November 26, 2025 • 17 mins
A friendly, plain-English tour of agentic AI that shows how it plans steps, uses safe tools, and finishes real tasks with simple rules and approvals.⚡⚡https://wilwaldon.com⚡⚡
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
This is the Elon Musk Podcast, your daily hit of what is really
going on at Tesla, SpaceX X AI, and the rest of the Musk
universe. I'm your host Will Walden, and I
have covered Elon Musk for more than five years, spent a year on
the ground at SpaceX, Starbase during early Starship
development, and before this I spent my career as a software
developer working with billion dollar companies.
(00:22):
I've also built and sold my own businesses and now I make
content and help other people grow their companies.
Now on this show I used that experience to break down the
news, filter out all the noise, and give you clear context.
You can actually use a Gentek AIgives an AI system the ability
(00:45):
to take small sensible steps towards a goal.
Instead of stopping after one single reply, think of it as
moving from a one time answer toa finished task with receipts
that you can actually review. Businesses use it to book
travel, organize calendars, clean out spreadsheets, and
draft polite emails that follow company rules.
(01:07):
And you get outcomes rather thanlose suggestions with a record
of how each decision was actually happening.
And how does it actually work ineveryday life.
Though in this episode, you'll get a plain English definition,
a simple loop that you can picture in your head, and a few
real world stories you can adaptat home or at work.
And we're going to talk about what it does well, where it
(01:28):
needs clear instructions of how to keep control with approvals,
spending caps, and time limits. And by the end, you'll know how
to start small, test safely, andgrow with confidence.
And we're going to get into thatright after this short
commercial break. Here is the short definition of
(01:49):
agentic AI. It is a system that pursues a
goal through several steps, using tools along the way, until
it succeeds or runs out of allowed attempts.
Regular chat gives you one response.
Then you ask it again, and againand again and again.
You get a bunch of responses. An agent continues on its own
(02:10):
within the rules that you set for it.
That shift from answers to outcomes changes how you design
tasks, how you approve actions, and how you judge success.
Now picture a helpful assistant with a checklist.
You say plan a family weekend that fits a budget, includes a
museum visit. Keep Sunday afternoon free.
(02:33):
And the assistant breaks the goal into steps, chooses the
next step, uses the right apps, writes down what it found, and
it decides what to do next. In agentic AI, the assistant is
the model. The apps are tools like a
calendar, a map, a price look up, or your notes.
In the written record is a log you can read later.
(02:53):
System repeats this plan, act and check pattern until it
reaches the goal or asks you forhelp.
It may fail and the main parts are easy to remember.
First, the goal, which states what done looks like in simple
terms such as stay under $600, keep walking time short, and
pick refundable options. Second, a planner which turns
(03:15):
the goal into a short list of small steps.
Third, tools which are safe buttons the agent can push like
search hotels, read a spreadsheet, or add an event.
4th. Memory, which stores notes from
each step so the next step does not forget what just happened.
5th. A checker which asks if the last
step helped or hurt. 6th. A stop rule which ends the run
(03:39):
when the job is done, when a limit is hit, or when the system
needs your approval, or when it completely fails.
Now let us walk through a travelexample without any jargon.
You tell the agent plan a 2 day Austin trip for two adults, keep
lodging out of $200 per night, include a live music event, and
leave time for some nice Texas BBQ.
(04:03):
Now the planner lays out steps like find dates, look up hotels,
check refund policies, search music events, draft the
schedule, and prepare a summary for you.
The agent goes to work. It uses a hotel lookup tool,
writes down choices for you, compares prices for your budget,
(04:25):
and drops the top pick onto a calendar for you.
And after each step, the checkerasks if the plan still fits the
rules. Now the stop rule ends the loop
when the schedule, budget, and refund notes meet that goal.
Then the agent hands you a clearsummary and a log of every
single action that happened there.
And the style of AI works best on chores with clear rules,
(04:48):
repeatable steps, and measurableresults.
Reading bills, copying key numbers into a tracker, and
writing a short status note fitswell because each step has a
right or wrong outcome. Drafting polite replies to
common messages fits well because examples teach the tone
and structure. open-ended tasks with fuzzy goals like make
(05:08):
something creative for the website isn't a good one.
It's not a good prompt for that.It can drift because the success
target is not clear. You guide the agent by setting a
very narrow goal. List rules in plain English and
name the tools it may use. Now you got to think about
(05:29):
control and safety. It's all within this loop, but
not at the end. You give the agent only approved
tools like a read only calendar or hotel search that cannot buy
anything. You add spending caps, time
limits, and a maximum number of steps.
You require human approval for anything irreversible, such as
purpose purchases, deletions, ormessages to customers.
(05:52):
You keep private information safe by redacting secrets,
limiting who the agent can contact, and preventing it from
pasting data into public sites. These basic moves act like
seatbelts, locks, and curfews for a very fast helper, and the
helper can do these tasks withinseconds sometimes.
So you have to have rules. Now there are a few simple
(06:14):
shapes for how agents works. One helper handles a straight
path, like reading a form, filling a tracker, and writing a
summary about it. A planner and a doer split the
job, which reduces trial and error and keeps the log knee.
A small team uses a supervisor to route tasks to specialists,
like a calendar specialist, a data specialist, and a writing
(06:36):
specialist. Many teams do well with the
planner and do repair because itstays simple, exposes the
decision points, and makes reviews really fast.
Now you do not need a big platform to start.
Think of tools as small safe buttons with clear labels and
clear limits. One button reads a file, 1
button looks up a price, 1 button adds a calendar entry.
(06:57):
Each button returns a simple result that the agent can
understand, and the log shows which button the agent pressed,
which words it sent into the button, and what came back.
If something looks odd, you can replay the run and see exactly
where it went off track and you can figure out how to fix that.
Humans are still needed at this point with AI.
(07:18):
Some are really good. I run an agent sometime to get
me the latest news on Elon Musk and it does wonders for me.
Now. The process that it takes is
very straightforward. I say check these amount of news
sources, check 20 news sources, etcetera, etcetera, and then
(07:41):
send me the links. It's very simple.
My my agent is very simple. Send me the links, give me a
like a three bullet point rundown of what the article is
about. See if it's worth my time.
I'm going to redevelop this agent into something a little
bit more robust. So it's easier for me to do this
(08:02):
podcast. I've been doing this podcast for
a very, very long time, five years or so thousand episodes
plus. So if you are a fan of the show,
thank you, and if you aren't, thank you for stopping by and
listening. And also, since you are a new
fan to the show, please take a second and hit the follow
(08:24):
button. That'll be really helpful.
Or pick up some merch at starshipshirts.com.
That helps out tremendously. So agents are really cool
because you can measure an agentthe same way you measure a
person who handles a task. Then it finished the task, yes
or no. How long did it take?
(08:46):
How much did it cost to run it? How often did it ask for help?
Now those numbers give you a clear picture of whether the
system saves time or actually creates more work for you.
When I first started developing my agent, I was, it was taking
me more time than it than it wasbefore I had it just to get it
(09:07):
right. But by fine tuning it, I have a
clear path to, you know, the best results for the day.
I can't just Google things. You get a bunch of just junk in
there and you can't just chat TBT things because it doesn't
give you the right answers. So you have to make something.
I had to make something custom. I had to make a custom agent to
(09:29):
do the things that I wanted to do.
And it has a very simple clear task.
And then it doesn't fail anymorebecause it knows exactly what to
do, has like 4 things it needs to do.
Search the web on certain sites.Send me back the headline.
Send me back three bullet pointsabout the article because it
(09:50):
reads the article. Send back three bullet points of
the article. Send me a link to the news piece
so I can check it out myself. And that's it.
That's all it does. And then I read everything I can
and come up with stuff on my own.
So my agent is very simple. You can make something
absolutely complex. Crazy, you know?
(10:11):
How often does it ask for help? That's the important one, right?
So those numbers give you a clear picture of whether the
system saves time or creates a huge headache for you.
And if it creates the headache, either work on it more or find
another process. I had to go through 5 or 6
different processes in order formy agent to work properly.
(10:34):
If you have the time, great. If not, there are systems out
there that will make you an agent.
Or you can do like a drag and drop agent for this kind of
stuff. And you have to treat each run
like a transaction with a resultyou can accept or reject because
each one of these costs compute time or tokens.
If you do it on something, you know where somebody else hosts
(10:56):
the LLM. And when you reject a result,
add a quick note about what wentwrong and then turn that note
into a rule the agent can followthe next time.
And let's talk about like a story from an office because
that might put it into perspective.
So customer support, we all hateit, right?
(11:20):
We all hate going through customer support.
If something goes wrong, we wantto answer now.
But if you're on the other end where you're a customer support
agent, inbox assistance are important.
And if you have a goal, the goalwould say, read each new
message, match it to one of fivecommon issues.
Suggest a reply that follows theplaybook, which is, you know,
(11:43):
you have a playbook that you feed it and hand anything
unusual to a person. So the agent opens an e-mail,
finds key details, checks the playbook, drafts a reply and
puts it into a queue for a person to review.
And if the person reviews it andit needs a little bit of help,
(12:05):
type in there, you know, sorry, sorry you're having this issue.
My name is Will. I'm here to help you.
And that person confirms that the draft uses the right issue
type and the right tone. So it's basically a huge
database of things like the playbook.
Now, a person gives a quick approval on the first week of
use, then reduces approvals to only high stakes messages once
(12:29):
the numbers look steady. So you're training this model
while it's doing its job. Now, another one we can think
about is where you mix tools andapprovals.
So if you're a finance helper and it could check small
purchase requests, the goal saysensure the request sits within
(12:53):
the budget, confirm the vendor is on the approved list and
prepare a draft purchase order. The agent reads the request,
looks up the budget balance, matches the vendor name, and
fills a form. The stop rule blocks any e-mail
to a vendor until a human clicksapprove, and the log acts like a
receipt for an audit, which keeps everyone comfortable with
(13:15):
the tool that touches their money.
You don't mess with people's money, man.
So if you want to start this, it's straightforward and there
are tools, you have to look themup.
I don't want to suggest anythingbecause I'm not very, I'm not
very educated in what tools are out there other than the ones
(13:37):
that I build myself. I'm a coder, so I build these
things myself. But to start, you can pick a
tiny job that annoys you somebody like pulling a number
from a document and dropping it into a tracker or drafting a
follow up e-mail after a meeting.
Google does this a lot. You know the all the e-mail apps
do this now, but a follow up e-mail after a meeting?
(14:01):
You could have your agent do that for you.
Write a goal in one sentence andlist 3 rules the agent must
follow. It's super simple, a goal.
You could tell the the AI agent every time I get an e-mail, send
it to my phone or what you know,like give me a give me an alert
(14:24):
on my phone, something like that.
Send an alert to my phone numberand text you, you know, text you
a summary of the e-mail. And if it sees seems like a big
deal, then sure, answer it. But if not, just leave it alone.
And I know there are e-mail appsthat do that by themselves, but
you know, we're just making a, asilly model right now.
(14:45):
Or if certain person emails me, send me a text.
How about that? That would be great.
That's an actual like a list of a actual thing that could be
helpful. If my boss emails me, send me a
text because sometimes your e-mail alerts don't go through
or sometime it's after office hours, but your boss might need
(15:06):
to get a hold of you. You don't have your e-mail app
on your phone. Your your work e-mail app on
your phone. So you know you do you get a
text from your boss. It's 8:00 at night when you're
putting your kid to bed. That would be the worst app
ever. Don't do that.
I, I'll tell you this, do not have your work and your home
like and your personal phone as one phone.
(15:27):
That's the worst idea ever. Unless of course, you run your
own business and then you got todo it.
So eventually you can just run it on autopilot and does all the
stuff for you. You can have your agent do so
many different things and you can check out different
(15:48):
services. Like I said, I don't want to
recommend anything because I don't really use them myself
personally. I built them, so I build them
for my own personal use. So I hope that this has cleared
a few things up for you. It's like a helper, you know, if
you, if you have an executive assistant, that's kind of what
(16:08):
an AI agent, agentic AI is all about.
It helps you do those tasks thatyou just don't want to do.
It turns a chatty program into ahelper that completes tasks by
planning those steps using toolsthat are safe and then checking
along the way so you can get theresults that you want.
(16:32):
You just got to give it a clear goal, a few rules and some
buttons that it can push. And you keep all the control
too. You get readable logs from it.
You measure success with finish rates, time to finish and
escalations. Like does it send it back to
you? So start small and confirm the
(16:54):
value and grow your agentic AI at your own pace.
You don't have to make a huge project, make something very
small that'll fix a task or fix a problem that you're having
right now. Hey, thank you so much for
listening today. I really do appreciate your
(17:16):
support. If you could take a second and
hit the subscribe or the follow button on whatever podcast
platform that you're listening on right now, I greatly
appreciate it. It helps out the show
tremendously and you'll never miss an episode.
And each episode is about 10 minutes or less to get you
caught up quickly. And please, if you want to
support the show even more, go to patreon.com/stagezero and
(17:41):
please take care of yourselves and each other and I'll see you
tomorrow.
This is the Elon Musk Podcast, your daily hit of what is really
going on at Tesla, SpaceX X AI, and the rest of the Musk
universe. I'm your host Will Walden, and I
have covered Elon Musk for more than five years, spent a year on
the ground at SpaceX, Starbase during early Starship
development, and before this I spent my career as a software
developer working with billion dollar companies.
(00:22):
I've also built and sold my own businesses and now I make
content and help other people grow their companies.
Now on this show I used that experience to break down the
news, filter out all the noise, and give you clear context.
You can actually use a Gentek AIgives an AI system the ability
(00:45):
to take small sensible steps towards a goal.
Instead of stopping after one single reply, think of it as
moving from a one time answer toa finished task with receipts
that you can actually review. Businesses use it to book
travel, organize calendars, clean out spreadsheets, and
draft polite emails that follow company rules.
(01:07):
And you get outcomes rather thanlose suggestions with a record
of how each decision was actually happening.
And how does it actually work ineveryday life.
Though in this episode, you'll get a plain English definition,
a simple loop that you can picture in your head, and a few
real world stories you can adaptat home or at work.
And we're going to talk about what it does well, where it
(01:28):
needs clear instructions of how to keep control with approvals,
spending caps, and time limits. And by the end, you'll know how
to start small, test safely, andgrow with confidence.
And we're going to get into thatright after this short
commercial break. Here is the short definition of
(01:49):
agentic AI. It is a system that pursues a
goal through several steps, using tools along the way, until
it succeeds or runs out of allowed attempts.
Regular chat gives you one response.
Then you ask it again, and againand again and again.
You get a bunch of responses. An agent continues on its own
(02:10):
within the rules that you set for it.
That shift from answers to outcomes changes how you design
tasks, how you approve actions, and how you judge success.
Now picture a helpful assistant with a checklist.
You say plan a family weekend that fits a budget, includes a
museum visit. Keep Sunday afternoon free.
(02:33):
And the assistant breaks the goal into steps, chooses the
next step, uses the right apps, writes down what it found, and
it decides what to do next. In agentic AI, the assistant is
the model. The apps are tools like a
calendar, a map, a price look up, or your notes.
In the written record is a log you can read later.
(02:53):
System repeats this plan, act and check pattern until it
reaches the goal or asks you forhelp.
It may fail and the main parts are easy to remember.
First, the goal, which states what done looks like in simple
terms such as stay under $600, keep walking time short, and
pick refundable options. Second, a planner which turns
(03:15):
the goal into a short list of small steps.
Third, tools which are safe buttons the agent can push like
search hotels, read a spreadsheet, or add an event.
4th. Memory, which stores notes from
each step so the next step does not forget what just happened.
5th. A checker which asks if the last
step helped or hurt. 6th. A stop rule which ends the run
(03:39):
when the job is done, when a limit is hit, or when the system
needs your approval, or when it completely fails.
Now let us walk through a travelexample without any jargon.
You tell the agent plan a 2 day Austin trip for two adults, keep
lodging out of $200 per night, include a live music event, and
leave time for some nice Texas BBQ.
(04:03):
Now the planner lays out steps like find dates, look up hotels,
check refund policies, search music events, draft the
schedule, and prepare a summary for you.
The agent goes to work. It uses a hotel lookup tool,
writes down choices for you, compares prices for your budget,
(04:25):
and drops the top pick onto a calendar for you.
And after each step, the checkerasks if the plan still fits the
rules. Now the stop rule ends the loop
when the schedule, budget, and refund notes meet that goal.
Then the agent hands you a clearsummary and a log of every
single action that happened there.
And the style of AI works best on chores with clear rules,
(04:48):
repeatable steps, and measurableresults.
Reading bills, copying key numbers into a tracker, and
writing a short status note fitswell because each step has a
right or wrong outcome. Drafting polite replies to
common messages fits well because examples teach the tone
and structure. open-ended tasks with fuzzy goals like make
(05:08):
something creative for the website isn't a good one.
It's not a good prompt for that.It can drift because the success
target is not clear. You guide the agent by setting a
very narrow goal. List rules in plain English and
name the tools it may use. Now you got to think about
(05:29):
control and safety. It's all within this loop, but
not at the end. You give the agent only approved
tools like a read only calendar or hotel search that cannot buy
anything. You add spending caps, time
limits, and a maximum number of steps.
You require human approval for anything irreversible, such as
purpose purchases, deletions, ormessages to customers.
(05:52):
You keep private information safe by redacting secrets,
limiting who the agent can contact, and preventing it from
pasting data into public sites. These basic moves act like
seatbelts, locks, and curfews for a very fast helper, and the
helper can do these tasks withinseconds sometimes.
So you have to have rules. Now there are a few simple
(06:14):
shapes for how agents works. One helper handles a straight
path, like reading a form, filling a tracker, and writing a
summary about it. A planner and a doer split the
job, which reduces trial and error and keeps the log knee.
A small team uses a supervisor to route tasks to specialists,
like a calendar specialist, a data specialist, and a writing
(06:36):
specialist. Many teams do well with the
planner and do repair because itstays simple, exposes the
decision points, and makes reviews really fast.
Now you do not need a big platform to start.
Think of tools as small safe buttons with clear labels and
clear limits. One button reads a file, 1
button looks up a price, 1 button adds a calendar entry.
(06:57):
Each button returns a simple result that the agent can
understand, and the log shows which button the agent pressed,
which words it sent into the button, and what came back.
If something looks odd, you can replay the run and see exactly
where it went off track and you can figure out how to fix that.
Humans are still needed at this point with AI.
(07:18):
Some are really good. I run an agent sometime to get
me the latest news on Elon Musk and it does wonders for me.
Now. The process that it takes is
very straightforward. I say check these amount of news
sources, check 20 news sources, etcetera, etcetera, and then
(07:41):
send me the links. It's very simple.
My my agent is very simple. Send me the links, give me a
like a three bullet point rundown of what the article is
about. See if it's worth my time.
I'm going to redevelop this agent into something a little
bit more robust. So it's easier for me to do this
(08:02):
podcast. I've been doing this podcast for
a very, very long time, five years or so thousand episodes
plus. So if you are a fan of the show,
thank you, and if you aren't, thank you for stopping by and
listening. And also, since you are a new
fan to the show, please take a second and hit the follow
(08:24):
button. That'll be really helpful.
Or pick up some merch at starshipshirts.com.
That helps out tremendously. So agents are really cool
because you can measure an agentthe same way you measure a
person who handles a task. Then it finished the task, yes
or no. How long did it take?
(08:46):
How much did it cost to run it? How often did it ask for help?
Now those numbers give you a clear picture of whether the
system saves time or actually creates more work for you.
When I first started developing my agent, I was, it was taking
me more time than it than it wasbefore I had it just to get it
(09:07):
right. But by fine tuning it, I have a
clear path to, you know, the best results for the day.
I can't just Google things. You get a bunch of just junk in
there and you can't just chat TBT things because it doesn't
give you the right answers. So you have to make something.
I had to make something custom. I had to make a custom agent to
(09:29):
do the things that I wanted to do.
And it has a very simple clear task.
And then it doesn't fail anymorebecause it knows exactly what to
do, has like 4 things it needs to do.
Search the web on certain sites.Send me back the headline.
Send me back three bullet pointsabout the article because it
(09:50):
reads the article. Send back three bullet points of
the article. Send me a link to the news piece
so I can check it out myself. And that's it.
That's all it does. And then I read everything I can
and come up with stuff on my own.
So my agent is very simple. You can make something
absolutely complex. Crazy, you know?
(10:11):
How often does it ask for help? That's the important one, right?
So those numbers give you a clear picture of whether the
system saves time or creates a huge headache for you.
And if it creates the headache, either work on it more or find
another process. I had to go through 5 or 6
different processes in order formy agent to work properly.
(10:34):
If you have the time, great. If not, there are systems out
there that will make you an agent.
Or you can do like a drag and drop agent for this kind of
stuff. And you have to treat each run
like a transaction with a resultyou can accept or reject because
each one of these costs compute time or tokens.
If you do it on something, you know where somebody else hosts
(10:56):
the LLM. And when you reject a result,
add a quick note about what wentwrong and then turn that note
into a rule the agent can followthe next time.
And let's talk about like a story from an office because
that might put it into perspective.
So customer support, we all hateit, right?
(11:20):
We all hate going through customer support.
If something goes wrong, we wantto answer now.
But if you're on the other end where you're a customer support
agent, inbox assistance are important.
And if you have a goal, the goalwould say, read each new
message, match it to one of fivecommon issues.
Suggest a reply that follows theplaybook, which is, you know,
(11:43):
you have a playbook that you feed it and hand anything
unusual to a person. So the agent opens an e-mail,
finds key details, checks the playbook, drafts a reply and
puts it into a queue for a person to review.
And if the person reviews it andit needs a little bit of help,
(12:05):
type in there, you know, sorry, sorry you're having this issue.
My name is Will. I'm here to help you.
And that person confirms that the draft uses the right issue
type and the right tone. So it's basically a huge
database of things like the playbook.
Now, a person gives a quick approval on the first week of
use, then reduces approvals to only high stakes messages once
(12:29):
the numbers look steady. So you're training this model
while it's doing its job. Now, another one we can think
about is where you mix tools andapprovals.
So if you're a finance helper and it could check small
purchase requests, the goal saysensure the request sits within
(12:53):
the budget, confirm the vendor is on the approved list and
prepare a draft purchase order. The agent reads the request,
looks up the budget balance, matches the vendor name, and
fills a form. The stop rule blocks any e-mail
to a vendor until a human clicksapprove, and the log acts like a
receipt for an audit, which keeps everyone comfortable with
(13:15):
the tool that touches their money.
You don't mess with people's money, man.
So if you want to start this, it's straightforward and there
are tools, you have to look themup.
I don't want to suggest anythingbecause I'm not very, I'm not
very educated in what tools are out there other than the ones
(13:37):
that I build myself. I'm a coder, so I build these
things myself. But to start, you can pick a
tiny job that annoys you somebody like pulling a number
from a document and dropping it into a tracker or drafting a
follow up e-mail after a meeting.
Google does this a lot. You know the all the e-mail apps
do this now, but a follow up e-mail after a meeting?
(14:01):
You could have your agent do that for you.
Write a goal in one sentence andlist 3 rules the agent must
follow. It's super simple, a goal.
You could tell the the AI agent every time I get an e-mail, send
it to my phone or what you know,like give me a give me an alert
(14:24):
on my phone, something like that.
Send an alert to my phone numberand text you, you know, text you
a summary of the e-mail. And if it sees seems like a big
deal, then sure, answer it. But if not, just leave it alone.
And I know there are e-mail appsthat do that by themselves, but
you know, we're just making a, asilly model right now.
(14:45):
Or if certain person emails me, send me a text.
How about that? That would be great.
That's an actual like a list of a actual thing that could be
helpful. If my boss emails me, send me a
text because sometimes your e-mail alerts don't go through
or sometime it's after office hours, but your boss might need
(15:06):
to get a hold of you. You don't have your e-mail app
on your phone. Your your work e-mail app on
your phone. So you know you do you get a
text from your boss. It's 8:00 at night when you're
putting your kid to bed. That would be the worst app
ever. Don't do that.
I, I'll tell you this, do not have your work and your home
like and your personal phone as one phone.
(15:27):
That's the worst idea ever. Unless of course, you run your
own business and then you got todo it.
So eventually you can just run it on autopilot and does all the
stuff for you. You can have your agent do so
many different things and you can check out different
(15:48):
services. Like I said, I don't want to
recommend anything because I don't really use them myself
personally. I built them, so I build them
for my own personal use. So I hope that this has cleared
a few things up for you. It's like a helper, you know, if
you, if you have an executive assistant, that's kind of what
(16:08):
an AI agent, agentic AI is all about.
It helps you do those tasks thatyou just don't want to do.
It turns a chatty program into ahelper that completes tasks by
planning those steps using toolsthat are safe and then checking
along the way so you can get theresults that you want.
(16:32):
You just got to give it a clear goal, a few rules and some
buttons that it can push. And you keep all the control
too. You get readable logs from it.
You measure success with finish rates, time to finish and
escalations. Like does it send it back to
you? So start small and confirm the
(16:54):
value and grow your agentic AI at your own pace.
You don't have to make a huge project, make something very
small that'll fix a task or fix a problem that you're having
right now. Hey, thank you so much for
listening today. I really do appreciate your
(17:16):
support. If you could take a second and
hit the subscribe or the follow button on whatever podcast
platform that you're listening on right now, I greatly
appreciate it. It helps out the show
tremendously and you'll never miss an episode.
And each episode is about 10 minutes or less to get you
caught up quickly. And please, if you want to
support the show even more, go to patreon.com/stagezero and
(17:41):
please take care of yourselves and each other and I'll see you
tomorrow.
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