All right. All right. I think we're

live. Let me check the streams to make

sure everything is working.

Perfect. Sweet. Twitter is go. Let's see

if YouTube is good too.

The live

YouTube is live also. Let's make is

good.

All right.

LinkedIn is also good. All right.

Amazing. Uh so 10 years ago, I had a

conversation with my boss at the time

and I told him I think that companies

can market themselves. Um all the data

existed at the moment. You could connect

all of it. You could analyze it. But

that middle layer, the the intelligence

that was necessary to have uh something

basically making decisions for you based

on that data, that was impossible uh

until now. And that's basically what

we're going to be talking about today is

how to build AI agents that can do

marketing. How do you plug in your

business data and then also

agents, give that context to those

agents so that they can go and do

activities for you. So, if you're new

here, I'm Cody Schneider. I'm the

co-founder of a company called Graph.

Um, today we're just going to be

building AI agents. I'm going to show

you the whole process, how to get

started with it. Um, and this is the

tactics that I've used to grow countless

companies. Um, I've spent the last like

seven years of my career working with

early stage companies, basically helping

them figure out what their go to market

strategies are and their promotions look

like. And this is going to be uh a

basically deep dive into everything that

I've been doing for the last three years

with my co-founder Max, the things that

we've been trying to figure out um

around all of this. So anyways, yeah,

man. With that said, let's dive into it.

We'll get it uh immediately started. So

high level before we get going um I just

want to talk through kind of the

foundations of what it takes to

basically build these agents really

appropriately. So first off you need to

have some type of data pipeline data

warehouse and then also have uh some

type of agent that's interacting with

that information. Without all of those

three together the agents are going to

make really terrible decisions. Um what

I'm going to be uh you know referencing

or like or what I'm trying to say is a

great way to understand why agents don't

work uh when they're siloed is because

the feedback loop isn't appropriate uh

for the actual thing that is creating

revenue for the company. So great

example of this is AI u AIBDRs or

AISDRs.

um tons of these companies uh came into

existence uh kind of the first wave of

this AI agent adoption and they were

optimizing for leads and then they had

terrible churn as a com as like a you

know a product because they were

optimizing for the lead. they weren't

optimizing for the revenue outcome that

was happening at the end and the agent

just didn't have the scope that was

necessary to be able to understand like

what was actually driving revenue, what

was actually driving the outcomes that

your you know grows your company. And so

this is the the most essential thing to

remember when you're thinking about this

and building these is basically how am I

giving the agent the necessary context

so that it can basically do the thing

that creates the outcome that I'm

looking for. So right with that said,

let's screen share and I can actually

talk through this. So everything I'm

about to show you, you can totally do

with open source software. Um you can

build a data pipeline, build a data

warehouse and then you can use agents

and also use agents with you know

um the is to do this. Um this is what we

do at graph.com is we basically build

you the infrastructure to build these

agents, manage these agents. That's what

I'm going to be showing today is how to

really just get started with this. But

everything I'm going to show you, you

can do entirely on uh your own using

open source. So, all right, let me share

the window. We can jump in. Perfect. All

right. So, to begin, let's just This is

an entirely new uh instance. Um so

basically I'm all I've done uh so far

with this is I've given the agent API

keys um before this like presentation

that I'm or this black class that I'm

doing. Um and then I've also connected

data. So I've connected my business data

sources to the application. Um so now

I'm going to start this chat with

Nathan. I'm just going to be like all

right let's get started

and hit send message. And so the first

thing that I'm gonna do is I'm gonna

teach the agent who I am. Um, so I'm

gonna have it say uh, and I'm going to

use transcription for this throughout

the whole while I'm doing this, but I'm

going to tell it uh, go research

graph.com as a company and then add that

to your memory. Uh, find all the

information that you can uh, about it.

Uh, and then interview me as well. I'm

the co-founder of Graph. I want to give

you context on my business that you

understand what we do, who our target

customer is, um you know, who we serve,

etc.

And so this is really the most like the

best thing that you can do. Oh, sorry.

Let me hit pause. I should have this

behav.com.

And the reason uh that you need to do

this or like why you should be doing

this is just like you have chat GBT or

clog code uh or any of these other uh

kind of like chat applications that kind

of learn you as time goes on. You want

the agent to do that for your business.

And so these agents, these modern

agents, um how they function is they

basically have these memory files that

they can go and build about whatever it

is uh that you're working with them on

or uh uh what you're um basically

sharing. It looks like my screen share

isn't working. Uh let me reshare this uh

do

full screen.

Perfect. All right. Okay. So um the

agent just went and basically researched

on the web. So it has this functionality

to go and pull in information. It added

to this memory. So now it's going to

interview me and it's asking questions.

And again what I'm trying to do is give

you the context of my business. And this

memory is going to build up over time so

that you can basically start to

accumulate all of uh this context around

your company, who you are as an

individual, etc. and basically build

that into its knowledge base. This is

super critical. It's like any company

that you you if you start a new job,

what's the first thing that you do? You

got to understand what is the company,

who do they serve, what is the actual

product, what's the differentiation,

etc. And then you can have it go and do

research um about and we'll do this in a

second about the landscape of the market

and how we can differentiate within that

space. So, uh let's talk about what the

business does.

business is a platform to build a data

pipeline, data warehouse and host

agents. So this enables me to go and

basically like build out uh live

marketing agents that work entirely

autonomously. So I can set them up so

that they can run on for example a

feedback loop that's on a daily cadence

something like etc. So I'll answer

number one. Uh let's answer number two.

Uh who's your target customer? uh who

we've been mainly working with uh is a

lot of early stage companies that are

trying to do this rather than hire

employees. We've been working companies

um we've been working with some

mid-market companies as well. Um and the

other subset is agencies that are trying

to basically do this rather than

increase their headcount.

And then uh for graph I'm the co-founder

of graph uh um and that's pretty one

simple on that side and then what's

conversation so or conversion so our

conversion uh that we're going to be

focusing on is free trial starts so

that's basically uh what we u want to be

spending our time uh like optimizing for

so everything leading up to that

basically.

All right. So, giving it that context,

I'm going to hit send message. And

again, as I'm interacting with this,

it's going to be adding to this. I'm

starting entirely from scratch. I'm show

you what the whole process looks like.

Um, if we were jumping immediately into,

you know, building an agent, we would

just go straight into, okay, here's a

workflow that I'm trying to accomplish

and let's go and do that now. So, all

right. And then asking more context. Um,

where are we on the free trial flow

today? um free trial is already live. Uh

um traffic source, a lot of it is from

organic uh and cold email is a huge

segment as well. Um and then we're also

seeing

but hasn't been driving so much traffic

and then I can go into the details with

this here. Um

so I'll just do that. uh conversion rate

free trial the paid conversion rate

we're seeing is about 24% right now with

put in stripe

and then

so I've given it this context

and I want to go and build my first

agent that conversation that we have I'm

just going to say hey

did you save this to memory

and hit message

and that context is now persistent. It's

living on and I can go and start another

conversation. And this is where we're

going to build our first agent. So the

first agent, we're just going to do it

super simple. It's basically going to do

keyword research for us, finding

longtail keywords that are related to

the product. It's then going to research

the keyword like what like basically

find the information necessary for be

able to write on it. How it's going to

do that is go and create the front page

for Google, put that into the context

window, and then write a blog post based

on that.

And then it's going to publish that to

the blog post and then I'm going to put

that on a crown job. So it publishes an

article of data for me. So this is the

first agent that we're going to build

today. Be super simple, super

straightforward. Um so save these things

to his memory

and now I'm going to go on to uh the

first agent. So I'm just going to

describe that workflow again. All right.

So the first uh workflow or agent that I

want to build basically uh we're going

to do use the keywords everywhere API um

from a uh sorry hold on give me one

second to confirm that this is working

appropriately.

Still not screen sharing right. Um

screen

All right, I think we're back on that.

Uh, give me one second. Actually, we're

just running into technical

difficulties. Always how it is when this

happens for the first time running this.

So,

all right, I'm handling this now. Looks

like the screen sharing should be good.

Um,

all right. Thank you guys for spamming

the chat on YouTube. I appreciate you.

This does not want

There we go. All right, we're fixed.

Thank you guys. All right. Uh,

appreciate the chat. Thank you for what

you're doing. Um, all right. So, I'm

just gonna walk through everything that

we did already. Uh, just to recap on. So

um what we've done is basically had the

agent started a conversation with it. I

started that new conversation which is

going to new chat getting that

conversation and then I basically had it

research the company. So I gave it the

company URL told it to go and research

uh about it um and then had it pull in

all the I had it interview me to give it

context as well. And then I'm just

continuing to add that memory file of

the agent so that it is understanding uh

basically uh like more and more about

the company. You can go as deep as you

want with this. We actually use this is

basically like continue to add knowledge

to it as new uh discoveries like happen

or new insights happen within the

organization. So and I've done that

already. Now I'm going to go and build

that first agent. So how I'm building

that first agent is I'm going to

basically describe the workflow uh that

I'm wanting to use. So let's just uh do

that from the top right now. So I'm

going to say all right uh we are going

to build a new agent that is going to be

using the keywords everywhere API to do

keyword research for keywords that are

related to the product cat uh category.

I'll give you some seed keywords to uh

kind of show you the direction to go in.

We're then going to use the serp.dev dev

API to extract what's ranking on page

one of Google currently and then we're

going to use the exa API to pull out the

content from those pages. You can also

use firecrawl as well uh for that uh or

you can use firecrawl and then I want

you to uh then write a blog post based

on that uh uh context that we pull in

from the keyword um and then we're going

to publish that to our CMS uh via uh

using the strappy API key. Again, I've

saved all the API keys already. Um, to

do that, all you have to do is prompt

like I'm going to give you an API key

and there's an input box that you go and

do. So, now the agent is going to help

me build out this workflow and what I'm

going to be doing is in tandem working

with this agent. Um, so I am going to be

teaching it how to actually do this

process just like you wouldn't teach an

employee. And then I'm going to turn

that into a recurring process and it

runs on a cadence. So again, we have

this be on a crown job. So uh the seed

key for this today we're going to do uh

Facebook ads for X

as the seed keyword. There's a whole

keyword family of this. Um I can show

you what that looks like.

And the idea here is we're basically

going to go and write uh content uh

focusing on this longtail keyword, all

these different variations. Um and then

we have a call to action that occurs

scattered throughout the blog post

content. Um so basically we get this

organic traffic. We get the signups that

occur from uh that organic traffic

coming from the blog post piece. So um

so Cword we'll do Facebook ads for X uh

for API access um I'll just say you

already have uh the um API credentials

uh with logs you've got uh the secrets

you have available

and then we're going to do output format

um so this is going to be a reusable

workflow we're going to do it repeatedly

u and uh so keep that in lines and then

on the strappy details you should have

the end points. It'll be a blogs. It

will be the endpoint that we're using.

The API key should be saved already. So

again, you're just working back and

forth with it. Um, and honestly the the

better way to do this or how I typically

the best way to do this is how I go

about it is basically um I will give it

the highlevel workflow that we're going

to build out like the agent that we're

going to build out and then I'm going to

step by step go through each of these um

sections uh showing uh like how to uh um

like how to do each of the actions how

that basically gets saved to memory and

then you're just accumulate ulating that

knowledge by doing this process. So

let's actually do this on the keywords

everywhere side. So keywords everywhere

and keywords everywhere uh is just a

really inexpensive uh API for doing

keyword research. Um it's way cheaper

than looking at something like an SCM

Rush or an AHS. AHS is really uh their

their API and endpoints or sorry and

their MCPS are super good for uh

specifically um like if you're doing

link building, but it's just costly. So

it sometimes doesn't make sense um when

you're doing like the volume that we're

about to do. So uh use keywords

everywhere

API and we're going to go find the long

variations of Facebook ads for X.

uh send that

API now to get a keyword list.

All right. Again, trying to show it this

process on how to do this. And I would

actually go now I'm going to give it the

keywords everywhere

documentation and found that this is

just a faster way to direct it and point

it um into and add this to a skill as

time goes on. So let me this as well

documentation

and we'll let that run. So this is now a

workflow that I'm working on. And how

I'm actually doing this work or how what

I would do next is I would actually

create an entirely new chat. I would

start building this other agent or this

other while I'm simultaneously working

back and forth basically between these

two points.

Um and so what happened right there is

basically the agent asked for my

permission to go and interact with the

public endpoint. Um and this is

something that is basically like the

hardest part of doing these agents. So

how these agents function is basically a

machine that's running in the cloud that

we have this agent um that uh can go and

use that computer um uh for whatever

task that it's trying to do. And the

reason um that this turns into security

challenges is basically you have API

keys that you don't want to expose um

and then you also have it you know

basically with the ability to go and

access the internet. So how basically

created the sandbox or solving this

problem is we

limitations on the tool functions that

it calls. So for what it can do and ask

uh and then also basically um are all

the secrets that you're providing to the

agent you are not ever exposing those uh

into the public. It's basically using

the proxy to to deliver those so that it

can get um well that's happening. So

let's bring this this new window open.

I'm gonna drive it over here

and then I'm gonna just start bouncing

back and forth between these two. Well,

this one's working. Let's work on the

next workflow. So, I'm gonna say, okay,

I want to make a workflow that uh serves

LinkedIn engagers. Um, and then we're

going to extract. So, basically, I'm

going to provide a LinkedIn post URL.

We're going to extract the LinkedIn

engagers uh from that post. So, it'll be

people who liked and then also

commented. And then we're basically

going to pull uh or pick those LinkedIn

uh URLs and we're going to use the

Apollo API to uh go and um find the do a

lead enrichment to find the emails of

those individuals. Um we're then going

to use the billing verifier API to go

and validate those emails and then we're

going to add them to an instantly AI

camp. So that'll be what the looks like

message there. And while these are

working, I'm just gonna check in on the

chat. How are things going? Awesome.

Still detect cool. I'll break it down to

be more chill. Screen goes out of times.

Awesome. On the screen piece. Thanks for

the feedback there. Perfect.

Cody, let me in. Awesome. Back in. Give

me one second while I share that.

All right. So, uh, those are continuing

to run. And while those are running, I

can go and, uh, work on something

entirely else. And right now what I'm

going to do uh is co-founder into this

just so that

he can

bear with me y'all while this is

happening.

Right.

And

all right. So that is now being handled.

Let me go back to screen sharing so we

can continue on.

Okay. So um Facebook ads for agencies.

Cool. We want to focus I'm going to have

it focus on the give me a list of a

hundred of these keywords and focus on

the low competition. So I want to go

after the long tail. I'm not really

concerned about the search volume. I'm

more focused on um like going after

these very longtail variations.

So I'm going to give it that feedback.

And again, everything that I'm working

on it with is going to be adding to its

context, adding to these skill files so

that in the future as this is coming as

it's doing this task for you, it's going

to be uh focusing on like the things

that you've taught it basically how to

function. Um, and if you're watching

this right now and you're like, okay,

this is like way over my head. I don't

understand what's happening here on the

technology side. There

simpler has been like

you think you can literally just ask the

I'm trying to do this like here's an

outcome that I'm trying to get to how

can I get to that outcome uh with what's

available like what is a path basically

to get there it will go and help you

through that research process you just

basically have to like give it the

guidance of like the direction that

you're wanting to head in with this and

also if you're wanting to do this um and

not sure how to do this um trying to

implement it at your company something

else that we provide as well um is bas

basically we will go um and like forward

deploy and build out these uh agents for

um the uh you know companies that we're

working with. So rock back this keyword

list. Awesome.

Sweet. So we've done that now and let's

move on to this next step. So we've got

this initial keyword list that we're

going to work on. And the reason that I

had it do process um uh is again we're

going to go and have it it write an

article a day and publish it to our CMS.

So that is the final like end uh agent

that we're going to be building here. Um

so once now that we've done this let's

actually go and start the or just the

writing process. So uh I'm going to have

it go and use the uh serper.dev

API. So let's do stripper

dev API

say use this uh use this API uh to

extract what is on page one of Google

for the target keyword Facebook ads for

pet

and again doesn't have to be organized

or like thought out you can just give it

that raw context and it's going to be

able to go and figure that

And then back over here

permissions cookie editor.

Awesome. So, we've got the Appify API

for this. Um, this is perfect.

So,

I'm going to provide you the Appify

endpoint or the Appify um URL that you

should use for this. Just give me a

second.

And then again bouncing back and forth

between these agents as I'm building

them out. And this is like what

traditional they call a go to market

engineering was like this I say

traditional and this is like two months

ago. This is what you would be doing

doing this within cloud code. The

difference here is that this activity or

this workflow that I'm building right

now, I can go and then save that as a

that as a function that the agent run

continuously right into the future. And

so it's not me just touching the

keyboard and having to manage this.

You're trying to create autonomous

agents go and do these activities on

their own simultaneously. So that said,

uh, let me come back to this and see the

racks. We got max here now, which is

amazing.

Just edit the settings. Perfect. Okay,

so

that point um,

>> hey Cody,

>> and let's go to back to these agents.

See, this one is those top articles. Uh,

it read the scer.dev that I can actually

again provide those API docs.

>> Hey chat, can you hear me?

>> You're like, okay, I'm watching this.

This is, you know, fascinating, but all

there's so much knowledge in this. Like,

where are you getting all this

information from? Do not worry.

Everything that we're going to do today

is a skill that transferred over to any

account that is or any agent. basically

just like package this up, hand it to an

agent and then they can go

these exact workflows that we are going

to be doing. You're going to uh be able

to uh basically just like take and

implement it within your and this is the

whole point of this is that it's this

like module system module solution that

you can take around it. Right? So we're

running into this error right now. uh

let's reverify.

So let me just reverify this API key

and just come back here. So give me one

second. I just have to log into Serper

to get the API that's necessary.

Max any questions from the uh YouTube

video just from the chat while I'm doing

this.

All right,

let me read that API key in now.

And so to add this API key, just so you

guys can see what the process looks like

here

to add this API key. Um, Max, can you

hear me now? Or do you have the ability

to talk?

And I'm going to present screen again

just so you can see what the process

looks like.

So basically um I'm gonna say add this

new uh secret.

>> Yo Cody, can you hear me?

>> And what you're going to see is it's

basically going to create this dialogue

box. You can add that secret in

a second.

While I'm waiting for that, let's check

in on this

and we'll bring that ampify endpoint

URL. So this new API key uh what I'm

going to say is called sereri

key and then provide that value to it.

So again going to drop this screen share

just so I don't expose this API key and

have to roll it live uh while we're

streaming.

Uh, and then I'm going to hit submit.

And that is now completed.

All right. So, we're going to come back

uh to that

uh API next.

Screen share.

All right, back in this. I'm going to

refresh that page

and then uh just tell it try the serper

uh dev API key again. Look for the API

key. Serper um API key 2 I think is the

name.

>> Hello.

>> Right. And uh we can have it restart

that process. While that's happening,

let's move on to this uh LinkedIn piece.

So the appy that I've been using um that

I've been getting awesome results with

is this one here. Um, so amplify

basically what it

basically people reselling scraping

solutions, give you an API endpoint that

can pull out some type of piece. There's

tons of these companies that do this.

Um, this is just one that I've seen be

like consistent. Um actually a friend's

company is like about to release

endpoints for this and then Twitter

that would be like another way or

another this um but the uh just for now

we're just going to do this one just uh

because I have experience.

All right. And provide uh this endpoint

um and then say uh here is a LinkedIn

postage

test. All right. And the reason that

we're doing this just to like zoom out

for a second from the the the marketing

strategy point. So when uh people are

engaging with post you can look at as

like a signal that they have interest in

whatever that content of that post is.

So for example, this is the out like the

inbound outbound strategy or whatever

you want to call it that I use. I

basically will do content um that I know

my audience uh know the audience that

I'm trying to sell to will find

interesting for example like how to

build X agent for Y thing, Y marketing

thing. Um and then I give a playbook on

here's how you can do it manually. But

then we'll go and do cold email outreach

to those same individuals. So this works

for both your own posts but also uh for

uh other people's posts on the platforms

as well. So you can extract from uh you

know any post that uh like an individual

is publishing or from ads that

competitors are running. You can set

this up so it's a recurring thing. And

this is actually a really powerful way

to do this is you basically set up a

listener. So set up an agent that's

saying hey every day go to this post

that we know that my competitor is

running ads to. extract all the new

engagements from that post and then I

want you to like basically you're just

like filling your list. They're they're

paying for that reach and then you are

basically piggyback on piggybacking on

top of that um that uh to to identify

those people that have buyer signals

that they are looking for a product like

this. So let's pull a LinkedIn post now.

Um I'm going to just go to my LinkedIn

and let's pull

Uh, let's do this one here.

Let me confirm. Uh,

awesome. So, I'm going to pull this

post. Uh, 32 engagers, 40 comments. I'm

talking about, you know, here's how to

do this thing. Um, I'm going to then

take that. Um, and the amplify that I'm

using is this one. Um, so again, just

for people that are asking, uh, for that

URL, um, this is, uh, the one that we've

used in the past. Um, the, uh, All

right. So, I'm going to paste that in

here.

And I'm also going to give it uh I end

points for this or sorry the API

documentation for this

it can go and basically pull out the

necessary information.

Um awesome. Let me see what's going on

one second.

Uh, saying your mic isn't connected,

Max.

>> Chat, can you hear me?

>> Edit mic second settings.

Yeah, not sure what's going on. Mic

side.

>> I think chat was be was able to hear me

a second ago. Um,

>> can you hear me now?

Okay, cool. Um, how about now? Max, can

you hear me?

>> Chat hears me.

>> Perfect. We're good. We're good. Thank

God.

>> Amazing.

>> Finally have a producer.

>> This freaking amateur hour. Thank you

everybody for your potions. This is our

first one. Dry Run always goes well, but

then when you're live, it's worse.

What's going on, Max?

>> Cody, can we can we take a moment and

see if we can make your stream quality

better? Um, it's kind of blurry on all

the platforms. Yeah, let me try to do

that now.

>> Also, Streamyard kind of sucks. Who uses

this?

>> Yeah, I'm not a not a fan, honestly, of

what this is. Um,

camera. Let's do 480

in the general.

Can I

change the quality of this?

Give me a second, y'all. I'm just going

to go to chat or

quality.

Sunny, we're using Streamyard.

Cody's garbled audio improvement. Yeah.

Uh, Cody.

Uh, I think Cody actually hopped off the

call. Maybe he'll come back.

>> No, no, I'm right here right now.

>> You're here? Oh, I'm seeing a black

screen. Streamyard just sucks, man. Um,

if we can improve your audio. Um,

>> part of me wonders like how much of this

problem is Streamyard or LinkedIn

because Clifton was saying he's on

LinkedIn and his audio sucks there. Um,

>> let me uh check my mic settings.

Yeah,

>> Mike, it looks normal. I'm just using

like my road, so

>> yeah,

>> whatever. We're just going to keep going

all day.

>> Sorry, guys.

>> Um I don't see a setting for higher

quality right now. Um, it's saying that

it's possible on

Let me see the plan that we're on that's

associated.

All right.

Yeah, it doesn't have like a team

settings.

Yeah, I'm not seeing a way to like

update it. So, we're just going to keep

going.

All right. Um, for the best quality, uh,

Max, do you want to give the Streamyard

link so if they want to jump into this?

Um,

>> yeah. into the chat. But all right, I'm

gonna keep going on this. Cool. All

right, so we're back uh at it. Let me

confirm screen share is live. So we're

back at it. Um so this agent, so to pick

up where we left off, we're building an

agent that's basically finding longtail

keywords that are related to the brand.

It's then going to create a content

schedule for that. Uh and right now

we're showing it how to basically

extract what's ranking on page one

currently um and put that into the

context window to write on. So why this

works um is Google's already signaling

that this type of content is what users

are searching for uh from a um search

intent standpoint and the the reasoning

behind this of like why I basically

context orient the AI from a from a

writing if I have it write to the

average or just guess on this specific

content you're not going to see you're

just going to find that it's going to

write a a a blog post that is like super

average and so the way around this is

you basically give it the source

material the context oriented the uh

where you're like wanting it to write.

Um and so how I typically do this um is

again extracting what's ranking

currently and then I'll also give it a

document um that is basically a

transcription on like my views on the

industry so that it gives that specific

context um as well and writes based off

of both those things. So, you're writing

based off of what's ranking and then

writing off of your own personal views.

And that combination, we've seen this

work really successfully from a content

production standpoint. Um, you can also

give like brand style guidelines. But

the big kicker here is that this is not

a one-time task. So you have an agent

that's basically running this production

process, but you can also have an agent

that is looking at your live data and

then going and modifying that content

based off of the feedback loop, the data

feedback loop that you're getting from

your Google search console and your

Google Analytics. Um, this also

influences the new content that you

write. So we're going to write all these

articles, right? But we want to know

like which of these so say for example

we do Google ads for X. We also do uh

Facebook ads for X and we also do um you

know all you know any X verse Y

competitors or X alternatives. What

we're going to what we're trying to

identify is the keyword uh the keywords

that are the most likely to create a

user action that we want whether that be

a signup or free trial start. And so uh

we can actually see that data and

provide that back to the agent through

the data pipeline in the data warehouse

so that the agent starts to understand

okay content like this is creating

actions that we're trying to optimize

for. Let me go do more like that and

less of like the ones that are working.

And so this is how this can be a whole

process. So going to have it go do that

extraction now. Um let's do that. Uh,

and then while that's working, let's

come back over, uh, to this other agent

that we're building. Um, so let's do

this one. Um, and again, uh, let's just

have it explore, uh, the API in its own

in the background. So,

all right. So, it's pulling out, uh,

those URLs currently.

Um, and let's see if it's running. So,

right now, it's using the web extract

function, um, which uses fire crawl. Um,

so we'll see what it's capable of

pulling out. Firecrawl can't do Reddit.

Um, and so that's where you can bring in

the XAI um, API, um, which I've I found

it's just like more robust and basically

what I can pull out. Um, so we got the

branding pages. Now I have the full

context to write the blog post. Um, let

me ask you, have you extracted the

content from those blog posts? So like

go to each of the individual URLs and

extracted that out. um you use the um uh

XAI API to do this or you can use fire

crawl. Either of those are fine to do

that data extraction.

All right,

let me use the XAI piece

API. Hey Cody, when you have a moment,

can you grab the public share link for

StreamYard and put that in the chat or

text it to me? Um, I don't have access,

unfortunately.

>> Absolutely. I'll do that right now. Um,

>> and guys, everybody listening in, uh,

hopefully this will get you the best

video and audio experience. So,

give me one second.

>> That is the studio.

>> Yeah, I think this one will work. Let me

confirm though real fast.

is the studio. What is the public share?

>> Chat is telling me share the

destinations watch link. You get this by

opening the schedule broadcast and

clicking the three dots on view on and

then copying the destination URL.

>> Awesome. Perfect. Thank you, chat.

Okay, I can only share the LinkedIn, the

YouTube or the Twitter. Um,

share public streamyard link.

Cody, we're recording this, right?

>> Mhm.

>> Great.

>> It should be

Streamyard.

destinations. Add Streamyard

destination.

And it's only having uh destinations be

YouTube, Facebook, LinkedIn, Twitter. I

can't do a direct.

I'll select the destination. Let me see

if I can do it. This might be just like

because it was

>> Are there webinar? Do you see webinar

settings in StreamYard help or sorry in

Streamyard?

Um

if you

>> do you see three dots next to enter

studio button and then there should be

on air settings.

Uh this is what I'm seeing chat.

So,

um,

>> interesting. Can you go back to the

studio, Cody?

>> Yeah.

>> Studio.

>> And then

those three. Nah.

What does that gear icon on the bottom

do?

Uh, this is just showing.

Yeah, I don't think it's going to be

here.

Yeah, I'm not sure. All right, let's

move forward.

>> All right, moving along. Okay, so um

let's pull this out. It's already

extracted.

Perfect. Um

and then I'm going to tell it uh so

right now what I'm about to do is again

my view and what's happening within this

like specific keyword category within

this space. Um, so angle that I'm going

to go from is like how uh AI is changing

like Facebook ads creative production.

So talk about that and then the uh the

Andromeda update. So like how basically

targeting all of Facebook and then your

ad creative is self optimizing into the

people that are most likely to like make

that purchase. So you're basically

traditionally you would do introspace

targeting but in contrast now what

you're doing uh is basically targeting

all of u like Facebook's like users um

and then the ad creative um that you're

making it's like intelligent enough from

a platform standpoint to understand okay

this is targeting uh you know pet

groomer business owners uh within the

United States um and uh or you know this

is targeting people who have pets within

my geography just using that pet primer

example. So the ads that you're running

are basically the way that the algorithm

the ads algorithm is actually filtering

that down. All right. So um let's do

that now. Let's uh like

say uh interview me um just to provide

context on my opinions on the industry.

We're going to use this to write um the

articles as well. We use the context

that we scraped from page one of Google

for the target keyword. And then we're

going to also use this um document as

well as context.

All right. So on doing that, um I'm just

going to start. I already know what's so

just going to go. Um, so when we're

thinking about uh like Facebook ads and

what's changing within the industry, um,

it's basically that the volume of

creative that you can make now is like

insane in comparison to what it was

previously. Using tools like Nano Banana

um, or any of these AI avatar

generators, you can basically go and

make as much creative as you need. Now

the the hard part um is twofold. you

have to basically um uh find the pain

points that your target customer is

actually having. Um and what we've seen

is the best way to do this is like

scraping social media for the actual um

conversations that people are having. So

a way to do that is using something like

Reddit. So you can go and scrape Reddit

uh to actually extract out the um uh uh

like conversations uh from uh a specific

category. uh you then take that into

context um and then you write uh based

off of um uh those like pain points or

that desired outcomes that they're

wanting to happen. Now ad creative is

one piece. The other piece uh that we

have to think about um is the u uh uh

basically the the new ads algorithm and

how it actually shows these ads. So

showing these ads um what's really

occurring on the backside is the

algorithm the Facebook ads algorithm has

gotten smart enough to understand what a

user is pre previously you'd have to go

and target you know somebody's specific

interest like say you wanted to get in

front of anybody that had a mountain

biking interest you would have to go and

like designate them now you just create

an ad that's about mountain biking and

Facebook is going to be able to find the

people that have a mountain bike

mountain biking interest it's that

intelligent and So your job uh as uh

basically um you know a company is to go

and you know solve for these problems.

How do I create creative at scale? How

do I um you know do that optimization?

On the targeting side, what we see works

um especially uh for like local

businesses in particular um is having

some type of offer um that's like a new

customer offer especially if you have

like a customer lifetime value um that

you know is high. basically can pay for

that that that new customer like a a

cheaper price. Maybe you break even on

it. Um but the goal there is just to get

them uh into in in the door um through

this. So um and then the final piece is

that uh AI is entirely changing how you

can run Facebook ads. Um so particularly

um what's it's enabling is I can have

agents that are basically running my

Facebook ads for me. So, it's connected

into my Facebook ads account. Um, and it

is, uh, making creative. It's actually,

uh, you know, publishing that creative

to the ad account. Um, it's allocating

the budget. It's turning off losers.

It's promoting winners. And it's doing

this entirely based off of conversion

actions that I've designated for that

Facebook ads account.

All right. So, I'm going to provide this

as context to the agent. I'm gonna tell

it to save this um as a save this as a

file uh that we can use later uh for

writing

for any uh Facebook ads for

X keywords. And so then again, just to

reiterate it to the agent, I'm basically

telling it we're going to write we're

going to write in the future based on

actually just to make this clean, I'm

just going to have it do this file

first, but basically we're going to just

to talk through this again, what's

happening is I'm taking the context and

let me just do a

visualization to actually like show guys

what I mean by this. Um, so I'm

basically taking, okay, so here's the

target keyword that we're searching for,

right? So again, Facebook ads,

uh,

ads for X.

I'm then taking

what is ranking on page one of Google,

so all of the search results, right? and

we are extracting those and putting this

into a

new document

that has all of this context. Right? So,

we're aggregating basically all of those

search results, the content from all

those search results into a new

document. And then we're using this new

document and what I just recorded this

like angle basically opinion about the

space and what's happening what's

changing and we're using that

to write our post

that is being published for the same

keyword. Right? So this keyword we're

basically going to give the agent okay

write about this keyword

but based on

this context that we've extracted and

also that personal like view opinion or

take. Okay. And then this is just for

one keyword.

But now imagine that for every keyword

that we've done in this list. So all of

that keyword research that we did

previously,

this same process is going to happen in

the future. But we're not doing this.

The agent is the one that's doing this.

We're just teaching the agent how to run

this process that we know creates the

outcome that we're looking for. And

again, don't need to like worry about

any of this. This is just a skill file

that ships with Graph. We're going to

just be able to go and do this. um like

it'll be out of the box have the ability

to function like this. Um and again to

get started with graph just go to

graph.com you can start a free trial

comes with 14 days you can do everything

that I'm showing you here within that

period nothing is blocked totally. So,

all right, moving back to this. Um, I'm

now going to tell it, right? Write a

blog post uh for the target keyword

phrase. Um, we're going to do Facebook

ads for pet grooming. Um, write the blog

post based off of the content that we

scraped from Serper um that we extracted

using Serper. And then also, uh, the

document that we just made, the Facebook

ads industry insights document.

Again, we're writing for that target

keyword, Facebook ads for pet grooming.

All right. So, it's going to go and

write that document.

And once it's written that, we can now

go and publish this directly to the CMS.

So, directly to the content management

system. Um, what we use to manage our

content uh is a company called Strappy.

It's an open-source CMS solution. Um the

reason that we use it uh is the API is

super robust. So it allows for us to

manage almost everything within Strappy

uh via the API. And this is how I'm

thinking about all marketing decisions

now and like what we use within our

stack. It's basically um

I'm we're trying to give everything to

an agent to function and if there's

something that has to be clicked or

interacted with within the UI that

limits the agents functionality or

capabilities and this is like the new

paradigm that we're going into. Um so

you have all these legacy companies um

that you can't do all the act activities

or actions that you need through the

API. An example of this is a software we

use called Send Grid for our email. um

this doesn't allow for you to make like

marketing emails really um effectively

via the API and so all of these new

companies are coming out an example like

this uh that are solving these problems

is a company like resend which basically

replaces send grid but the API is more

robust which enables the agent to do

more

because it now it has the capabilities

to do all of those actions that you know

are basically blocked or blocked within

the UI. I send grid. So that's working.

While that's working in the background,

let's come back to this one. I can work

through this. Uh looks like the network

for that timed out. Let's try it again.

Any questions I can try to answer, Max?

>> Nothing. Nothing. No.

>> Awesome. Sounds good.

>> Yeah.

>> Cool. So,

uh, wrote that file.

No. Did the agent time out? No, it's

still thinking.

I'm just going to stop this and then we

try again.

Let's clean up that prompt.

Awesome.

So, it's pull calling the Appify API key

now. Um, so I'm allowing that request

and it's going to go and hit that

endpoint. And once it is given that

access, that agent now has that access

continually into the future. Um the only

thing that you have to do um is

specifically um like refresh the API key

uh if you need to uh if it expires.

All right, so that workflow is now

running.

See where this is at.

I've got this stuck in a loop. I think

I've given it

correct context. Let's just make it

super simple.

blog keyword phrase

Facebook ads for pet grooming

the blog post based on the content that

we scraped from serer

the surfer made in the Facebook ads

industry insights document uh that we

created. Also,

let's do this and run that message

again.

All right,

it's giving me a 404. So let's come back

to this now and we are going to give it

the API

CLI commands.

And so this is what a lot of your job

turns into is basically you're just

jockeying these agents to train them on

the action or the activity, giving them

the context that they need and like

guide them. And it's again how I think

about this is like you're basically

creating an employee. That's the whole

strategy here is you're creating an

employee that you can go and uh

basically have it run in an autonomous

like action in the background. Um and

the best way to do this is you create

like multiple agents that have specific

act activities that they are you know

allowed or basically using. So, um, to

give you an example of this, um, on the

Facebook ads side, I would have a agent

that's, uh, if I'm, you know, that's

running my Facebook ads, I would

basically have it, um, like one agent is

managing the account. It has the rules

on, um, like what is, uh, um, how do I

say this? It's going to have the rules

on what is the um like

winner and losers like what basically

like you know if a CPM hits x amount

we're going to turn off these ads um and

then if uh you know the ads that are

winning let's dedicate budget to those

so create a totally new ad set um that

it can uh basically like put towards

that single individual ad. So I would

have an agent that's just doing the

management. I would have an agent that's

doing research on creative that writes

briefs that hands to another agent that

then goes and makes static creative or

another agent that goes and makes video

creative. Then those static and video

creative, they're basically uploading

that content into uh Facebook ads while

that manager is running in the

background. And then behind that, what

you're doing is you're basically having

um an agent that's looking at the data

that's saying, "Okay, ads like this are

performing well. Let's go to the

creative strategist and we give that

context, we give that information to it.

>> Cody, just to clarify, you can run these

in the background, right?

>> Uh yes. So all of that everything that

I'm talking about is can be ran in the

background. Um that

>> so right now you're like you're training

the agent. You're getting it so it

understands what it what it can do and

it's essentially writing this all down

into a file and then at some point when

you feel good about it, you're going to

be like, "Okay, now go do this every day

at nine o'clock." And the agent do this

loop every

>> day. Absolutely. And you can put it on

whatever cadence you want. This could be

like every hour look at this. Every day

look at this. Every week look at this.

Every month look at this. It just

depends on the cadence and really the

data volume that you're having. This is

the real um uh that's like the real the

thing that determines how frequency that

you're doing this with. Um and again

like what I'm showing you here like

walking through this step by step. Um,

I'm just showing you what's possible or

like how to teach the agent. All of

these things that we are talking about

today, these are just skills that ship

within Graph. So, as you sign up for

Graph and start using it, these are

things that we can just deploy to your

account so that you can just go and

start to do this operation like this

exact blog post skill that can just be

moved over and you can go and basically

start running that for your, you know,

your company, your brand. All right. Um,

so

>> and when you when you sign up for Graph,

you'll be able to book a call with us

with Cody and I and we'll we'll sit down

with you and figure out what you need,

like what skills should we build and

we'll build this out for you inside of

your account, etc. Um,

>> exactly. Exactly. So, I'm going to give

it the strap URL uh for it to basically

publish that. So, uh, strap URL

and then I'm going to say, uh, look at

the constant structure, uh, using the

strappy API key, um, on our CMS, um, and

then basically publish the article, um,

based off of the structure that we have

currently.

All right, let's come back here.

Um,

I don't want it to use notion.

Skip that.

Awesome. So,

>> was it trying to use a different API key

for notion?

>> No, it this is amazing. So, I I gave it

the whole workflow already and it just

was like, cool, I've already like

extracted people from this. Let me just

continue on and like try to basically uh

find the um

like let me just continue on and find

their email addresses. So like without

me prompting it, it just continued

working down this like path that we

basically.

>> Yeah.

>> Um all right. So I'm going to say uh

list the uh

list the URLs. Sorry, that was Apollo.

>> Um so list the or the LinkedIn URLs.

We extract it

just so that we can see them

and I'm going to allow that API endpoint

hit.

Then we're just going to bounce back and

forth between these. And at this point,

if I'm waiting, I'm starting a new one.

So, we're going to move on to our next

agent. Um, so we do cold email and I uh

want um basically an agent to manage the

the responding back especially if a a

lead goes cold. So if they like positive

positively reply but they don't uh like

we don't get them to like schedule a

call. I want it to basically go and

manage that uh that reach out. like, you

know, on a cadence where it's like a

week later it follows up um and then

every month after that it's going to

follow up in that same thread, right?

So, let's build an agent that does this.

From a stack standpoint, this one is

really simple, actually. Um all we're

doing is using uh instantly as our cold

email stack. We've got emails that are

already in instantly. Um, and when I say

we've got emails, we've got inboxes that

we've connected um that allows for us um

to uh basically do cold email um from.

So, we've uh created this other

segmentation. The infrastructure that we

use for those inboxes is a partner

called Hyper Tide. They're amazing. Tell

them I sent you. Um they basically

handle the spin up of Google and

Microsoft inboxes. Um you can send about

50,000 cold emails a month for about

$500 with them, which is awesome. Um,

okay. So, what I've done is I've

basically taken the instantly API key.

I've saved it within here. And I'm now

going to teach tell this agent, hey, I

want you to handle the the responses

back. So, let's do that now.

All right. I want you uh to or let's

build an agent that is managing the

instantly AI positive response inbox.

So, when somebody positively responds to

a cold outreach that we're doing, um, I

want you to reply back and get them

booked on my calendar. Um,

um, I'll get provide that link in a

second. And then if they positive reply,

respond, but then they go cold. Um, I

want you to follow up with them. Um, so

follow up with them. Uh, if they go

cold, uh, follow up with them three days

later. Then um um

seven days after that um and do that for

four weeks. Um so basically it'll be

three days um and then seven days later

um seven days after that, seven days

after that, seven days after that and

then uh once it's been a month uh that's

passed since they positively replied,

every month I want you to continue to

reach out to them.

And so we're going to put this on a

crown job. um ask me verifying

questions.

So what's going to happen is we're going

to put this on a chron job basically and

basically that is checking every hour

for when people have positively replied

and then it's going to be looking at the

last time um that they uh replied back

to us. And um let's do this. Um, we're

going to be using uh instantly AI API uh

for this. Um, they have a interested or

a positive response back. Go explore the

instantly API to understand what that

looks like.

And then let's just get instantly API

box.

Do this.

And I'm going to bring that into its own

window now. And again, we're just going

to be bouncing back and forth between

these. So let's name these just for uh

clarity sake, right? So instantly

response agents.

Rename that. Let's rename this one.

Uh we'll do allow.

And then let's go back to this one as

well.

And let's rename

this one also. So this will be LinkedIn

outbound

strategy

name.

And then we'll have this behost writing.

All right. Um,

now I'm just going to give it some

feedback. So, you can see that it did

this slug incorrectly.

Um, let's go to this. Now, this is live

just so you can see it.

Blog Facebook ads for pets grooming

strategy guide. Doesn't look like it

published actually.

What is the URL of the blog post? The

blog post slug also should only ever be

the target keyword phrase.

Cody, it actually looks like there might

be an issue with um our CMS publish just

fine in the back end, but it might take

a minute for that web hook to hit our

front end.

>> Okay, cool. No worries.

>> Yeah,

>> see if that one worked.

>> Yeah, it's just 404ing. So, we'll come

back to that in a second. Um, while

that's happening, let's work on this

Paulo API key.

And I'm gonna say,

>> oh,

>> this the LinkedIn URLs. We're going to

come back to that.

>> Cody, I think you rolled our blog post

key.

>> Okay, cool. No worries.

>> I'm gonna fix it.

>> Sounds good. It said it was hitting it

appropri appropriately. So,

>> no, you gave it a new key, but the one

that our the one that our front end runs

on. Uh

>> Ah, gotcha.

>> So, if you if you go to our front end

right now, all our blogs are gone

because the thing

>> I'm gonna I'll fix it. You're good.

>> No worries. Sounds good. Thank you.

Okay. So, show me the LinkedIn

URLs

extracted.

Appify.

Let's come back to this.

All right. I'm going to provide the

calendarly link

and we'll do web poker polling. Um the

response I'm going to give is just uh

just respond or run a cron job on an

hourly basis and check the API um for

new responses

and then for this I'm going to designate

the campaigns

just do the campaigns uh that I think

it's called like LinkedIn engagers and

Twitter engagers will be the ones that

we start with

and tone we're going to do

conversational

friendly

all I'll reach

demos booked

and then let's send calendar link

when they say they're interested.

Now we're just providing this initial

context to this agent. But the like

magic here is that I can come back to

this agent later and I can modify its

instructions and also its context.

Something else that you can have it do

as well is read the responses that it's

all like a human has already done. So it

understands like what the process looks

like for managing this inbox. And again,

you're just teaching it the activity

based off of like working with it or

work that has been done previously. And

so this is like a really effective way

actually to build these agents out. So

say if you have like a cloud code

workflow or you know even just something

that you do in chat GPT like a process

or standard operating procedure that you

run, you can bring that into these

agents and these agents will basically

go and build um that workflow or that

process based off of what you provided

and you can coach it towards the action

or the outcome that you're trying to

get, you know, it to perform or it to

do. All right, so it's done that. Um,

so I'd say this is great. Uh,

this is great. Only thing, uh, can we

add like, you know, is there any

questions I can try to answer now at the

end? Something like that um to continue

the conversation on um rather so instead

of it just only being like book this

call, like there's like a escape hatch

for them. um if they uh you know want to

uh like like if that's a big too big of

a jump for them from a commitment

standpoint

this

see LinkedIn URLs. Okay, so it pulled

those LinkedIn URLs and again uh let's

it's been trying to do the Apollo

things. We'll finally let it actually do

this. All right, now do Apollo for those

LinkedIn URLs finding the email

addresses that are associated. Run the

whole workflow. You can continue on.

>> All right, Max is fixing the piece.

>> It's almost done building.

>> No worries. All good. I know it's a ton

of content. So, cool. Um, so this is

blog posts,

but I want to do the same strategy for

landing pages, but I want to build these

landing pages based off of my live data

that I'm seeing from Google Search

Console and Google Analytics. So, let's

do that now.

So, I'm going to tell it, hey, use the

graph MCP to look at uh graph.com's

Google search console um and Google

Analytics. And what we are looking for,

what we're going to be doing is building

landing pages um for uh target keywords

that we are showing up for, but we don't

have a landing page built for already.

So, basically, what I want you to do is

mine the data of Google Search Console.

find keywords that we're getting a lot

of impressions for but we're not getting

a lot of clicks for and then look at the

keyword to see if the search intent um

would uh uh is fit for a landing page or

a blog post and then only return the

landing page keywords that we want. Ask

questions if you need

and just to reiterate what's about to

happen here. So the agent has access to

the live data from our Google search

console. Google Search Console is this

free tool that Google provides that

basically allows for us to see the uh

keywords that we're showing up for um

within uh like on Google and it's

basically going to pull in that data

connected

check for your I'll just say this um

check for your access for the graph MCP

Sorry, stop that message.

All right. So,

what is happening is it's basically

taking the live data stream on our

website and then it's making a decision

based off of that data,

based off the logic that I provided.

Max MCP session terminated.

>> I'll look into that. Um blogs are live

right now.

>> Okay, cool. Awesome. Let's jump back to

that one then. Perfect. So that blog

post has now been published and you can

see here basically how that structure

is. Um so just to call out what's

happening here. So this piece of content

focusing on Facebook ads for pet. Uh

looks like it did for 2025. Let's make

that update. Remember, the year uh that

we're currently in is 2026, not 2025.

>> Hey, Cody, I'm going to have to restart

our um Hermes container uh to fix the

MCP. Okay.

>> Um let me know in a good spot. It'll

take maybe one minute.

>> No worries. Yeah, you can do it right

now. That's totally good.

>> Cool.

Cool. So while Max is doing that um I

want to talk about

um the blog post structure here and

basically uh what's happening. So we're

targeting this keyword phrase Facebook

ads for pet screaming. So the search

intent of this individual like when

somebody searches this right they're

basically looking for a solution of like

how do I run my Facebook ads and so this

creates an opportunity for us to put uh

basically a call to action in front of

them uh that drives them deeper into the

product that solves that solution for

them. So how this is happening is we

have logic that's set up that's

basically injecting these call to

actions after the first paragraph.

This is 25% of the way down, 50% of the

way down, and then 75% of the way down.

Um,

more and more, like if you're a marketer

and you're listening to this, I would

strongly suggest looking at moving your

content management system away from

something like a Web Flow or like a Wix

and like building a custom site. Um, at

this point there this the speed and the

scale that you can work at by just going

straight to code is so much faster than

what you can do on these traditional

CMSs. Also, a lot of them like don't

have API rate limits that like enable

you to do the scale that we're talking

about from a changes standpoint. So,

like for example, we uh like we'll build

an agent today that's looking at our

previous blog posts, like our best

performing, and then it's going to go

and rewrite those based off of the um uh

data that we're getting from Google

Search Console. So, it's basically going

to go and like refresh that content for

us automatically in the background.

um

if it's doing that like you know again

say we have 10,000 posts and we're doing

that on a weekly or a monthly cadence

you could probably do it on a monthly

cadence that's probably enough for this

um these old traditional CMS's don't

really like stand up to they they can't

really work at the not they're not built

for this basically is what I'm trying to

say and so it's this challenge that

you're facing and this also translates

to like everything that you're doing

like all of these old solutions they

aren't build built for agents basically

like working on top of them. And so this

is going to be this like transition

that's happening over the next like

couple of years is basically next couple

years over this year. This is the

transition that's going to be happening.

Um is basically uh they're going to have

to evolve because agents are going to be

running more and more of these

activities, more and more of these

tasks. But again,

>> hey, just to Yeah, go ahead.

>> Sean Sean had a question. How do you

decide between blog post or landing

pages on keywords? Is this something

that you can that you can filter for and

then the AI can decide for you?

>> Yeah, great question. Um, so and easiest

way like how a human would do this is

you look at what's ranking on CA page

one currently and then you're you're

basically creating content that's

similar to what's ranking. So you can

see here it's like blog posts, right?

Also Google or Facebook ranks really

well for a lot of these as well. Um, but

it's basically blog post content um is

the ones that are winning besides the

user generated content. All of these are

blog posts. So I would then assume,

okay, people are looking for some type

of like blog post. You can look at how

terrible this content is. Like this is

just some like AI slop and that is

ranking in position one. Um, so you just

have

>> our agent can like so you could you

could get a list of keywords or the

agent gets the keywords and then you can

tell it to use the the web scrape tool

to crawl all the top 10 pages for these

things and then determine whether it

should be a blog post, a listical, a

landing page, whatever.

>> You got it. You got it. Um, the the main

formats that I use are it's going to be

blog post or landing pages. Um, you I

found that so you can do that. You can

have it go and scrape and like look like

a human would manually. I found that the

AI is like almost perfect like I at

basically guessing whether the keyword

should be a blog post or should it

should be a landing page. Um it's it's

very capable at that like it really

understands like the semantic search

intent of of the keyword that you're

extracting. Um so I I wouldn't like you

can honestly just lean on the AI at this

point. of like I'm just leaning on the

agent or sorry leaning on the

foundational model to understand uh like

whether that keyword is better as a blog

post or better as a landing page. So um

hopefully that answers that question. If

you have other questions as well uh

please fire in the chat m will pick

those up and raise those up to me. So

all right um so couple other things I

want to talk about on the blog post side

just like small tactical things. Um, so

the call to action here, um, this is to

actually send them to a video

salesletter page. So when you think

about the intent of a person that's

coming in from like Facebook ads for pet

grooming, if I'm like, "Hey, just sign

up for this software." Um, that's a huge

ask. And so what you can do is do this

bump page basically that sends them to a

video that like walks them through the

process of like how to build. We haven't

updated this yet to the new marketing

site uh content, but that's like, you

know, something that we'll do. The other

piece is you want to create different

entry points for the user um at like

different levels of commitment. Um so

what I mean by that is like this is

basically trying to get a signup to

occur. But the other call to action that

I'll have here is just like hey here's

this free asset, this free download that

you can get if you just you know provide

your contact information, right? And so

I'm just trying to make different levels

of commitment that I can have a person

come into um like through the this the

page that I'm I'm building out. Um all

right. So this uh that's kind of a a

rundown of the blog post piece. Um once

Max has that back online, we'll come

back.

>> MCP is good. Cody, go for it.

>> Awesome. Sweet. Perfect. Cool. Um try

and CP again

and then

perfect. All right. So we're coming back

to the LinkedIn cold outbound strategy.

Um it's now just again walk through what

we're doing here. We're building this

workflow out that extra fi that extracts

LinkedIn post engagers.

It goes and finds their email addresses

using the Apollo API. We're now going to

send it to million verifier to verify

those emails and then we're going to add

those to instantly. Right? So I'm going

to say use million verifier to validate

the emails

and I'm just going to restate the

workflow to it. workflow is

extract

engagers.

Extract

emails

LinkedIn profiles

profiles using Apollo

And then we're going to

take the emails from Apollo and send

them to million verifier to validate the

emails.

And then the final validated emails.

Only validated emails get added uh to

the instantly campaign

that's named LinkedIn engagers.

Right? So just restating that workflow.

We'll let that work. And we're almost

done with the form. And once we are done

with this workflow buildout, now at any

point I can come back in to the agent

and I can just give it a new LinkedIn

URL and it's going to go and do that

process of extraction and add basically

um like run through that whole thing for

me. The other thing that I can do is I

can instead of having to come back to

graph to do this, I can create

connection to Slack and I can have this

just be a Slack function where I

basically like you know forward slash an

action and then through Slack can send

that to the agent

send that LinkedIn profile to the agent

and then it will run that workflow. The

other the other like piece of this too

is like your whole team can do this,

right? You can give your this they now

so when they stumble upon a post that is

relevant, they can just hand it off to

the agent. Um uh you can extend this

even further too and like get crazy

where you're basically like having a

agent like scroll LinkedIn finding posts

that are relevant and then adding it to

the workflow pipeline. Uh this is

something I'm experimenting with right

now. I haven't done it completely, but

again once I've like figured out that

process, this will be something that I

can just hand off entirely um like as a

skill to everybody that's within, you

know, that's basically a graph user. And

this is something that we're like

constantly doing or I'm constantly doing

is basically identifying okay like what

are these skills that I can pull from

other like GitHub repos or things that

like people are showing as like alpha

that you know that that we're they're

seeing like work within the market right

now. and then just building that as uh

resources into uh the agents. And so

then it's just like, okay, cool. I have

these one-click solutions to deploy

these agents that can do these uh

specific jobs for me. Um the buildouts

that we've been doing, again, all of

them that we've been doing um like

manually. Um these are things uh that

will be uh like you'll be able to

implement right within your Graph

account uh without having to go through

this whole buildout process. I'm just

showing you the step by step so that you

can learn how to think about this agent

process and how you actually might teach

agents to do these things. Um, so that

they have that knowledge that they can

use in the future. And again, just to

reiterate, how these agents function is

they have a memory that's being built up

in the background constantly as you're

interacting with it. Everything that

you're talking to it uh here about, it's

just continuing to add that information

um into its knowledge. And this is the

real power of this is that as it

accumulates more and more understanding

of your business, it's going to

understand the company at like a

cellular level deeper than even you or

your employees could even understand.

And this is going to enable it to both

do the work for you, but also do the

analysis for you of hey, here's the