So, I've built hundreds of AI agents and

AI workflows, which means I've made

hundreds of mistakes building my AI

agents and my AI workflows. So, in this

video, I'm going to be talking about the

four different layers of the AI systems

pyramid. And for each layer, I'll talk

about when you would actually use it and

a real example that I either use or have

built in the past. I also teach and

coach thousands of people who are

building AI systems for businesses. So,

this isn't just a gut feeling. This is

actually what I'm seeing in the space as

one of the most common issues. So, I

don't want to waste any time. Let's get

straight into the video. So here is the

pyramid that I was referring to where

you can see we have four different

layers. We have custom GPTs or cloud

projects or things similar to those at

the bottom as the foundation and we move

all the way up to the top with our AI

agents. And as we move up the pyramid we

increase in complexity, cost and

maintenance. So we've got the custom

GPTs, we've got simple workflow

automation with no AI, we have AI

workflows and then we have AI agents up

top. And really the most important thing

to understand is that all of these are

great and they have their use cases, but

it just kind of comes back to this idea

of becoming a problem solver, not an AI

agent builder. And this is something

that I talk about a lot in my plus

community. If you want to check it out,

the link for that is down in the

description. A lot of times people will

come to you and they think they need AI

or they know they need to start using

AI, but they don't know exactly how. And

they'll tell you what's going on. But

your job isn't to say, "Here's what I

will do with AI." Your job is to say,

okay, let's actually understand the root

of this problem and what is the most

efficient solution that we can implement

right away that will give you quick win

that will, you know, keep your cost low

and keep everything simple. And a lot of

times the answer could just be a simple

automation or a cloud project or maybe a

SAS product that already exists out

there. All right, so let's go ahead and

start with this bottom layer. What

actually is this bottom layer? Well, a

custom GPT or project or gym is a

conversational AI assistant that you're

able to preconfigure with your own

specific instructions, knowledge base,

and voice. So, you can almost think of

it as an intern who knows your company

because you give it a prompt, you give

it data, and then you ask it to help you

out with things. And the defining

characteristic here is that it's

entirely reactive, meaning it doesn't

scale a ton because it only actually

works when you trigger it or when you

initiate a conversation with it. And so

there's a lot of use cases where people

are telling me about how they do

something manually and typically they

will be triggered by a certain event and

they will need to do something and they

might need some sort of like back and

forth loop or the ability to iterate. So

for example with me every time I make an

end YouTube tutorial if you guys have

seen those in the past and I drop my

templates in the community I typically

have a sticky note in there that's like

a setup guide. And this is a super

repetitive process because it happens

every time I post a YouTube video. So I

needed to make some sort of system to

help me do this quicker. But if I built

an automation for it, it could

effectively do the same thing, but I

would still end up doing a lot of

revision and it might be more manual

than if I just set up a custom GPT and I

gave it instructions about how to do the

setup guide and different links that I

like to use and things like that. And

then if I need revisions, I'm already in

that interface and I can just tweak it

until I get something. And now that

process for me is super quick and easy.

All right, moving on to the second

layer. This is simple workflow

automation. So this is when you have a

series of steps connected together that

run automatically on some sort of

trigger that follow pure if then

conditional logic with zero AI

intelligence. The defining

characteristic here is that it's

completely predictable. It's got the

same trigger. It's got the same sequence

and you have basically the same output.

So it's a very deterministic flow with a

predictable input and predictable

sequence of steps and then predictable

output. Now that doesn't mean we can't

have something intelligent in here like

polling. But once again, that polling

check right here is based on did it

either succeed or error. And that's

black and white. There's no element of

reasoning in there. And these are a step

up from a custom GBT because these can

run in the background while you sleep.

It could be triggered when a meeting

recording has been finished or it could

be triggered every morning at 6 a.m.

Things like that. Now, just one more

step up from that is an AI workflow,

which basically holds the same principle

of a set amount of steps or a set

sequence of steps in the same order

every single time. But now we're able to

use AI in there for a little bit of

reasoning or output. So even though

there's AI to do some planning and to do

some writing and editing, it still

happens in the same order where it goes

1 2 3 4 5 6 7 8 9 10. Now, another use

case of adding AI into a workflow is

maybe you need AI to understand which

way to switch. So, like in this example,

and here's another quick little setup

guide. You can see that the AI right

here classifies the incoming email and

decides if it is going to be support or

finance or priority or promotion. And

that is because it would be really hard

for traditional logic or code to be able

to read the body of an email and the

subject and then push it down one of

these paths. It would really only be

able to do that if it was looking for a

specific keyword to trigger it. But in

this case, we actually need AI to read

and understand and then shoot it off in

the right direction. But this is still a

workflow because there's no full

autonomy and decision-m. It's basically

just one very narrow use case of AI and

then we go right back into our

traditional sequence of steps. So the

defining characteristic here is fixed

path with intelligent decisions. The

workflow always follows the same

structure, but AI makes contextawware

choices within that structure. And

actually, what I've seen when we've

worked with businesses and when we've

helped people work with businesses is

that the majority of automations can be

simple and they don't even need any AI.

And I'm talking like 50% of automations

that you could go in and build for a

business right away. But then you start

to get a little bit more complex or

maybe on your traditional automations,

you want to make them a little bit more,

you know, increase the functionality.

And that's where you may just need to

add a little AI step at the end or in

the beginning or something like that.

But when you're just getting started

with a business and you're helping them

get more automation ready, the AI agent

is almost never the right call to start

off with right away. And even just

helping teams integrate custom GPTs and

Gemini gems and things like that into

their daily workflow will also drive a

ton of efficiency. So finally, let's

take a look at an AI agent example. So

what this actually is is an autonomous

system that can work toward a goal by

perceiving its environment, making

decisions, calling tools, handling

exceptions, and adapting its approach

because it is basically fully autonomous

here. So as you can see with the

defining characteristic, true agency and

autonomy. You give it a goal and it

figures out I have these five tools,

which ones do I use and in what order do

I use them to achieve said goal? So in

this case, this was a marketing team

that I did for a YouTube video. And you

can see that it got a request from a

human via Telegram and it used its brain

to understand, okay, I have these three

content creation tools. I have these two

image creation tools and I have this

database tool. Which ones do I need to

use? And it had full autonomy based on

its system prompt and input to help it

work towards its end goal. But it's

actually interesting if you were paying

attention. this create image tool or

maybe it was the edit image tool is

actually this one right here where we

hooked it up to call on this tool. And

the reason why I'm saying that it's

interesting is because even though we

give the agent autonomy to call on its

tools once it chose the right path we

basically wanted it to stay super

predictable and that helped with the

agents performance a lot more because

its tools are workflows or AI workflows.

the way that I architected that to have

all of these be fixed. This almost makes

it seem a little bit more like an AI

workflow here where the AI does the

reasoning and then shoots it down a

fixed path. But the only difference is

after it would go down this path, it's

done. But in this example, it could go

back and forth as many times as it

wanted. So I know it can be a little

overwhelming. So I made this really

simple diagram to help you understand

what should you actually be looking to

build based on your problem at hand. So

the first question would be, do I need

to be in the loop every time? If yes,

custom GPT is the answer. If no, move on

to question two, which is, are the steps

100% logic- based? Meaning, could I have

a simple filter or a simple code? Or is

there a very explicit black and white

thing that tells me which way to go? If

yes, build a workflow? If no, then come

down here to question three, which is,

is the order of operations fixed? Is it

predictable? If yes, you're going to go

with an AI workflow. If no, if you need

some more autonomy and decision-m and

flexibility, then you want to go with an

AI agent. Now, I wanted to end off with

one example of maybe even a time where

you would come down to question three

and your answer would be yes, but you

still might want an AI agent instead.

And so, here's a really simple example

where we have a customer support agent

or customer support flow. And what

happens is it receives an email and it

has to use its brain to understand,

okay, here's the email I have. I have a

database I can look through and then I

need to reply to the incoming email with

accurate information. And so when I

think about that process as far as like

how would I do that manually, I would

one read the email, I would two check

the knowledge base and then I would

three write a response. And it really

should happen in that sequence every

single time. Which is why this could

also be a workflow where we read the

email, we search the knowledge base. We

even do something fancy here where we

filter to keep just the best responses.

But anyways, then we write the email and

then we respond to it. But the

difference here is sometimes you may not

need to search the knowledge base. In

that case, you would just be wasting

time and tokens where you could just

reply right away. Or sometimes you need

to search the knowledge base three or

four or five times before you're

confident. And in that case, the agent

up here would be better as well. The

point I'm trying to make is that it's

never just a clear black and white logic

flowchart like this. It depends on the

situation and it depends on how you

would want the manual process to be

done. And then the other tough pill to

swallow at times is just the fact that

you don't know what you don't know.

Sometimes all the signs might be

pointing to using a custom GPT, but then

you might have to just scale up after a

few months of usage to an AI workflow.

Or you might have an AI agent and then

realize later you have to scale that

down after you've actually had time to

let it get some runs and let it collect

some feedback. But once again, if you

have that problem solving mindset, it

shouldn't be an issue. And if you love

to think about solving problems and

wanting to understand what is the best

ways to solve these problems, then like

I said, I talk about this kind of stuff

a lot in my plus community, the link for

that is down in the description. We've

got a great community of over 3,000

members who are building businesses and

automations with Naden every single day.

And we have a classroom section where we

break down lots of other stuff. Agent

zero 10 hours to 10 seconds, one person

agency course. We've got a lot of

projects in here which are like live NAM

builds and things like that. Um, and we

also do a live call per week. So, I

would love to see you guys in those

calls in the community. But that's going

to do it for today. So, if you enjoyed

the video or you learned something new,

then please give it a like. It

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.