Thanks HubSpot for sponsoring this

video. What if your company got better

while you sleep? Why combinator just ran

a whole session on this? They're calling

this selfimproving companies. A

companies in the current batch are

already hitting 5x more revenue per

employee compared with 18 months ago.

Their agents have been handling all the

internal ops and write 45 of its own

tools all autonomously. We saw company

like Pocha raise $30 million to just

build and run the whole company from

scratch. All of them is point to one

thing that there's a new AI native way

of running a business. My team and many

others has been experimenting those AI

native way of running company past few

weeks and we've encapsulated our

learnings and best practice into an open

source agent skill for long horizon work

and self iterating tasks alongside many

other useful tools. And this is what I

want to talk you through today. How does

this actually work and how can you set

up step by step for your own team? So

for any company operation before AI

human has been the glue to use mole

different sets to get a certain outcome

and human the one that prioritize decide

what kind of things to do. With the

recent AI boom most of us has translate

to this AI enhanced workflow which means

you talk to an agent or a AI workflow

that is completing a task end to end.

However, there's no feedback loop back

to assistant to inform the improvement

that it should do. The human still be

the mean driver about prioritization,

plan and trigger tons. What's really

powerful about all those other use case

we're seeing here is this real AI native

loop where agent can take a input or

trigger about a certain goal doing

certain task but most importantly

actually captures feedback to learn

what's working not working plan next

steps to making sure next time it is

doing things better. Diana from YC is

explained this in a control system setup

with comparison of closed loop versus

open loop where there's no feedback

bathroom system to a closed loop where

status decisions and outcomes are

continuously captured and feedback into

intelligence layer and in a later video

it break down into five core elements

for each AI loops from how the data

actually ingest into system to the

policy layer which is like a contract

about the workflow and SOP and two layer

that allow agent to access different

systems and write quality gates in the

workflow. So either human or AI

evaluator can guard the output quality

and lastly some sort of mechanism that

can bring those learning back to system

so that it can improve its own

operations. This might feel complicated

a bit overwhelmed when you want to

automate the whole company but in

reality it's actually pretty simple and

easy to get started. Fundamentally for

each AI loop the way I started is just

set up a memory layer or environment so

the agent can keep a system or record of

the task and outcomes as well as

different skills and chron jobs for

agent to continuously executing and

monitoring the results and depending on

the type of task you must have different

cadence and skills and let's take SEO as

an example it is really good use case to

start with because SEO is kind of solve

problem that can be engineered at high

level human is basically doing this loop

of doing rese research across Google

console, internet, AF to form a keyword

strategy and based on that strategy we

start pumping out different social

content web page and continuously

monitoring the performance. Update the

strategy if needed to get agent self

sustain this loop. You can set a proper

memory layer quite commonly my memory

layer will be break down into two parts.

one's a temporal lock to log what agent

did every day or every week and from

that continuously forming a latest

strategy and pumping all the learnings

into it and this is like a simple setup

there could be a lot more things to it

which I will talk a bit more and

meanwhile you can set up a skills with

CRI so the agent can continue the whole

loop end to end by itself things like

SEO audits draft content publish and

reading data from Google Analytics or AH

and again there are also a lot of nuance

and the tools that I'm going to cover

pretty soon and This is one free tool

that's actually super relevant here,

which is HubSpot free AEO creator. So

AEO basically means answer engine

optimization to increase the chance your

product and service show up in chat GBT

peri and Gemini's answer. Fundamentally

AEO complements SEO because it cover

channels like AI answer engine that

traditional SEO didn't fully measure.

What kind of page are cited when people

asking relevant questions and what kind

of things were mentioned? So you can

reverse engineer to find relevant

authors to outreach and create content

that going to fill the gap that nobody

is covering. Normally all the tools on

the market is basically help you get a

refresh answer from different chatbot

and top pages AI is sourcing information

from. They normally charge a good amount

of money for this type of information.

That's why hot free aquer is really

good. You basically just give your

company name. It will analyze how

tragic, perplexity and Gemini

characterize your brand. Give you score

across multiple different dimensions as

well as growth area that you or the

agent can fix. So you can take the audit

report back to the agent to form the

right keyword and content strategy or

even turn into a skill that agent can do

once a while to get more rich data. I

have put a link in the description below

so you can go get audit report for free.

Now let's get back to the last part of

building those type of AI loops perform

memory SQ that is those chron jobs. You

can set up chron job to get agent

recursively executed on the action and

also do cloud auto dreaming type of

setups by having a weekly planning

chrome job. And these three things

together allow you to form a closed

loop. So the agent will continuously

monitoring the results publish content

and update it hypothesis continuously.

and ankit from AI buildup also set up

some similar AI loops SEO and increased

traffic by three times in just one to

two months. He has shared some of setup

he had for the growth analysis designing

website information architecture and

assignment to even write high quality

SEO content and blocks. I've also

included his public ripple in the

description below so you can check out

and theme loop setup can be applied for

many other different scenarios like my

friend Gio tried this experiment to get

agent autonomously wrong ads for months

by applying such autonomous loop with

power chrome and skill setup. So get

list of skills from analyzing

performance copyrightiting image

generation research and also kept a

state folder to log all change logs and

learnings as well as JSON file of

campaign history and live ads. In this

process, the first week agent tests 10

different ads format from whiteboard

sketch, notebook page, cardboard science

to tweet screenshot. And from this

process, agent learns that ugly ad

assets that looks like this actually win

better. And second way made a decision

based on all the learnings including

specific asset format to via a

whiteboard plus what kind of copy

showing on the whiteboard as well as

content itself should be around a free

skill pack and generate 243 leads within

months for a $1.5,000 budget. So this

loop does really work but there are a

lot of nuance getting into it to really

differentiate whether your AI loop is

actually going to deliver the results as

well as some tooling that will make a

setup a lot easier and one learning here

is a memory setup. Normally there are

two types of information that it need to

be saved. One is those kind of factual

memory which normally is a logs of

things that agent ever did so you can

remember what have been done before and

review the performance. Another is those

kind of procedural learnings which you

can normally turn into a skew and of

course you can just prompt the agent to

save everything as a log but when there

are quite a messy information or complex

structure it can make it very difficult

to retrieve later but their open source

memory layer that you can reuse like

Gitan's Jbrain which is a plug-in that

you can use open clock per cloud code it

has instruction to handle the data

instruction so you can access meeting

scripts YouTube videos transcripts

things like that would be otherwise

difficult to extract and data will be

saved in a specific format like in your

brief folder where this list of

predefined entities. It's kind of like

Andrew Copsy's large language model wiki

where the wiki is mainly designed for

consuming different research paper

versus the jing setup has been designed

for logging different entities for

personal assistant usage like meetings,

people, program and purchase. Each

folder has a readme file to detail

explain what goes into this type of

entity and each entity will follow a

markdown structure to log the facts as

well as timeline log. Meanwhile, it

comes with a retrieval pipeline.

Basically, all those knowledge saved

will be automatically turned into a

vector DB alongside some CRM and MCP

tool for you to search against and it is

pretty good from personal assistant

point of view that is managing hundreds

of thousands of different entities for

people like Gary Tiff. However, jub

brain is still again designed for those

entity based memory. Stone X on my team

were actually experimenting with another

entity memory setup. What do we call

looping is like a company in the loop.

We optimize this memory layer for those

long cycle task and for self-arning

behavior. So the agent has relevant

chron job to output learnings and skill

proposals. And the example of using that

is we can just copy this instruction to

any agent like her open crawl. They will

start setting up the memory layer on

computer and there are few predefined

artifacts that can be used ask you

questions step by step about what kind

of AI loops that you want to create or

what type of missions that you want

agent to drive for example I can say I

want agent to autonomously draft social

contents for me to daily drive the

growth for my Twitter then ask you

question by question about what kind of

API skill that you need to create and

some information about the procedure

knowledge like voice and tones the

cadence and you can just back and forth

with the agent based on the conversation

You can just prompt agent to set up the

relevant artifact type and the chron

jobs. In my specific case, it creates

this post draft artifact is going to

allow agent to log what has been dropped

before as well as a feedback alongside

relevant chrome job. And this chrome job

will have instruction to scan previously

relevant nodes and information and

generate new one. Most importantly

during the daily chrome, it also has

instruction to extract learnings as well

as propose updates to skills. So you can

log those procedural learnings. often

pair with this loopony plugin with some

special data access skills for data

injection because quite often agent

autobots can't really access certain

special type of data and you might have

preured data that need special CRI to

access by having a skew for data access

the detailing strategy for accessing

specific type of data is really really

helpful there are a lot of different

open source ripple that is handling this

problem that I list out here so you can

go check out using those ones for if you

build clubs I have included my data

access skills in the agent skill 101. So

you can copy paste to use. Meanwhile,

this also pretty useful open source tool

called printing press. It's trying to

solve the problem that most of the API

or MCPS or even official CRI is not

actually designed for agents. So it's

not that token efficient and also has a

whole bunch of problems like it might

get into the interactive mode which

agent is not great at interactive with

error message might not contain enough

information for agent to self-healing

and sometimes CRI can return big amount

of data and Trevor actually have article

about those 10 principle for design

agent native CRIs that's really good and

this printing press tool is basically

encapsulating all those principles into

a CIS cube so you can basically ask

agent to build any sort of CRI like

access your internal database case or

some third party software that don't

have MCP or CRI officially autonomously

research and build the CRI with those

principles in mind. So with this you can

actually build quite sophisticated and

efficient data injections. So that's it

for today's video. If you find this

helpful please like and give me a

subscribe. Thank you and I see you next

time.