you guys

how this AI builder works. My honest

thoughts on what it's going to be good

at and what it needs to improve on a

little bit. And more importantly, I

think the mindset of leveraging this

stuff should be the same mindset that

you have when it comes to AI automation

in general, which is AI is not going to

do everything. But if it can get me 70%

of the way there or cut down 70% of my

time from it, that is a huge win. And

that's the way you should be thinking

about approaching this as a tool that

you can leverage to learn quicker and

build quicker. And speaking of learning

and building quicker, if that's

something you're interested in, then

definitely check out my plus community.

The link for that is down in the

description. We got a great community of

members who are learning and building

with Naden every single day. And we also

have a straight path to starting to

monetize your AI automation and spinning

up a oneperson AI agency. You can learn

here as a beginner, move into the

foundations of Naden, and then start to

figure out how you can actually start to

work with clients and build a business

off of this. So, if that sounds like

something that you're interested in,

then definitely check it out. The link

for that is down in the description. But

that's going to do it for today. If you

guys enjoyed the video or learned

something new, please give it a like.

Definitely helps me out a ton. And as

always, I appreciate you guys making it

to the end of the video. I'll see you on

the next one. Thanks everyone.