So, the way the most productive people

on Earth work today is very different

from just maybe 6 months ago. And the

entire reason is because of AI agents.

But, despite everybody and their mom

talking about AI agents, using them in

workflows, I don't think anybody's

actually really clarified what it looks

like in a practical knowledge work style

situation. Uh I think Claude made some

good headways with, you know, Claude

integrated in Slack. But, today I want

to show you guys how I am currently

getting things done. My business is

going to do over $400,000 this month,

and we work with a variety of different

things. We work with media, we work with

AI implementation, we do consulting, we

have a variety of different like

verticals and stacks. And I want to show

you how you can use AI agents to

meaningfully improve both the quantity

and then the quality of work that you do

in an organization in a very simple and

straightforward way. So, the very first

thing you need is you need a shared AI

and human workspace. And to make a long

story short, this is a place where you

can have AI working on some task

alongside people.

Genuinely, you can use whatever platform

or tool you want for this workspace. Um

the thing that is more important than

the specific like selection is just the

shape of it. And I'm going to run you

through what ours looks like right now.

I use a tool called Linear, which is

just one of the many project management

workspaces that you could use for this.

And basically, the way Linear is broken

down is you have a variety of different

statuses that denote where a task is in

the pipeline.

And so, the statuses that we've set up,

if I zoom in a little bit over here, is

we have one called inbox. We then have

another one called next. We have one

called doing. And then we finally have

one called waiting.

And then there's also the done status,

which is where we put tasks after

they're done, just so that if, you know,

we need to take a look at how a task was

completed or get some more context or

move it back to next or doing, you know,

we can do that pretty easily. So, I'm

not going to talk all day about this

setup because hopefully that's pretty

standard. The key thing in here is this

isn't just humans working on this to-do

list. We now have the ability to weave

in AI agents. And rather than have to

like prompt an AI agent constantly, all

I'll do is I will make a task and I will

tag it as AI ready and then I will send

it off and actually have it do said

task. So one of the tasks on my to-do

list today is I need to ideate, you

know, another five videos. So I'm going

to press C, that's going to open up a

new issue and then I'll say ideate five

new YouTube videos. And then in the

description, because keeping in mind

that I'm not just going to give this to

a person, I'm going to give this to an

AI agent, I'm just going to, you know,

ad-lib what I want. Hey, I want you to

use context based off videos that I've

published before to come up with five

new YouTube video ideas including three

title options for each, some angles for

each, maybe even some brief outlines

based off of current trending content on

the internet. After I'm done, I'm going

to head over here to create issue and

click that button.

Now, all I need to do is I just need to

tag this and this is a very slow, naive

way of doing all this, but I will also

show you a much faster way.

Um and once it's an agent ready, we can

actually begin working on the task. So

the way that, you know, I'm going to do

it is I'm just going to drag it over to

next.

And essentially what occurs when that

happens is this sends a request to a

server where I have an AI agent living.

This AI agent will pick up that task. It

will then consult my knowledge base to

learn kind of contextually how previous

tasks have been completed, my

preferences surrounding tasks and so on

and so forth. If we just click on this

and scroll down, you can see the Nick OS

agent run has actually started. So it's

picking it up now, assessing the card,

posting the plan and so on and so forth.

And then it's even actually writing

stuff down.

It's doing so just with my profile cuz

I'm cheap and I don't want to spend, you

know, a bunch more linear costs, but

obviously you could have this be like my

fable agent. And then it's going through

and actually doing the task for me right

now. You know, if I exit out of this,

you see we've now changed the status to

doing.

And so now this is this is occurring

almost entirely autonomously. All I do

is I'm just like the the project

manager, if that makes sense. I add

things to a queue, agents complete them

according to my specifications, and or,

you know, if there's a Q&A step

involved, which a task this would

probably have a Q&A step involved, um

you know, the agent will wait for my

approval before actually doing, let's

say, the publishing or interacting with

like the wider internet, creating me a

post on some profile. So, while it's

doing the task, let me just run you

through essentially what's going on

under the hood.

You know, we start with the new task

over here, and that's the one that I

just created talking about, you know,

the content that I wanted to make, the

the YouTube titles and and angles.

Then it's caught by a webhook. For those

of you guys that don't know, that's just

a place on the internet that a request

gets sent to. And the cool part about

Linear, and one of the reasons I like

it, is just because it has that

functionality built in. You can just

like send a request off when it's tagged

with something.

And now it goes to Fable 5. The thing

is, Fable 5 has access to three things.

It has access to workspace context. So,

this is everything that, you know, the

we are currently using or doing in the

workspace, all the files, all the other

tasks, you know, our content pipeline,

and so on and so forth. It then has

access to a knowledge base, which is a

bunch of additional textual information

that it can draw upon if necessary.

And then finally, it has it has access

to credentials. And these credentials

are things like, you know, passwords to

various services and platforms that

maybe it needs to use Chrome DevTools to

sign into, or you know, API keys, and so

on and so forth. And all this is stored

relatively securely, to the point where

Fable can use all of it in conjunction

with its own gigantic galaxy brain

intelligence to actually do the work.

And you can kind of see that in the

task. I mean, after the update, it's

just confirmed that this is how it

understands it. Make me some videos.

Well, I've given it context as to my

highest performing courses in the past.

I've given it I've given it context as

to my knowledge base. I've given it

context as to like my strategy docs, so

how I typically create ideas and angles,

and so on.

And you can also see that it just

actually changed the label to waiting,

and changed the status to waiting. So,

let's take a look at what that looks

like. Now, the thing to know about AI

agents is, you know, their outputs

aren't incredible. You can't just trust

an AI agent to do everything entirely on

its own. What you need to do is you need

to verify its outputs. You need to

essentially have some sort of final line

in the sand where you will check on the

outputs and then select the ones that

you like the most. You'll basically

force ideation, let's say, over a large

solution space and then apply your human

taste to selecting like the best

winners. I just did that in my ads video

that I published earlier. And you can

see here that I don't really like all of

these ideas. I don't think they're all

brilliant. But this one down here, I let

AI agents run a YouTube channel for 90

days. This one here's pretty solid. You

can see it's come up with some different

title formats, a source trail. It's

given me some reasonably good reasoning

and rationale behind why something like

that would work. And I think that might

actually be a video now that I am going

to do. In a typical day, my pipeline

might look reasonably like this. I'll

have a couple of tasks in next, I'll

have a few in waiting. Uh you know,

let's say I make another one right off

the top of my head. Um to sign up to

Anthropic partner network, find out

everything I need and pre-write draft

application. Um you know, I'll I'll just

be rolling through here selecting agent

ready on tasks that make sense, but then

also adding tasks that maybe other

people in my organization or I need to

do. Uh this is really the the idea

behind a collaborative and shared

workspace. And the main benefit there is

I no longer just have to sit down and

then look at a terminal, wait for its

outputs, and then finally when it, you

know, gives me an output, I proceed. Um

I'm capable of operating like the speed

of thought. I'm capable of going very

fast here. You know, I can bang out 20

of these ideas simultaneously of 20

different agents all operating on

various tasks using my knowledge base,

and then I can just check in, you know,

once or twice a day when they're done to

like assess in batches. This basically

solves context switching. Now, once

you're done, you can actually just give

it some information like, "Hey, I like

this one. I'd like to generate 10 thumbs

for video

Alongside adding this plus alternatives

plus everything into content pipeline.

The reason why this is valuable is you

just don't need a text box open all day

and you can actually work off of a

single source of truth. Um I wanted to

buy an ergonomic chair for my home

setup. Well, I had to go through and

just do a tremendous amount of research

cross-referencing Reddit and so on and

so forth to get me a bunch. Then I

actually ended up buying uh one of these

here, which took like 30 seconds. And

you can do this for any task, you know,

email rest of people and clear of a CRM

cancel Rogers internet for family. You

can see that was back when I was trying

to master my English accent. That didn't

work very well. Uh looking to how to

achieve US resident status and fill out

forms. The whole idea is you're

basically taking the agent out of the

chat box and then you're actually

integrating it into the core of your

business. But believe it or not, as it

is, this isn't very valuable. I mean,

what we've done is we basically just

created a way to dump ideas in where we

haven't really created a way to extend

past that. Um the thing that makes it

valuable is when you combine that with

capture. Now, the first time I

encountered the concept of capture was

when I was reading through David Allen's

Getting Things Done from Forever ago.

And to make a long story short, what

capture means is it's just a simple and

easy and low-friction way of getting

ideas into some to-do list.

Now, we actually have our to-do list,

right? That to-do list, in essence, is

the project management shared human and

AI workspace that I showed you earlier.

Well, the valuable thing about having a

low-friction capture method is, like a

good project managers, anything that

comes to mind over the course of the

day, any task that you need to working

on, any concept or idea for anything, um

you can get into that system extremely

easily, whether or not you're on the go,

let's say, like, walking around your

city or something like that, going from

meeting to meeting, or sitting down at

the computer. So, obviously, a really

low-friction capture method is just

using linear and hot keys. So, maybe

create WikiData entry for Left Click AI

Incorporated. And maybe this is

something that, you know, I want to tag

as agent ready, create an issue for, and

then actually have it proceed with the

task like we just did a moment ago.

Well, the simplest and easiest way of

sorting this out is actually just by,

you know, binding a hot key or

something, and then, you know, adding a

task as follows. And so, now I actually

have this built into my computer. So,

maybe I'm out and about, and I don't

know, I'm watching some YouTube videos

for my daily updates, and I realize that

the thumbnail sort of generation prompt

is a little bit off. Well, now what I

can do is I can actually kick off an AI

agent workflow literally without having

to stop a beat just by opening this and

saying, "Fix thumbnails for daily

updates channel." Well, now this has

actually been sent off to my linear. And

if I wanted to tag this as AI agent

ready, you know, I could actually send

this to my agent with a brief. I just

hold command and press G. So, that's

pretty easy. I can give it some

additional information if I want. I can

tell it, um,

"Right now, the thumbnail text seems to

be a little bit small." At any point in

time, I basically have woven in AI into

my my day-to-day flow, such that it

exists with me, next to me, and I can

work on essentially whatever tasks. I

mean, if you think about it, I could

fire off 50 of these simultaneously. I

can now operate at the speed of thought.

The bottleneck is no longer, you know,

how much work can I do in a day, it's

how quickly and accurately can I scope

the work that I want done in a day. I

just ran over here to get my phone, so I

could show you what this looks like as

well. Um, you could build a shortcut

into your phone such that you can

actually just hold this button,

create a demo task in Linear,

and it'll actually go through, and I

don't know if you guys could tell, but I

just had a little chime noise, and then

I can go back over to Linear, and now

you can see that I just I just added

that in the system. Um, you know, it's

like tied to my action button, it's

extraordinarily low friction.

These little hacks, these aren't

necessary, to be clear, but the lower

friction that you make something like

this, the more you end up relying on

sort of your to-do list, your project

manager, your shared AI and human

workspace. And you can also tie this in

such that, you know, maybe if you click

the action button twice, it

automatically starts it as an agent

task, whereas if you click it once, you

reserve that for a human task. You can

also set notifications so that when, you

know, an AI agent needs you for

something, it'll actually pop a

notification up on your screen. I did

this a while back and it was super

valuable. Having push notifications when

agents are waiting on you, and then all

you do is you click on it, and then

voice transcribe, like, that is pretty

freaking sexy. And not only is it sexy,

it's also, obviously, the future of

work. If right now our ability to assess

and verify the outputs of agents is the

bottleneck, we need to make our ability

to do that as easily as humanly

possible. What's cool, too, is you get

total task visibility, so there's like a

trail, and it's persistent. It doesn't

disappear at the end of every prompt.

So, for instance, you could see here

that I actually moved a task from

waiting back to next. I removed a label

that said waiting on Nick, because, you

know, I wanted to provide some more

context to the task. And this is going

to get picked up just like any other

task. All the context is going to be fed

into this async runner, and then, you

know, things are just going to kind of

work on its own

autonomously without my direct

oversight. The third thing you need in a

system like this is you need what are

called evals.

Now, in case you guys didn't know, evals

are a set of evaluations that you run an

output through, or a model through, in

order to determine whether or not it is

giving you the sorts of things that you

want. We're essentially assessing its

performance.

And for project management and modern

AI-based productivity, you know, I

define evals as basically a standardized

checklist of steps that I run all

outputs through before they give it to

me. In that way, I know that, you know,

we have my tone of voice on every

project. We have all of the basic

LLM-isms taken out of the text, like

m-dashes and stuff like that. We have my

reasoning applied to everything. So, I

fed in a big knowledge base basically

based off my the way that I make

decisions, and I was like, "Was this a

sort of decision that I would reasonably

have made under these circumstances?"

>> [snorts]

>> And the whole idea is rather than giving

work off to AI completely and then just

hoping it does a good job, what you do

is you give it a set of guardrails

if it assesses that it's fallen out of

those guardrails, it will just iterate

and continuously retry the project until

it eventually gets within the

constraints. Once it's in the

constraints, my actual work to, I don't

know, touch it up or change something

or, I don't know, pick better YouTube

titles, pick better tasks, change the

work, whatever, is significantly

reduced. So, for instance, here's one on

the left-hand side here called visual

asset. Is this render usable? This

applies to demos, thumbnails, diagrams,

and generated imagery before it reaches

my folder. So, you know, in order for a

render to be considered waiting on Nick,

basically in order before it even gets

to the point where I'm ready to make a

Q&A check, the text has to render clean.

It needs to, like, literally visually

assess every output and determine that

there's no garbled or melted characters.

It needs to figure out, "Hey, you know,

is this in the style Nick likes?" Which

right now is this ink editorial thing,

black ink on pure white. I just love

that. Is it legible at thumbnail size?

Basically, is it like sufficiently

zoomed in? Is it faithful to the brief?

And I doesn't have the right shape? AKA,

you know, I gen my thumbnails at 1280 by

720. You know, if I do diagrams, it's at

920 1920 by 1080. Here's another one

here. Was this done the way that Nick

thinks? And this is loosely, you know,

related to the knowledge base that I fed

it in a while ago, but it's,

um, you know, "Hey, there are five

questions. You got to score each of

these zero to two. If the total is less

than seven, AKA, if one of them is like

a zero, then this whole project is a

fail and we need to continuously iterate

until it is good." So, one of my

principles is first principles. Did the

work reason from the actual mechanics of

the problem or did it just pattern match

to what people usually do?

EV discipline. Are the conclusions and

choices high expected value? Uh, if so,

then it's good. And if not, then it's

not. Is Nick's time minimized? You know,

if there's any part of this that could

have been done without Nick in the loop,

make sure to go back and do it according

to this so that Nick doesn't have to do

any additional work. Is it verified, not

plausible? Is there a leverage check?

You know, if there's a big lever ignored

because I had a skill in my workspace

that I want you to rerun this. And so, I

mean, like, this is these are tokens,

right? This isn't free, but typically

this is worth far less than the time

that I would spend getting an average

task up to snuff. And the key is, you

know, having a knowledge base of some

kind that it consults to like get the

the right shape, and then forcing that

shape into that guardrail container that

I talked about earlier. And finally,

there's quality and assurance. And this

is really where you come in as, you

know, the final, as mentioned, gate to

the task.

The reality is, the nature of work has

changed a fair amount. You know, before,

I was actually doing a lot of the work.

But now, what I'm typically doing is I'm

spending time scoping the work so that,

you know, the guardrails are set really

efficiently and effectively, as well as

a clear definition of what it is that I

want. And then I'm spending most of my

time evaluating the results of the work

doing Q&A. It's kind of a mind shift

from back when I was actually like

freelance writing in my content

marketing agency. And then later on, I

ended up managing a bunch of content

writers. It's very similar. I'm just

going one level up the abstraction

chain. So, not actually directly

involved with the deliverables. You

know, my fingers and my hands aren't the

the things that are doing the heavy

lifting, moving the products, and so on

and so forth. It's my mind assessing the

quality and ensuring that my taste sort

of applied to everything that I want.

And so, obviously, you can take what I

showed you today, and you can apply it

in any way, shape, and form. You could,

for instance, go down to the

description, expand it, show the

transcript, go to the top right-hand

corner, copy and paste the entire thing,

and put it into Fable, and it could

actually recreate something like this

fairly straightforwardly for you.

Probably without burning more than 25%

to 50% of your session limit. Um you can

do the same thing with GPT-5.6 or really

any model. This is just my own

architecture. But I just wanted to begin

the the wider conversation of how to

actually apply AI in a modern workplace.

I think a lot of people here have sort

of pieced together bits and tiny atomic

habits from various workflows that they

see on the internet, but this is a

pretty cohesive way to weave this into

your organization. Um and I know a lot

of you guys are probably going to look

at what I've done and be like, "Huh, we

could make that better." Um and that was

sort of sort of the idea. I'm sure there

are many ways you can make this better,

and I'm keen to see, you know, what you

guys do with us. Since implementing this

inside of Left Click, uh Clervo, uh and

Maker School, you know, my day-to-day

responsibility has also shifted a fair

amount, too. Rather than necessarily be

involved with every little thing, I've

sort of had to go up one level of

abstraction, and then be responsible for

the the fleet of managed agents,

essentially. I've also had to start

thinking about things like my token

budgets. These are things that I never

used to think about before because I was

typically constrained to operating

within one little like terminal window,

having some input go in, and then

waiting for the output to come out. I've

also become significantly more

productive because no longer do I have

to just sit and wait for an output. Um

you know, I can just fire off outputs

basically about as fast as the speed of

thought. And I think that's a major

unlock. You have like a little ideation

session, you sit down, ideate the top

five tasks that need to be done, assign

agents to as many of these as can be

done. They do a bunch of pre-lifting

while you focus on something else, and

then maybe three or four times

throughout the day, every couple of

hours, you check back in on the outputs.

Um that sort of gets you out of just

staring at the terminals all day.

Actually gets you, I don't know,

building relationships with key vendors,

selling, which is obviously the number

one lever that any founder or, you know,

top-level executive in a company really

needs to be doing, and so on and so

forth. Okay, if you like this sort of

thing, I actually have a bunch of files

down below in the description, uh in the

line that says Maker Zero, where I you

can just copy and paste everything that

I write there to an agent, and you can

have it do it just like that transcript

idea earlier. Um I also want to make it

clear that this isn't prescriptive. I'm

not telling you have to do it all

linear, you have to do it in a

particular way. But this is just how I'm

managing it in my organization and the

results that I'm seeing. And if you want

to learn how to implement strategies

like this into an AI and automation

service business, basically a business

where you get paid to build things like

this for other people, definitely check

out Maker School, as well. It's my

90-day automation road map, where I

actually guide you through everything

you need to do to start as a total

beginner and actually get your first

client. And I guarantee you that first

client in 90 days, or I give you all

your money back. It's very

straightforward, it's very tried and

tested, and it's been finished by over

10,000 people to date. Thank you very

much for watching. I'm looking forward

to seeing all y'all in the next video.