Corporate America is starting to ration AI. Beat.
The days of giving employees random tools are
over. They really are. We are moving away from
the chaotic Wild West. Companies are simply tired
of paying massive monthly bills. Right. The budgeting
got totally out of hand. Now, executives are
asking a much harder question. What are we actually
getting from all this? Two sec silence. Welcome
to this deep dive. Thanks for having me. today
we are exploring a strange transition in corporate
tech we have an Excellent source article for
you today. It is a really solid breakdown. It
focuses on building business systems with cloud
-managed agents. The goal for our conversation
today is simple but profound. We want to understand
how we move past messy chatbots. We're moving
towards structured, cloud -based AI employees.
Systems that actually do real business work.
Exactly. Today, we will explore what these managed
agents are. We will look at how they use actual
sleep cycles. Which is a brilliant concept. process
their day just like we do. We will also examine
the most profitable business use cases. Yeah,
the real money makers. And finally, we will discuss
how you can start building them. You want to
do this without creating a chaotic bot army.
Because that is a trap a lot of people fall into.
The current state of AI is incredibly messy right
now. Companies are paying for a massive scatter
of tools. It is everywhere. You have one tool
just for writing copy. You have another separate
tool for technical research. You probably have
another for coding or internal search. I will
be completely honest with you here. I still struggle
with this chaotic setup myself daily. Oh, totally.
We all do. I feel completely overwhelmed by having
a disconnected stack. My AI tools are scattered
across my entire workflow. Right. I am constantly
copying and pasting between five different tabs.
It honestly drives me crazy sometimes. You are
definitely not alone in feeling that way. Yeah.
That is the standard experience for almost everyone
right now. It really is. And that scattered usage
does not connect to business results. Yeah. After
one month. The corporate billing looks absolutely
insane. Just a wall of random subscriptions.
Exactly. Companies pay for empty AI seats nobody
uses. They pay for extra API calls across fragmented
platforms. Yeah. They pay for random tools different
teams signed up for. That is when the core conversation
at the executive level changes. They stop asking
what the AI can technically do. Right. They pivot
entirely. They start asking which systems are
actually worth paying for. This is where cloud
managed agents become incredible. incredibly
interesting. Before this, developers used something
called cloud code. Right. The local version.
It ran locally on your own physical computer,
which sounds great for personal testing and hacking
around. It was fantastic for personal coding
projects. But a local workspace only works for
one single user. Right. It is completely trapped
on your laptop. Cloud managed agents move that
power directly into the cloud. The agent can
now run inside a shared product. It can run inside
a client system or broader workflow. But a basic
chatbot already exists in the cloud. It just
answers a single prompt when you type it. That
is true. But a chatbot is purely reactive. A
managed agent actually follows a persistent specific
role. It uses your company's business context
automatically. It securely accesses your internal
databases. It supports repeated complex workflows
over long periods. It basically becomes part
of the company's central operating system. Let
me pause and ask you something about that transition.
Why does running in the cloud fundamentally change
the business value? Beat. Why is it so different
from running on a local machine? A local machine
limits the AI to your personal hours. When you
close your laptop, the AI stops working. In the
cloud, the system runs continuously for everyone.
It integrates directly with your company -wide
workflows over time. It becomes a shared resource
rather than a private calculator. So cloud means
it supports team workflows over time, not just
isolated solo tasks. Exactly. But for a cloud
agent to be useful over time... It needs one
crucial thing. It needs a reliable memory. It
cannot wake up with amnesia every single day.
If you have ever used a standard chatbot, you
know this pain. It is the absolute biggest limitation
of AI today. The model might be brilliant during
one single conversation. Right. But it forgets
everything the second after that session ends.
In a fast -paced business setting, that amnesia
is a nightmare. Making employees repeat rules
and tone daily is terrible. It makes the agent
useless. You cannot repeat project deadlines
every single morning. The human spends more time
managing the AI than working. Exactly. The return
on investment vanishes instantly. A useful business
assistant must carry vital context forward. This
is why cloud -managed agents utilize a dedicated
memory system. To fix the amnesia, developers
give them cognitive architecture. Let me stop
you right there for clarity. What does cognitive
architecture actually mean in plain English?
The structure built so an agent can remember
and review its work. That makes perfect sense.
It is basically a filing system. Yes, and it
involves several distinct layers of memory. Short
-term memory keeps track of the immediate current
task. Okay. Long -term memory stores useful information
for all future sessions. How does it actually
store those long -term memories? Does it just
bloat the active prompt with endless text? No,
that would get way too expensive and confusing.
Instead, it uses simple, readable text files
called markdown files. Like basic text documents.
Exactly. You might see files like dailylog .md
or a file called userpreferences .md. Right.
And a very important file called decisions. This
leads us directly to the concept of the sleep
cycle beat. I find this mechanism absolutely
fascinating. It is really cool. At the end of
the day, the agent reviews its work. It saves
important decisions and updates those markdown
files. It ignores useless conversational details
and writes a summary for tomorrow. Right, and
it does this completely automatically in the
background. It parses its own daily transcript
to find the gold. Okay, let's unpack this sleep
cycle idea, two -sec silence. It sounds exactly
like a head chef closing down a kitchen. I love
that analogy. You do not keep the potato peels
from the chaos. You throw the trash away and
wipe the counters. But you write down the new
recipe tweak you perfected. you leave that specific
note on the counter for the morning shift. That
is a perfect way to visualize the mechanism.
And that is exactly how the next day's startup
flow feels. Before the agent answers any new
questions, it reads its notes, it reads yesterday's
summary, and checks any open tasks. So it preps
itself? Yeah. It reviews the current rules and
the updated preferences. The human user does
not have to repeat the background context. I
am really curious about the actual mechanics
of that review. How does the agent actually know
what to remember? That is the tricky part. How
does it separate vital facts from random chatter
during its sleep cycle? You have to give it strict
prompt rules for its evening review. You explicitly
instruct it to only save decisions and process
changes. Okay. That strict filter forces it to
drop all the conversational noise. It actively
deletes the filler and only keeps the signal.
It relies on your specific rules to filter out
the random, useless daily details. Spot on. Now
that the agent has a functioning memory, we have
to give it a job. And this is where most companies
make a critical mistake. The source material
is very clear on this next point. Do not start
by asking what the agent can do. Yeah. Instead,
ask what painful, expensive business workflow
it can fix. What's fascinating here is how unglamorous
these valuable problems actually are. Businesses
complain about the exact same mundane things
every week. Highly paid employees waste hours
searching for simple internal information. Managers
repeat the same onboarding instructions to every
new client. Weekly status reports take hours
to write manually. You feel this. Oh, absolutely.
The source outlines five specific business use
cases to solve this. The first group is what
I would call information wrangling. Yeah, making
sense of the mess. This includes the internal
knowledge assistant. Right. Company information
is usually scattered across Slack, Notion, and
CRMs. It is a tangled mess of overlapping documents.
This assistant searches policies and standard
operating procedures instantly. Employees do
not waste time digging through ancient disorganized
folders. The second use case in this group is
client onboarding. This involves organizing intake
forms and drafting initial kickoff briefs. Whoa.
Imagine an agent seamlessly turning a chaotic
pile of emails and forms into a perfect kickoff
brief overnight. It's absolute magic for modern
service businesses. Every new client needs intake
forms, access requests, and specific timelines.
Usually a human spends three hours hunting down
missing attachments. The agent cleans up that
messy input perfectly while you sleep. The next
group of use cases. cases focuses on writing
and pipeline velocity. Use case three is the
reporting assistant. Nobody likes writing weekly
status reports from scratch. No one. The agent
collects the scattered meeting notes from the
entire week. It highlights the critical blockers
and formats a clean document automatically. Use
case four is sales follow up. This one feels
like it connects directly to the bottom line.
It absolutely connects directly to revenue generation.
Sales teams constantly lose deals because their
internal notes are messy. Often, follow -up emails
are sent three days late. The managed agent quietly
listens to the meeting transcript in the background.
It turns that transcript into updated deal statuses
in the CRM. It even drafts the customized follow
-up emails automatically after the call ends.
The final use case is vertical sauce. This means
building assistance for very specific, narrow
industries. Yes. Instead of a general assistant,
you build an expert. Right. You might build an
assistant just for real estate listing notes,
or you build an assistant strictly for legal
case note organization. Looking back at the reporting
assistant use case, I have a fundamental question.
Why does the source suggest aiming for a 70 %
to 80 % finished draft? Beep. Why not just promise
the client full automation? Promising full automation
usually sets you up for inevitable failure. AI
models still hallucinate and edge cases always
exist in business. Delivering a solid draft manages
expectations and keeps humans safely in control.
It is much easier to sell a highly reliable first
draft. Right. A draft shifts the AI from an autonomous
manager to a helpful intern. A draft is safer
to trust and sell, keeping a human in the loop.
Exactly. Sponsor placeholder. Welcome back to
the Deep Dive. With all these incredible, profitable
use cases, a big strategic question remains.
Why should a business use clawed managed agents
instead of other popular platforms? Why not just
use something accessible like OpenClaw? Right.
Or why not just spin up hundreds of simple single
task bots? OpenClaw is still a fantastic tool
for many situations. It's absolutely perfect
for quick demos and rapidly testing ideas. Okay.
It's also really good for less technical users
who want to experiment. But a quick demo is not
a daily reliable business tool. It is just a
proof of concept. Exactly. A demo is just the
very first. step of the journey. Cloud managed
agents are designed for deep, reliable, continuous
workflows. They are built for serious system
integration and strict operational control. You
can tether them securely to your proprietary
company databases. I really love the forward
slash new employee command idea from the source.
It treats the creation of an agent exactly like
a human job interview. It is a brilliant mental
model for system builders. Yeah. Instead of randomly
spinning up an agent, you must answer rigorous
questions. What exactly is this AI employee responsible
for doing? What sensitive actions should it never,
ever perform? When must it pause and ask for
explicit human approval? Establishing those boundaries
prevents the trap of having too many agents.
People on social media love bragging about having
200 AI agents working simultaneously. It sounds
incredibly impressive on a viral post. Yeah.
But in reality, it usually creates a massive
new operational mess. If you have 200 agents
generating long, random updates, you fail. You
just become a human bottleneck reading their
endless spam. You spend your whole day reviewing
robotic busywork. Yes. And that defeats the entire
purpose of automation. Your day turns into reading
repetitive agent outputs instead of making strategic
progress. Right. The true goal is to drastically
reduce manual human work. It is not to create
a brand new layer of AI middle management. Let
me push back on that idea for a second. Doesn't
having more agents fundamentally mean you have
more productivity? If I have more digital workers,
that should equal more total output. It seems
logical, but it breaks down in practice. Without
clear, restricted workflows, more agents just
create exponentially more noise. Okay. They generate
conflicting data and overlapping reports. You
end up spending your entire day managing their
inevitable mistakes. Wow. You literally become
a full -time editor for robotic spam. No, having
too many agents just makes you a bottleneck reading
their messy outputs. Exactly. Quality workflow
is always better than quantity of agents. Which
brings us to the final strategic advice for system
builders. How do you actually start building
this the right way? How do you avoid the spam
trap? The first absolute rule is to start very
small. You have to pick one single, highly specific
workflow. It needs to have messy, unstructured
input on one side. It needs clear, structured
output on the other side. For example, messy
client call notes go in. A perfectly formatted,
clean summary document comes out. Then you evaluate
it using a very simple test scorecard. Did this
specific agent actually save me time today? Yeah.
Was the output easy to review and approve? Right.
And most importantly, would I voluntarily use
it again tomorrow? If the honest answer is no,
you must stop and fix the workflow. Okay. Do
not add more complex features to a fundamentally
broken workflow. You also need to build a highly
repeatable setup. You must define a clear role,
strict inputs and an exact output format. You
also need a rigid review rule and a clear failure
rule. This discipline stopped you from building
random disconnected vanity agents. You end up
building a modular system you can easily reuse.
Right. You can package it and deploy it for your
very next client. Another massive piece of advice
from the source is to pick one market. Do not
try to build generic AI for general business.
That is a very common trap. Instead, build an
AI reporting assistant specifically for marketing
agencies. Study exactly what marketing account
managers do every single week. Find out which
highly specific tasks waste their valuable time.
The deeper you know the specific market, the
better the final tool. Finally, you have to turn
your internal tests into undeniable proof. You
must track your before and after workflow metrics
incredibly carefully. You need to say a client
brief used to take 45 minutes. Now a high quality
draft is ready for review in exactly eight minutes.
That is the real proof that executives actually
want to buy. I want to ask you about that specific
market focus strategy. Why is picking one specific
niche market so crucial for success? Beat. Especially
when modern AI models can theoretically do almost
anything. Because specific markets have highly
unique repeatable documents and internal jargon.
Dentists have different operational pain points
than corporate lawyers. A generic AI requires
too much custom instruction every single time
you use it. A niche AI already knows the exact
context and the specific rules. Understanding
one market deeply makes it way easier to solve
their specific expensive problems. Exactly. You
ultimately sell the clear business result, not
the underlying technology. Nobody buys AI just
to have AI anymore. No. They buy a solution to
a painful, expensive, operational bottleneck.
Let us synthesize this entire journey for a moment.
The era of buying random, disconnected AI seats
is quickly ending. It really is. The wild west
of scattered chatbots is closing. The future
belongs to structured, cloud -based, managed
agents. These are specialized systems that possess
actual cognitive architecture. They have robust,
organized memory files. They execute daily sleep
cycles to clean up their context. They follow
highly specific, rigid prompt rules. Most importantly,
they solve incredibly painful, expensive, and
specific business problems. Two sec silence.
So I want to leave you with a final thought to
mull over today. We are successfully building
AI systems with absolutely perfect sleep cycles
now. These digital agents cleanly review their
chaotic day. Right. They neatly file away their
important memories into readable documents. They
prep smoothly for tomorrow without any residual
stress or anxiety. Which is amazing. What does
that actually say about our own human sleep cycles?
Are we as good at cleanly putting our own work
away at the end of the day? That is a great question.
Or is the artificial intelligence already out
managing our own mental bandwidth? Neat. Thank
you for joining us on the Seep Dive. Take care
out there.