Build ANYTHING With n8n's NEW AI Workflow Builder!🤖 (Text2Workflow) FREE n8n Template (INSANE)

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⏳ Timestamps

00:00 - Introduction to N8N’s New AI Workflow Builder

00:38 - What is Text2Workflow and How It Works

02:02 - Major Shift from Manual to AI-Powered Workflow Creation

04:08 - Live Demo: Building a Multi-Agent Research Workflow

07:14 - Workflow Debugging and AI Iterations in Action

11:36 - Full Test Run of the Finished AI Workflow

14:10 - Second Example: Building a RAG Knowledge Assistant

16:33 - What This Means for N8N Builders and AI Experts

18:30 - Free Guide and Selling AI Automation Services

21:03 - Final Thoughts and Joining the Stride AI Academy

Transcript

This might just be the biggest change to

NN since well, it even existed. And I

got early access. What if you could

build a complete AI agent or automation

workflow simply just by describing what

you want in plain English? No more

dragging around boxes for 8 hours. It's

literally just the chat GPT for NAND. I

just built a workflow that would have

taken me hours within the last few

minutes. And let me show you how. This

is NadN's new AI workflow builder, also

called text to workflow, and they made

it easier than ever somehow to actually

go ahead and build AI agents without

writing a single line of code. Hey, I'm

Josh and I've been covering NAN

automations on this channel for the past

year. And NAN just invited me to their

limited beta for this new tool. And

honestly, I'm not sure if I'm excited or

a bit concerned for my own job security

because here's the thing. remember

spending hours connecting nodes,

debugging web hooks, and wrestling with

JSON? Yeah, that might be over. In this

video, I'm going to show you real

examples, both incredible wins and

limitations, with this tool and whether

this actually lives up to the hype. I've

been using it for the last few days and

the results have been crazy. Plus, I'll

break down what this means for the

future of NAM Builders and why the

opportunity ahead has never been

greater. I've even put together a free

eightpage guide on how to actually start

selling AI automation services or

leverage this in your current existing

business. So, make sure you stick around

to see what's possible and get access to

this guide. But first, let me show you

what this thing can actually do. All

right, so like I mentioned, this still

is a beta feature within NAND and it

will be rolling out, I believe, sometime

this next couple weeks. So, you should

be seeing it in your cloud version if

you're on the cloud. Right now, they're

still determining when they're actually

going to be releasing it to the

self-hosted version, but once they do,

it's going to be really, really cool.

So, now when you go ahead to create a

workflow within NAN, you see this nice,

beautiful chat GPT like interface where

it's asking you what would you like to

automate. You do of course still have

the option to start manually and

actually manually build out the process,

but here you can see it's giving us some

example prompts right here. So, invoice

processing pipeline, daily AI news

digest, rag knowledge assistant,

summarize emails with AI, YouTube video

chapters, multi- aent research workflow.

So, just to test this out initially,

we're going to go ahead and run this

multi- aent research workflow. So, you

can see here we got multi-agent AI

workflow where different agents

collaborate to research a topic, fact

check information, and compile

comprehensive reports. Now I'm going to

go ahead and click on create workflow.

It's really that simple. And now you'll

see on the right hand side we have our

N8N AI. So it's a similar type UI as

maybe things like cursor or chatgpt

whatever the case may be. And this is

really cool because you can see you have

the option to either ask or build. So

right now of course it is going about

building. So we can see our prompt here

and we can see the assistant right now.

So it is searching the nodes getting

node details and it's thinking about

what it actually should build. Okay. So

here is kind of the first iteration. It

just threw a bunch of nodes on the um

canvas here. And as you can see right

now they are not connected or anything

like that. Okay. So as you can see it

threw a bunch of nodes on the canvas and

it starts to kind of think throughout

the process before it connects them. So

it initially just throws the nodes on

the canvas and now you can see it's

starting to actually build some of those

connections. So it's connecting the

nodes. It's still thinking. We have kind

of the second iteration here. But just

wait a second and we're going to see how

it iterates throughout this process.

Okay. So here we're seeing that it's

updating save report node parameters. So

we can see all the different kind of

tools that it has access to and that

it's using throughout this process. All

right. So now it is officially done. We

can see that it's giving us a setup

guide. So how to set this up? Configure

our open AI credentials, our SER API,

update the safe report node URL to your

actual report storage endpoint, modify

the research topic in the N workflow

configuration as needed. And we can see

this workflow runs automatically at 9:00

a.m. You can also trigger it manually to

test and the system will research the

configured topic using the web search

fact check findings for accuracy,

compile a comprehensive structured

report and save the final report to your

specified endpoint. Now let's go ahead

and take a look at what it actually

built us here. So we can literally see

just that. Right here we have a research

schedule. So 9:00 a.m. node right here.

Then we have our workflow configuration.

So this is actually where we would go to

configure things out. So it's just a set

node right here. We can see our research

topic. Right now it's AI and machine

learning trends. Here we have max search

results. Right now it's at 10 report

format. So here it's comprehensive. And

we can see that it's going into our

initial research agent right here which

has a system message. So pretty basic.

You are a research specialist. Conduct

comprehensive research on the given

topic using available tools. Focus on

gathering accurate up-to-date

information from reliable sources. Then

we have of course access to our open AI

right here which is just using the

model. We have some memory right here.

Okay. Next we have our SER API which is

the Google search tool right here. All

right. And we have an output parser

right here. So it's literally giving us

the defined JSON schema that we want the

output to be in. All right. So next we

have a merge node right here. We have a

fact check agent right here which is

kind of doing the same thing. It looks

like we're getting a bit of issue here.

We're going to come back to that. And

then we have a report compiler agent.

Okay. And the report compiler agent has

access to the research agent tool right

here. So it's same tools as well as the

fact check agent as well. So, it has

access to all those same tools. And then

we have the final uh report parser

output right here. And then saving the

report endpoint, which is really just to

wherever we want that report to. Now, it

looks like we're experiencing an issue

here. So, I'm saying right now, it looks

good. The fact check agent isn't

connected to anything on the input side,

though. So, I'm going to go ahead and

send this and let's see what this

actually does to make some of these

iterations. So, we can see that it is

currently thinking and working about

this. Okay, now it is connecting the

nodes. Okay, so now we can see the fact

check agent is now properly connected to

receive the research findings from the

research agent. The workflow will now

flow correctly. Research agent, fact

check agent, merge results and then

report compiler agent. So this looks a

lot better. Now we can see we have our

research agent and then the fact checker

agent is right here with the merge node

which makes a lot more sense. Then we

have that same report compiler agent

right here and then the save report. So

this looks pretty good for me right now.

I'm going to go ahead and save this and

actually run a test through this. Okay,

so now we're going to execute our very

first test. So we can see the research

agent is working its magic right here.

So we can see it's using the SER API

right here to should do a total of 10

times like we configured within the

workflow configuration. It looks like

we're getting an error here. So you need

to define at least one pair of fields in

the field to match to match on. So I'm

just going to paste this error into the

actual agent here and say the merge node

is giving me this issue. Now I could fix

this of course myself, but I want to see

the agent update itself with the correct

update. So now we see that it has

updated itself. Let's go ahead and check

this. Okay, the merge node did work this

time and now we're using the fact

checker agent and that went through too.

So next we can go ahead and just use the

compiler agent right here. So it's going

to take all that information from the

research agent and the fact check agent.

Okay, looks like we're getting an error

here for it to be reading the uh fact

check agent. So now I'm going to say to

nai here the fact check agent tool under

the compiler agent and then I'm pasting

in that error here. It should update it

with the correct nodes here. Okay, so

now it made the update and if we go

ahead and rerun this. All right. So, I'm

still getting another issue here for the

fact check agent. So, I'm going to paste

this back in here. All right. So, I just

actually asked Claude about this and

it's basically saying that there is a

circular tool dependency which makes

sense. So, I just pasted that into NAI

here and then it's basically saying

you're absolutely right about the

circular dependency here and now it

actually went ahead and executed on that

should make things a lot simpler which

looks like it did a good job on. So, we

can go ahead and test this now. So, I

encountered another issue here where the

fact check agent wasn't running. So, I

actually just asked Claude about it as

well. It gave me an answer and I simply

just paste that into NAI here to execute

on. And let's see what it actually does.

Okay, so now we got a code node right

here after the research agent which

formats the data for the fact check

agent before it does the merge node. So,

let's go ahead and test this and see if

it works. All right. So, after going

back and forth a couple different times,

you can see that we actually have a

finished workflow that is working. Okay.

So, now we're going to go ahead and run

a test right here throughout the entire

workflow. And as you can see, the

research agent is going. It's doing its

magic right here with SER API going

through. Next, we have our fact checker,

which is going through, and it's called

the factchecking tool right here. Now,

this factecking tool just has some mock

data right here. Of course, if you

actually wanted the fact checker to

work, you could connect this to an

external tool such as Google search or

whatever the case may be. Next, you can

see we have our merge node right here.

And then we have the report compiler

agent right here. And you can see that

it's following the same schema that we

outlined right here in the final report

parser. So, I'm not going to read

through this entire uh output, but you

kind of get the gist right here. Now,

there was a couple different issues

here. you know, things we had to

troubleshoot back and forth within NAN

AI or within Claude. But this whole

process really does just streamline

building AI agents and makes your job

and my job just a whole lot easier. As

you can see, this still does mean that

you as an AI builder and NAN workflow

builder need to be able to troubleshoot

different things and, you know, still

have to have a good understanding of

what you're actually building. But I'm

sure you can see here how the

possibilities are endless when you're

diving into different realms on

different projects or workflows that you

may or may not have experience in. And

maybe you just want to get something

working really quickly as an MVP. This

is something that you can spin up in

just a couple minutes and then iterate

on improve yourself or with N's AI as

well. So just to walk through this

process one more time, let's say I

wanted to create a rag knowledge

assistant. So we can go here, click on

that. Now, of course, I'm just showing

you some of their pre-made examples. You

can, of course, prompt this in any

single way that you choose. So, here

we're saying, "Build a pipeline that

accepts PDF, CSV, and JSON files through

an NAND form. Chunk documents into 1,00

token segments. Generate embeddings and

store in a vector database. Use the file

name as the document key and add

metadata, including upload date and file

type. Include a chatbot that can answer

questions based on a knowledge base.

Now, we're going to go ahead and click

on create workflow. And now, we can sit

back and see the magic unfold. It's

searching through nodes. It's thinking

right now. All right. So, you can see

that it's going through different

iterations here with the different

nodes. It starts off very messy and then

we're going to get a more refined

version later on. Okay. And boom, we got

our workflow set up. Now, of course,

there is some issues here that I would

actually go and troubleshoot. I can

already see right off the bat a few

different things that need to be uh

changed around. This is the of course

the first iteration, but we can see it

has the setup uh guide right here how to

actually use it. And of course, like I

showed you before, you can go back and

forth and get this AI agent to iterate

on itself and change the workflow. And

once you get access to this feature and

you start using it, like I said, this

gives you a really good starting point.

And then you can go back and forth with

the build AI agent in N8N or the ask

node right here. The cool thing about

this one is if you ask it is actually

going to leverage N8's dogs as well as

N8N's community FAQs, all that

information to help you troubleshoot

this. Of course, you can also use GPT

and Claude to assist you on different

things. So all in all, you being able to

troubleshoot the initial build that it

gives you here and then improve upon it

is really where I see that most of your

time can be spent on since NAND's AI

builder takes care of really the first

few iterations. Now, like I mentioned at

the start of this video, guys, I put

together an eightpage document outlining

the N AI workflow builder and how you

can capitalize on the amazing

opportunity ahead of you with building

different AI agents and workflows with

NAND. Now, in this doc, it covers a few

things about the changing landscape for

NAND builders. what high demand workflow

categories that you can focus on to

build your agents and workflows in are

different ways you can package your

services, price your services, how you

can position yourself in the market,

some different examples, and then an

action plan that you can use to get

started. Now, this whole document as

well as the NAN AI agent that we built

in this example of the video will be

available within my new free Stride AI

Academy on schools. So, make sure that

you join down below 100% for free to get

access to the resources in today's video

as well as all the resources in my old

videos and future videos to come. You

can also connect with other like-minded

NAND AI agent builders as well as myself

and my team. Now, I do want to cover a

few things from this doc in this video.

So, the release of the AI agent workflow

builder marks a fundamental shift in

automation. Before building workflows

required technical knowledge, time,

patience, just like coding before

cursor. Now with this workflow builder,

anyone can describe what they want and

get a working starting point, a

prototype in mere minutes. So what does

this mean for you? The market for

automation one is about to explode. Your

expertise becomes more valuable, not

less. New service opportunities that

didn't exist before are now here. You

have a speed advantage over traditional

builders. Now you must differentiate

yourself in the market to ensure that

your services are not just another

commodity. This also means that there's

going to be a lot more competition that

arises within the market. So with this

shifting landscape, you know, the old

models where you spend hours manually

connecting nodes, deep technical

knowledge was the primary value

proposition limited by how fast you

could physically build. Now, the new

model, and of course, keep in mind this

feature is just in beta, and this is the

dumbest it's ever going to be. It's only

going to get better from here. So, the

new model AI handles basic workflow

structure, strategic thinking, and

optimization is the value proposition.

It's really only limited by how fast you

can think and iterate. Now, why experts

will win? So, beginners will hit walls

and complex logic. Error handling,

scaling, and production readiness still

requires expertise. So prompt

engineering for your workflows is a new

skill that you can learn faster. So

quality assurance and optimization

separates pros from amateurs. And one

thing I'll say too is that your IP, your

intellectual property and your domain

expertise around specific subjects, your

ability to create knowledge bases on

different industries and everything like

that is really going to be what

separates you from other NAN AI

builders. And then also too guys, one

thing I always say is don't just sell

like a copy and paste workflow. That's a

commodity. As you see, it's becoming

more and more of a commodity, and it's

only going to get like more of a

commodity with this feature and as it

improves. You need to sell an entire

business solution, a transformation for

that business owner. Keep in mind, guys,

clients pay for outcomes. Whether that's

you being able to save a business time

in their operations and increase their

efficiency, or if you can create AI

agents and workflows like appointment

setters, different sales enabled AI

agents that actually can increase the

business's revenue. Personally, I've

been focusing on the ladder just because

that's how I've been able to charge

anywhere between5 to $25,000$5

to $25,000 for my services. Now, like I

said, this entire guide will be

available in my school community. But

just a few final thoughts here. The AI

workflow builder isn't going to replace

NAM builders. It's going to empower you.

It's going to create more NAM builders,

of course, and make the experienced ones

more valuable. So, the opportunity is

now. Last week, I posted a video in our

school community about an AI agent

template I made at N&N that made me 21K

in just a few days. I have it saying in

a week here, but it was actually just

over the weekend. So, it was literally

like 3 days and that was 21K with the

same AI agent template that I posted in

the school community for free. So, if

you haven't already, join the school

community and watch this video as well.

Download the template, all that good

stuff. And I just say that to show you

that this is something that if you focus

on developing this skill set on NADN,

this is something that can actually pay

you. And I know for a lot of you guys,

this is money that can change your life.

So yeah, guys, with that being said, I

am extremely grateful myself to be able

to leverage amazing tools like NAND. And

these are tools that have completely

changed my life. So I'm grateful for the

great NAND team over there to give me

access to this feature to test it out

and share with you guys in this video

today. If you guys are new here, we

upload videos all the time on NN AI

automations, AI agent mastery. So, if

you got some value here, make sure to

like the video, comment down below, ask

me any questions you have, and subscribe

to stay up to date with our future

videos. Also too, guys, like I

mentioned, link to our free school

community with all the resources, all

the different trainings, and eight-page

document like I shared with you in

today's video will be in the school

community. And then I'll leave a link

down below as well for the StrideAI

Academy Pro if you want to join for our

premium templates, trainings, etc. And

right now it's the lowest price it will

ever be. So you can check that out if

you want. And then also if you want help

implementing AI into your business or if

you actually want help selling these

same AI solutions into other businesses.

Like I mentioned, we sell voice AI

solutions, sales solutions, CRM, AI

automations to businesses. And like I

said, our prices vary from anywhere from

5K, 25K plus. So if you want to be able

to do the same and sell that to

businesses, we can actually show you

how. We can show you how to set up a

proper acquisition channel that gets you

predictable appointments booked on your

calendar so you're not just sending out

pointless cold emails or cold calling

and just hoping for appointments to show

up on your calendar. We run predictable

paid acquisition for our own business

and our clients and that's been the

quickest way we've been able to

accelerate our growth. Other than that

guys, let me know what your thoughts are

about this new feature from NADN. Let me

know what you think this means for the

future of NAN builders and AI

automation. Other than that guys, I hope

you enjoyed this video. Have an amazing

week. Like I said before, this feature

will be available for you guys shortly

from the NAN team once they actually

deploy this new version. So, have a

great week, guys. Keep hustling, keep

grinding, and of course, guys,

accelerate your stride. Take care.