Dan Martell - July 15, 2026


You’re Not Behind (Yet)： How to Build Your First AI Agent (Full Guide)


Episode Stats


Length

22 minutes

Words per minute

232.82

Word count

5,215

Sentence count

128


Summary

Summaries generated with gmurro/bart-large-finetuned-filtered-spotify-podcast-summ .

Transcript

Transcript generated with Whisper (turbo).
00:00:00.080 I just read a study that by 2030,
00:00:02.280 AI is going to create 170 million new jobs,
00:00:05.560 but they won't be jobs where you just sit there
00:00:07.400 and chat with AI.
00:00:08.480 There'll be jobs where you build AI agents.
00:00:11.200 And I get it, the AI space is moving crazy fast.
00:00:14.000 I mean, what even is an AI agent?
00:00:16.360 Not too long ago, I was right there with you.
00:00:18.440 But after going deep myself and building dozens of agents,
00:00:21.880 I found out it's actually way easier to build
00:00:24.040 and manage these agents than it looks.
00:00:25.920 So much so that my whole team and I have hundreds
00:00:28.840 with AI agents doing 92% of all the work across my companies.
00:00:32.640 So today, we're gonna go through every step
00:00:34.800 on how you can build your first AI agent,
00:00:36.720 starting with AI chatbot versus AI agent.
00:00:41.040 A chat is like a meeting.
00:00:43.000 An agent is like an employee.
00:00:44.800 Chat is you ask it a question and then you get an answer.
00:00:47.260 And a lot of people just copy and paste things
00:00:48.760 and do something with it.
00:00:49.600 With an agent, you actually tell it what you want to do
00:00:52.420 and it runs the full workflow.
00:00:54.420 Think of it like these are the body parts.
00:00:56.400 I call it data.
00:00:57.640 So one is D, it can diagnose.
00:01:00.420 It can actually figure out what the problem is
00:01:02.900 and solve it on your behalf.
00:01:04.280 Kind of like hiring a consultant.
00:01:05.660 Next is A, it can assemble.
00:01:07.920 It can build a plan.
00:01:09.360 It can design tools.
00:01:10.680 In that way, I think of it like an architect.
00:01:13.220 It knows all the different pieces
00:01:14.760 that it can pull together to get something done.
00:01:16.780 Next, we have T, it can take action.
00:01:19.560 And that way, I think about it
00:01:20.460 like somebody that executes tasks.
00:01:22.760 And finally, A, it can assess.
00:01:25.560 It can check its own work,
00:01:27.220 see where the opportunities are,
00:01:28.920 and then make sure that it landed on the right answer.
00:01:31.060 And if not, it can review itself and make itself better.
00:01:33.760 This whole thing is called a loop.
00:01:35.720 And without a loop, an agent would just do the job
00:01:38.260 and then stop, that's called an automation.
00:01:40.260 But with an agent, it keeps learning,
00:01:42.020 it keeps getting better, it kind of acts like a person.
00:01:44.840 With chat, it pulls on us, it's ask us,
00:01:47.140 what do you want me to do?
00:01:48.040 We prompt it and then we wait.
00:01:49.560 With an agent, it pushes on us.
00:01:51.280 It's doing things and changing things all the time
00:01:53.460 and it's checking in to make sure
00:01:54.820 that it did it the right way.
00:01:55.860 so you might be able to buy back your time with chat but you'll actually learn to let go of whole
00:02:00.180 areas with an agent but how do we even know if it's worth giving something to an agent instead
00:02:04.660 of just doing ourselves for that i use the rule of r the first one is repetitive is this a task
00:02:10.340 that i'm going to do every week two is rules base does it take the same input and generate the same
00:02:15.300 output every time the third is does it generate a return on my time for the amount of time it takes
00:02:20.020 me to build this thing i'll show you how will i actually get my time back if the task takes two
00:02:24.660 minutes but it would take me two weeks to build this agent how about i just keep doing the two
00:02:28.100 minute task but if you think about it and the task is only done once in a while doesn't follow
00:02:32.820 a clear process or get to a specific outcome and doesn't save you more time to automate it than
00:02:37.540 just doing it manually then stick with what you got use the chat so now that we know the difference
00:02:42.420 between chat and agents how do we build one to make an agent it's super easy and i even turned
00:02:47.860 it into an acronym called agent and the first step is a which means aim for a specific outcome
00:02:55.060 when i'm sitting down and i'm like i want to build an agent for this i have to first ask myself what
00:02:59.780 is the specific goal start with the outcome the agent is going to give you it's like if i'm
00:03:04.980 climbing a mountain taking a step is the task getting to the top is the outcome i want to define
00:03:10.340 the outcome and be really crystal clear because the cool part with ai and agents is that the ai
00:03:15.380 can actually figure its way there this is why creating ai agents is hard for people because
00:03:20.340 they want to control every step but the truth is it may know how to get there way better than
00:03:23.940 you can figure it out think about it like when you hire a person you say here's your job when
00:03:27.860 they applied for the job they had these specific outcomes that they would need to accomplish like
00:03:32.180 grow the business or get more customers or sell and get people to buy from you those are the
00:03:36.660 outcomes you don't start by telling them how to do the job you tell them what you're gonna need
00:03:40.500 from them that's the outcome aim the agent at the outcome you're looking for so like how do we make
00:03:46.340 sure we're being clear to the agent about what kind of outcome we want to achieve the first is
00:03:50.020 we got to give it the why before the how tell it why you're trying to achieve the goal so that it
00:03:55.460 can make some smart decision on its own to make this really easy for you i'm gonna use an example
00:04:00.900 we're gonna build together an agent to manage your inbox as an outcome i would prompt it and say i
00:04:06.500 need to spend less time managing my email inbox see how i'm not telling how to do it yet i'm just
00:04:11.140 saying this is the outcome the second is we have to write what's called a dod or a definition of
00:04:16.820 done it's giving them the instructions to know if they achieve the thing we want to be specific we
00:04:21.060 want it measurable ideally you have it in one sentence so for example building our agent for
00:04:25.620 our inbox we would not say handle my emails instead we would say done means every morning
00:04:30.900 at 9 a.m the inbox is empty replies are drafted in my voice and anything that needs me is flagged
00:04:36.180 to the top and nothing important slips if you can't picture it done the agent can't hit it it's
00:04:41.700 like a target they can't see and finally we got to start with the end and it's called reverse
00:04:45.940 prompting but we want to tell it the results that you want then we tell it to ask you the question
00:04:51.540 it needs to get full clarity this is the advancement this is what nobody out there is
00:04:55.460 teaching you then we let the ai do its thing because it's better than us and a lot of stuff
00:05:00.340 and it builds the plan itself and the truth is if we can't state the outcome in one sentence we're
00:05:05.300 not ready to build if you can talk the task like explain to somebody else then the ai can do the
00:05:10.580 task and the cool part is you knowing this already puts you ahead of most people using ai today even
00:05:16.260 folks you're like oh this person's so smart they don't know this stuff and we're just getting
00:05:19.860 started so we've got the agent it has its reason we have a clear target and now it has clarity and
00:05:26.100 now the next step is g give it an identity truthfully out of the box ai knows a little bit
00:05:32.820 about everything but it doesn't know anything specifically well so an identity allows us to
00:05:38.580 focus its power in the right expertise so when we build the identity instead of it knowing a
00:05:43.380 little bit about everything it gets really sharp about that one thing that you've hired slash built
00:05:47.700 it to do and the best part is that the tighter we define who it is the better it works the better
00:05:53.140 the outcome is the better the agent is an agent i remember reading a report where they built a
00:05:57.780 bunch of ai agents to do customer support for an airline and then they removed all the rule books
00:06:02.980 its identity from the agent and it dropped from 33 success rate down to 11 so we're talking same
00:06:09.700 model same task same request and it got three times stupider because it forgot who it was think
00:06:15.540 of your agent as a genius and he's sitting at a desk and he's wearing a blue shirt and he's got
00:06:20.100 great hair this genius has infinite potential but until you tell them the job they just sit there
00:06:26.100 doing nothing because they don't know what they're supposed to do so what we need to do
00:06:29.220 is tell it what his job description is and set some rules for how to do the work so this is how
00:06:33.300 we create the agent's job description using three plain english files the first one is the soul
00:06:39.220 file right it's the agent's personality i have a lot of fun when i create my agents i tell it
00:06:44.580 what kind of quirks i want what kind of values does it have how does it talk it's essentially
00:06:49.060 defining how it behaves the second file is the identity file that's its dna that's its name
00:06:55.220 that's a description of its role for example one of my primary agents his name is kai i just worked
00:07:01.540 with him for two weeks and we built a bunch of stuff and i said hey man it's time for you to
00:07:05.540 give yourself a name because i feel weird not knowing who you are and he's like oh how about
00:07:10.500 this and here's why and he gave me all the reasons and i said cool update your identity file so now
00:07:15.380 he knows who he is to the world the third is the user file and this is the context your agent needs
00:07:21.060 to know with you it knows who it's going to be interacting with so it can adjust its loops to
00:07:25.460 get better for you so for example in this file you might have your goals your role how you like
00:07:30.020 things done but essentially it defines who we are the sole file is how it behaves the identity file
00:07:35.540 is who it is and then the user file is who we are now here's a pro tip don't write these files
00:07:40.900 yourself no no no let's tell ai to write it as we build the inbox agent here's the prompt that
00:07:46.580 you use to generate them i want to build an ai agent that runs my inbox your aim from the previous
00:07:51.860 step we insert that there create its three identity files a sole file an identity file
00:07:56.580 and a user file and ask me any question you need to fill these in accurately then write all three
00:08:01.460 notice we did the reverse prompting where we asked it to ask us questions so now it'll go do
00:08:06.740 the research and then it'll hand back a template that is 99 awesome and complete for example here's
00:08:13.140 Here's what our inbox agent identity files
00:08:14.980 might look like after the AI interviews you.
00:08:17.740 Soul file, how it behaves.
00:08:19.420 Writes in my voice, concise, direct, zero corporate fluff.
00:08:23.900 Calm and reassuring, never pushy or salesy.
00:08:27.100 And avoids phrases like,
00:08:28.480 I hope this email finds you well.
00:08:30.320 Of course it found you well.
00:08:31.380 When it's unsure, it flags instead of guessing.
00:08:34.380 Identity file, who it is.
00:08:35.920 It has its name, Amelia.
00:08:38.100 E-mail-ing-ya.
00:08:39.700 See what it did there?
00:08:40.480 Isn't it cool?
00:08:41.040 It's got personality.
00:08:41.760 The role, personal inbox manager.
00:08:44.440 The job, you read, you sort,
00:08:46.940 you draft replies to every new email.
00:08:48.880 Lane, this is the parameters.
00:08:50.680 Inbox only, never touch my calendar.
00:08:52.960 Don't you touch my money or anything outside my email.
00:08:55.800 Now we got the user file, who it works for.
00:08:58.040 I'm a founder who gets around 100 emails a day.
00:09:00.580 We prioritize people, my team, my current clients,
00:09:03.600 my VIP list.
00:09:04.640 I have multiple AI companies, a media company,
00:09:07.100 it might list them all.
00:09:08.020 With these three files,
00:09:09.080 our inbox agent knows how to behave,
00:09:10.900 who it is, and who it's working for.
00:09:13.220 And look, building one agent changes how we work.
00:09:15.460 But if you're a CEO or founder,
00:09:16.980 the real unlock is a whole team of them.
00:09:19.480 That's why I put together my full AI company OS playbook.
00:09:22.340 It's the best way to plug AI agents
00:09:23.780 into every single department in your business.
00:09:26.140 If you want it, just DM me the word AI business on Instagram
00:09:28.940 and I'll send it right over.
00:09:29.960 So now our agent knows the job it needs to do,
00:09:32.140 but we haven't given it the necessary tools
00:09:34.040 to do the job with.
00:09:35.240 This is where we gotta go to E, which is equip it.
00:09:38.500 Like any human team, an agent is gonna need some context.
00:09:42.400 It's gonna need some tools.
00:09:43.540 It's gonna need some logins to systems
00:09:45.340 so it can actually do its work.
00:09:46.440 When we give our agent the context, the history,
00:09:48.880 the data, the tools,
00:09:50.020 that's actually when it gets to do the real work.
00:09:52.260 And in all agent design, the context is the moat
00:09:55.740 because garbage context in, garbage context out.
00:09:59.120 Think of this whole desk
00:10:00.420 as what's called the context window.
00:10:02.360 I am the AI, the LLM, and I'm the genius,
00:10:05.560 and I'm sitting at the desk.
00:10:06.500 Over here, I've got my playbooks.
00:10:09.000 These are the processes and procedures
00:10:10.540 on how to do my work.
00:10:11.780 On top of it, I've placed my identity files,
00:10:14.120 the things we just created so that I understand
00:10:16.040 how I'm supposed to behave and who I'm working for.
00:10:18.680 This is like my constitution.
00:10:20.580 And then over here, I've got the tools.
00:10:23.240 These are the laptops, the monitor, the mouse,
00:10:25.880 anything I need to use to connect to other systems.
00:10:28.040 And above that, I've got my loops.
00:10:30.020 These are the schedules, the heartbeat
00:10:31.800 that I talked about earlier so that I know
00:10:33.520 when I'm supposed to get things done by.
00:10:35.100 It's like the calendar.
00:10:36.220 it's my schedule and then under the desk is where i have my filing cabinets this is my memory this
00:10:41.740 is where things that can't fit on my desk sit so that it's available but i'm not creating clutter
00:10:47.580 on my desk if you've ever heard a context rot that's when you just load the desk with a bunch
00:10:52.620 of files and it becomes complicated and i can't find things quickly and all of a sudden i'm
00:10:56.060 answering questions but i'm not clear about it because i'm not certain about it whereas a clear
00:10:59.740 context window is when everything on the desk is neatly put away so that i can refer to it so that's
00:11:05.980 why we have to equip our agent with the right context so now that we're here how do we equip
00:11:11.660 the genius agent with all the right context and the tools first off we have to capture our processes
00:11:17.100 so we can let it know how to do the work for this i've got two ways the first way which i've been
00:11:21.660 teaching forever not the best way is the camcorder method you do the work you record yourself using
00:11:26.780 zoom video or any kind of recording software and then you can give that to an ai to turn it into
00:11:31.820 a playbook and then you feed that to the agent as like a procedure think about our inbox it's like
00:11:36.460 do you have a documented process for how to label your emails and triage your emails and write
00:11:41.020 replies on your behalf just make sure that when you're recording yourself you're talking through
00:11:44.620 the task so that when the ai takes that to create the playbook it has all the details the better
00:11:49.100 way and this is my recommendation is to reverse engineer it from the source if i'm building an
00:11:54.300 agent to manage my inbox i can actually connect using the connector tool to my email in my case
00:11:59.900 gmail and ask the ai to reverse engineer and create a playbook based on historical emails
00:12:06.540 see you've already been in your inbox replying and doing stuff the ai can actually use that to
00:12:10.860 train itself and that is actually the way i build most of my agents if i have the source information
00:12:15.820 i just ask it to learn how i've done it in the past and then create a procedure go find the
00:12:19.820 pattern go find the best practices go find the little intricacies based on how i've done it and
00:12:24.060 all the people in the relationships and you write that file so for example if you want the prompt
00:12:28.060 to do this here's what you write connect to my email read the last 50 messages that i've sent
00:12:32.700 study how i actually write my tone my greetings how i do sign offs how long my sentences are the
00:12:38.380 phrases i use most often then write a style guide that captures my voice and tone and to test it
00:12:43.020 ask it to draft a reply on your newest emails that are unread as you based on what it learned then
00:12:48.060 you can rewrite those so that it can use that to learn and tighten it up like it already knew who
00:12:52.940 it was in the best practice based on its research that's in the soul file but now it has clear
00:12:57.500 templates the step-by-step instructions and even examples that it can use to do this on your behalf
00:13:02.460 so now that it's captured all the information it still hasn't kind of solidified it into an actual
00:13:07.180 playbook and that's what we call a system prompt so then what you do is for each sub process in
00:13:11.900 the agent's activities like drafting emails but maybe it needs to sort emails you can have it do
00:13:16.540 the same activity either you tell how to do it or it researches and then it creates all these system
00:13:21.980 prompts based on the work you need it to do. Like I have it for my inbox, sort, reply, forward,
00:13:28.100 that's a big one, and even escalate things that it needs to show me and the reporting I want every
00:13:32.660 day. So then at this point, you actually have an AI agent running. This is exciting stuff.
00:13:37.980 You might feel right now, you're like, oh man, I'm going to give everything I got at it. Don't do
00:13:41.700 that. The N in the agent framework is to narrow the scope. The agent needs to have a narrow scope
00:13:48.160 of what it does so it doesn't confuse itself if you start asking it to do 17 other things then
00:13:52.640 all of a sudden this desk can get really busy which means it's not going to be a great agent
00:13:56.720 anymore just like you wouldn't give your administrative assistant the responsibility
00:14:01.120 to do marketing and take sales calls you want to make sure the scope is narrow for each agent
00:14:05.760 as an example i have an agent that writes code and then i have an agent that reviews code and
00:14:09.760 those are separate agents and they work together see how narrow the scope is we need to focus the
00:14:14.720 agent down to one specialist per job each agent great at one thing instead of having one agent do
00:14:21.040 everything which is what people usually do that's a mistake we'll have sub agents that do specialized
00:14:26.400 tasks under it that way it keeps all the context for the agent super clean it doesn't get confused
00:14:31.840 we don't have context rod we don't want to have a mega agent instead we need to spread out the task
00:14:37.120 to other sub agents so that it can handle other agents below it so for example kai who's like my
00:14:42.800 orchestration agent he's the one that not only creates other agents he also coordinates the
00:14:47.680 tasks of the different agents like my research agent and my relationship agent and my coding agent
00:14:52.480 and my reporting agent he then he pulls it all together and gives me answers so instead of giving
00:14:57.520 every task to one agent this is what we should do instead we build a manager agent its only job
00:15:02.960 is literally to manage and specialize in the management of the sub-agents think of it like
00:15:07.280 a real manager agent you are my manager agent i need you to manage my sub-agents and i need you
00:15:12.160 to make sure that you monitor the jobs and make sure they're moving along and if they're not
00:15:15.840 working you fix them and you decide what agents need to exist so for example we built our inbox
00:15:20.800 agent but we don't want to have to manage the inbox agent we create a manager agent that talks
00:15:25.600 to the inbox agent that might be responsible for a lot of different things like our inbox but also
00:15:30.320 sending stuff to other people on our team but we want to make sure each sub agent reports to that
00:15:34.480 manager agent so that it takes care of it so you might want to give it a prompt like this you're
00:15:38.480 my manager agent you never do any tasks yourself when it comes in you only move it to other sub
00:15:43.760 agents that are dedicated for that one specific job you hand it the task and then you let it run
00:15:49.120 so it's like one agent one lane and if a job touches multiple areas split it into the separate
00:15:54.160 sub agents one per area you're the one that coordinates and reports back to me like i said
00:15:59.200 mine's called kai he's awesome i talked to kai kai talks to all the sub agents i have one agent i
00:16:05.120 I got to talk to. If you want a pro tip, and I don't want to overwhelm you, but there's different
00:16:08.880 AI models. So for example, within Anthropic, you have Haiku. This is like for simple and high
00:16:14.780 volume stuff. If you want to sort things, you want to label things, quick draft, and it's the cheapest.
00:16:18.800 Then you might go to Sonnet. Sonnet's great for like day-to-day work, research, writing most code.
00:16:23.220 At a higher level, you've got Opus. This is a powerful model, good at reasoning, complex builds,
00:16:28.260 being a manager of agents. But now you have Fable, and that just dropped a few weeks ago. That's more
00:16:32.740 like an orchestrator a consultant it has full capabilities of opus but it's even more state
00:16:38.660 of the art it's extremely good at long running tasks and real complex things when you don't have
00:16:43.620 a lot of information to give it but it's the most expensive so depending on your task you might want
00:16:48.740 to give it different models because it'll cost less and it may not need that level of horsepower
00:16:52.660 to get the work done so for example my inbox agent since it's always running every 15 minutes
00:16:56.900 i just use sonnet because i don't need an opus level genius to run a process that we've already
00:17:01.380 defined to build the agent i might use opus that way it helps me create it i might even use fable
00:17:06.660 but then to run it i'm gonna run it on sonnet one time i had to do this whole refactor on my code
00:17:11.060 base and i could use a powerful model like opus it probably would have cost me 150 bucks instead
00:17:16.020 i use haiku and it only cost me a dollar fifty as of today here's a chart with gpt and other ai
00:17:21.700 equivalents that it's on screen so you can just take a screenshot of it to help guide you but
00:17:25.060 this is now changing every couple weeks if you've made it this far and you're still interested
00:17:30.020 congratulations but i need you to know something you're literally ahead of 99.999 percent of the
00:17:35.380 people out there and you're crushing it we've learned to aim the agent at an outcome give it
00:17:39.940 an identity so it knows its job equip it with the right context and tools so it can do the job and
00:17:44.740 narrow the scope so it doesn't get overwhelmed and instead use sub agents to accomplish specific tasks
00:17:50.500 now this last step is where our agent truly becomes autonomous t and it stands for trust
00:17:55.940 because we got to do it in stages.
00:17:58.500 Building an agent is actually the easy part.
00:18:00.720 Once you understand how to do that and you prompt it,
00:18:02.600 it just gets done.
00:18:03.540 The scary part is letting it act without us.
00:18:06.440 And I understand, especially as we talk about our inbox,
00:18:08.760 having somebody else write emails as you calm down.
00:18:11.940 I'm not doing that.
00:18:12.760 I'd rather it give me some ideas for copy.
00:18:14.760 The truth is, is we don't give the agent
00:18:16.380 the keys to the car on day one.
00:18:18.200 And what we do is we like give it stuff, see what it does.
00:18:20.440 Then we see if its response is what we expected.
00:18:22.560 If we do this right, you sleep well at night.
00:18:25.120 If you don't, you will not sleep.
00:18:27.500 The whole point of creating an agent
00:18:28.960 is so that you can go do other stuff.
00:18:30.940 If you're sitting there babysitting
00:18:32.080 or worrying about it all the time, it doesn't help you.
00:18:34.140 So up until now, we've let the agent
00:18:36.620 help us manage some emails.
00:18:38.840 Think about it.
00:18:39.360 First, you might sure it's doing his job properly.
00:18:40.920 When we tested it to write those drafts to unread emails
00:18:43.660 and we looked at how it did it.
00:18:44.900 At first, we're micromanaging him a lot,
00:18:47.400 but then we gotta learn to trust in stages.
00:18:49.940 So maybe the first stage is just like,
00:18:51.580 hey, can you sort the email?
00:18:52.940 And then we see what it does and we're like,
00:18:54.060 okay, that's good.
00:18:54.680 Then we like ask him to do more drafts.
00:18:56.660 So we already tested it, but now let's let it really do it.
00:18:59.040 So now it's running drafts and we're like,
00:19:00.600 okay, I like those drafts, change this, do this.
00:19:02.840 Okay, now it's doing its thing.
00:19:04.100 Then we might let it start sending emails on our behalf,
00:19:06.540 but not all of them.
00:19:07.560 Maybe just even forwarding emails to finance,
00:19:09.860 to our team, because it has the logic.
00:19:11.760 It saw how we handled those emails in the past.
00:19:13.720 Maybe it categorized certain emails
00:19:15.240 like Slack notifications into a specific label.
00:19:17.340 But eventually we want this genius
00:19:19.740 to manage our whole inbox without us even opening it.
00:19:23.220 that's the equivalent of us leaving the room and having the agent at the desk do all the work for
00:19:28.600 us because at this step we learn to let go we've trusted it fully because if you don't do this it's
00:19:34.860 like hiring a driver to drive your car and you got your hand on the wheel now we got to take our hand
00:19:39.060 off the wheel and let the driver drive here's how you can do it in a really safe way you set the
00:19:43.780 guardrails first you can actually set that up in its identity files what is it capable to do on
00:19:47.780 our behalf maybe it has the ability to spend money maybe it has the ability to make decisions maybe
00:19:51.820 has the ability to write drafts only not send yet it's always your call and you can define those
00:19:56.380 two approve everything at first i've never created an agent and was like yolo go nuts no
00:20:02.380 show me what you would do i like what you did do it again tweak it just like i just talked about
00:20:07.020 for our inbox agent third we loosen the leash right it's like a dog you're walking with and
00:20:11.980 you're like hey i trust you more i trust you more and all of a sudden the leash goes limp but he
00:20:15.260 still holds the heel and then four would be give it a heartbeat that it can run on its own set up
00:20:20.860 that schedule that reoccurring tasks so maybe before did it once and you reviewed everything
00:20:26.220 now i might do it every 15 minutes you know every morning at 9 a.m it did it once now why are we
00:20:30.940 waiting why we wait until the next day why don't we have it run all the time this process is scary
00:20:35.420 but the whole point of learning to let go is to buy back our time to have the agent do the work
00:20:40.220 for us and learning to let go is part of the process if you trust so for example when i showed
00:20:46.700 this agent to my executive assistant she thought she was out of a job instead it actually freed
00:20:51.020 her up to do things that actually mattered not sorting emails and writing drafts or telling me
00:20:55.660 what's in there the ai can do that i'd rather pay her to do higher quality work manage higher level
00:21:01.500 projects then we rolled out the same system to the whole team i taught everybody how to do this
00:21:05.660 now i want to say congratulations we just tackled the topic that most people don't even want to
00:21:10.780 learn they're like that's not for me i hear about agents i don't get it i'm confused but no you
00:21:15.180 didn't you went all the way till the end and i want you to understand that you might feel a little
00:21:19.260 behind in this ai world but here's where i've gotten to i've accepted that i will always feel
00:21:23.900 behind and i could never be on top of all of it but you just learned a strategy a shift a different
00:21:29.340 way of doing work that if you can learn how to direct the ai you will co-create with it if you
00:21:34.780 don't don't be surprised if one day you might be working for it remember the rules of art repetitive
00:21:39.740 rules base and return on time that's where we want to start looking for opportunities to put
00:21:43.580 an agent in there instead of you keep doing it and i'm gonna give you the pro tip of all pro
00:21:47.500 tips you grab the link to this video you give it to your ai and you tell it to use everything
00:21:52.860 i've shared to create the ai for you and watch it cook because it can do it now here's what i
00:21:58.860 want to know from you we have some fun below in the comments answer this question if an ai agent
00:22:03.740 could manage your inbox and buy you back all this time scheduling things on your behalf what would
00:22:08.300 you have more time for i'm curious post a comment below and let me know and if you want my whole
00:22:12.060 system, the playbook that I use to manage AI and all my different businesses, just DM me the word
00:22:16.920 AI business on Instagram and I'll send it right over. And if you want to know what AI businesses
00:22:21.080 are worth starting in 2026, click here and I'll see you on the other side.