Imagine an AI that doesn't just tell you things,
but actually does things for you, searching,
buying, maybe even crafting content, all without
you really lifting a finger. What if that future
is, well, kind of already here? Welcome to the
deep dive. Today, we're really digging into a
concept that's rapidly reshaping our digital
lives, AI agents. That's right. We're moving
beyond AI is just this brilliant. brain in a
box, you know. These agents are becoming our
digital hands and eyes. They're interacting with
the world for us. Yeah, and our mission today
is to explore what these AI agents actually are,
why there's such a potential game changer, and
importantly how you can start getting a handle
on them right now. We'll look at some real -world
uses, understand this new idea of agent -engine
optimization. VEO, yeah. And crucially, talk
about how to navigate the risk responsibly. You'll
get some practical insights, hopefully a clear
roadmap for this evolving tech. Okay, let's untack
this. For a long time, AI models, you know, like
the early versions of Chach -PT, they were brilliant
conversationalists. You'd ask a question, it
gives you an answer. But it was always sort of
contained, right? A mind without a body, almost.
Exactly. And the core idea with AI agents is
this really fundamental shift. We're going from
responsive AI to proactive AI. An agent doesn't
just answer, It acts. It's an autonomous system.
It perceives its environment. It makes plans.
It makes decisions. And then it executes actions
to achieve specific goals you give it. Think
of them like digital assistants. but with virtual
hands and eyes. So it's like we've handed them
a mouse and keyboard in a virtual world. The
big breakthrough really came when platforms like
OpenAI implemented these agent modes. This lets
the AI actually control a virtual computer. It
can browse websites, fill out forms, click buttons,
just like a person would. It's kind of wild to
watch. It really is. And this matters, like...
a lot. First, you get this exponential efficiency.
Agents can work 247 on repetitive stuff. Then
there's new accessibility. They can reach information
behind login pages or inside proprietary databases,
stuff that traditional APIs, that's application
programming interfaces, basically how software
talks, just can't get to. We're seeing really
deep automation now, not just single tasks, but
entire multi -step workflows. And early adopters,
they're gaining unparalleled speed, scalability,
and innovation. We're already seeing agents ordering
goods, sending LinkedIn messages, even doing
bulk video creation and deep market research.
OK, so boiled down, what's the biggest difference
then? between an agent and just a regular chat
bot. Chat bots answer questions. Agents take
action to achieve goals. That's clear. Action
versus information. OK. One of the best ways
to see this in action, maybe, is with something
familiar, like online shopping. Imagine an AI
agent handling that whole multi -step process
for you. Yeah, and it's surprisingly straightforward
to set up. But the precision is absolutely key.
First, you activate agent mode. Sometimes it's
called browse mode in whatever AI platform you're
using. Then the really critical part. You have
to give it extremely specific instructions. Don't
just say, buy shoes. You need something like,
search the Adidas Vietnam website for men's running
shoes, black, size 42, ultra boost line, under
3 ,500 ,000 VND. Add that to the cart and proceed
to checkout. Really specific. And then you just
watch it happen. Pretty much. You'll literally
observe the virtual computer screen. You see
the mouse cursor moving, text being typed, websites
loading and being navigated. The agent might
even ask you for more details, you know, like
which version of UltraBoost, the Lite or the
1 .0, that interaction is actually crucial. Watching
it helps you figure out how to make your future
prompts even better. Right. Now safety and security
are obviously huge here. We're talking about
potentially spending money. What's the absolute
number one rule for payments? Okay, this is vital.
Never share your full credit card details for
autofill. Just don't do it. It's way too risky.
Safer methods involve, say, instructing the agent
to generate a QR code for you to scan and approve,
or maybe using services like PayPal, where you
do the final manual authentication step yourself,
and always review the final order summary before
you approve anything irreversible. Keep that
control. I can definitely see some challenges
cropping up. What are the common roadblocks people
run into? Well, CAPTCHA barriers are one. Agents
generally respect those, so they'll ask for human
help. Website compatibility can sometimes be
an issue, especially with really complex JavaScript
-heavy sites. But honestly, the biggest hurdle,
9 times out of 10, is prompt precision. Vague
prompts just lead to unpredictable, sometimes
weird results. You absolutely need to be highly
detailed. OK, so if there's one core takeaway
for making purchasing safe with agents, what
is it? Keep financial control yourself. Let agents
do the searching. Got it. OK, beyond shopping,
these agents seem incredibly powerful for, say,
streamlining professional outreach, especially
on platforms like LinkedIn, expanding networks,
finding clients. But you've got to do it responsibly,
right? Oh, absolutely. Rule number one, non -negotiable.
Use a test account first. Seriously. You don't
want to mess up your actual professional reputation
while you're figuring things out. Then define
your target audience with laser precision. No
vague stuff. Like, find marketing managers at
software development companies in Vietnam, 201,
500 employees, using LinkedIn Sales Navigator.
That level of detail. That makes total sense.
It's like giving it very specific coordinates.
And personalization is key, too, I imagine. Not
just blasting templates. Mystically. You don't
want to sound like a robot. Prompt the agent
to maybe visit their recent activity feed first.
Find something specific, like compliment their
recent article on Axe or mention their company's
latest funding round. Then introduce your service
like a conversion rate analysis offer and always
always set clear limits tell it explicitly Execute
this process for exactly 10 people and then stop
report the results back to me in a table Then
you watch, see what works, and tweak your messages
based on the response rates. You know, I still
wrestle with prompt drift myself sometimes. It's
funny, I was trying to get an agent to draft
a nuanced email last week and it just kept defaulting
to this really like overly formal, almost stiff
tone. No matter how I tweaked it, it's a real
challenge. You realize pretty quickly how a slightly
off prompt can just send the whole thing sideways.
That's a great point and a perfect example. A
poorly written prompt especially for outreach
like that, can have genuinely disastrous consequences
for your brand image. It's not just about telling
it what to do, but also what not to do. Always
include stopping conditions, like we said, but
also negative constraints. Things like, do not
contact anyone at company X, Y, or Z. That gives
you much tighter control, prevents embarrassing
mistakes. So thinking about social media automation
then, what's the golden rule? Test on a separate
account. use extremely precise instructions.
Right. So with all these agents out there interacting
with the web, this has got to change how we think
about building websites too. We're seeing the
start of something called Agent Engine Optimization,
AEO. It's like SEO. But optimizing your site
specifically for these AIIs, it sounds like a
totally different game. It really is. Agents
search based on logic and efficiency, not really
aesthetics. They make very literal decisions.
If your meta description says, step -by -step
guide to XYZ, an agent will probably prioritize
that over something vague like, discover the
power of XYZ, the link choices. They look for
clear anchor text, clean URLs, technical signals,
not pretty buttons. And here's a huge one. Agents
have an absolute priority on speed. a slow site,
they'll just abandon it immediately. No patience.
That's a stark difference from how humans browse,
yeah. We might wait a bit longer for a nice design,
so you can even sort of command agents through
the website itself. Yeah, through meta tags,
you can give them direct instructions, like you
could have a tag that says, extract the table
with the ID, hashtag data table 2025. So for
optimizing, you need agent -friendly meta descriptions,
direct action -oriented language. Websites probably
need to optimize more for small screens because
agents often run in these low -resolution virtual
browsers. Loading speed, like I said, isn't just
a ranking factor anymore. It's like table stakes.
It's a basic requirement. Use large, clear buttons,
too. Agents navigate partly by coordinates, so
clear element IDs and good spacing are key. And
implementing a reading mode, like a text -only
version, can really help agents grab information
faster. And what about personal branding and
all this? Agents aren't just finding websites,
they're finding information about people, too.
Definitely. That's where personal SEO comes in.
You need to optimize your LinkedIn profile, for
instance. Keywords in your headline, your summary,
your experience descriptions make your contact
info easily accessible. Agents are designed to
find phone numbers, emails, build your domain
authority by creating content that really showcases
your expertise, and get listed in professional
directories specific to your industry. These
are all signals that agents will pick up on when
someone asks them to find an expert like you.
This shift to AEO, it sounds like it could really
overhaul web design philosophy. What do you see
as the biggest change for how websites will need
to be built going forward? Speed and clear literal
structural elements will become paramount. Okay,
let's shift gears to automated content creation.
We're moving way beyond just, you know, getting
help writing a blog post. Agents can apparently
manage complex content workflows now, like creating
entire daily news videos automatically. Yeah,
it's quite a leap, isn't it? You can define the
parameters, maybe something like, create a daily
two -minute video summarizing the top three tech
news stories from TechCrunch and The Verge. Make
sure it has subtitles. The agent then handles
the news aggregation part, scanning the sites,
identifying the top stories based on whatever
criteria you set. Then a large language model,
an LLM, generates a concise script for the video.
After that, the agent uses various tools, maybe
some Python libraries like MoviePy or APIs for
stock images like Pexels or Unsplash, to combine
an AI voiceover with relevant visuals. And finally,
the video gets compiled and can be automatically
uploaded to, say, Google Drive or even a test
YouTube channel. What's really fascinating there,
I think, is that the agents aren't just using
existing software in a fixed way. They're kind
of creating bits of software on the fly. Like
you said, they might write a short Python script
just to generate that one video, run it, and
then just discard the script. Whoa. OK, imagine
scaling that. Like, a billion different queries
coming in, and each one gets its own tiny bespoke
piece of software created just for that task.
Temporary, perfectly tailored. That's incredibly
efficient when you think about it. It really
changes the whole dynamic. Now, we should be
realistic, right? The quality probably isn't
going to win any Oscars just yet. The output
we see now is often pretty basic, maybe more
like animated slide shows than professional video
productions. That's true. The quality level isn't
always top -tier cinema. But the real value proposition
here isn't necessarily artistic perfection, at
least not yet. It's the sheer speed and scale.
Imagine getting dozens of different content variations
for A -B testing in just minutes, or being able
to produce consistent daily content streams with
basically zero ongoing human effort once it's
set up. It allows for incredibly rapid prototyping
of content ideas before you invest heavily. So
based on that, are these AI agents really coming
for creative jobs right now? Not yet, really.
They create solid foundations, but human refinement
adds the crucial value. OK, let's talk research
because this feels like an area where AI agents
can really supercharge things. They seem able
to break through information barriers that, you
know, a regular Google search just hits a wall
against. This is the huge advantage, definitely.
Agents can navigate sites that require logins,
assuming you give them secure test account credentials,
of course, think accessing platforms like Upwork
or academic databases, maybe private forums.
They can gather real marketplace data directly.
Imagine automatically collecting the average
hourly rate being asked for Lang chain development
jobs on various freelancer sites. That's powerful
data. And crucially, they really excel at cross
-referencing information from multiple sources.
They can compile these comprehensive reports,
looking for patterns, maybe discrepancies, across
websites, forums, social media, all synthesized
together. And we've actually seen this used in
practice. I read about a study where agents analyze
the AI skills market on freelancer platforms.
And they found that actual client needs were
often much simpler, like basic API integrations,
than what the really technical online communities
were usually discussing. That insight is gold
for business strategy. Exactly. So to optimize
using agents for research, You have to define
your objectives very, very specifically. Don't
just say research the real estate market. It
needs to be like, find the average selling price
per square meter for two -bedroom apartments
in Thu Duc City, Ho Chi Minh City, sourced only
from batdongsan .com .vn, and categorize the
results by specific wards. You need to identify
the exact target platforms, set clear parameters
for what data to extract and how to organize
it. Maybe export to a CSV file with these named
columns. Plan for human intervention points,
too, for things like complex captchas or decisions
needing judgment. And always have a way to verify
the information. Maybe cross -checking key data
points. So thinking about those data silos we
often end up in, how can agents help break those
down? They can log into multiple sources and
synthesize data from across the web. Okay, moving
from gathering data to presenting it. Agents
are also streamlining the creation of things
like presentations and documents. No more staring
at that intimidating blank slide deck, perhaps?
Hopefully less of that. Yeah, automated deck
generation is becoming a thing. It usually starts
with topic research. You give it a topic, maybe
the impact of quantum computing on cybersecurity.
And the agent goes off and scans relevant sources.
RCIF papers, tech journals, whatever you specify.
Then it handles the content synthesis part. It
combines the text, pulls out key data points,
structures them into coherent slides, generating
titles, bullet points, even draft speaker notes.
It can even try to incorporate relevant images
or basic charts for some visual enhancement.
And it tries to ensure a logical structure, intro,
body, conclusion. That sounds incredibly useful
for just getting that first draft down, which
is often the hardest part. So it's probably not
going to look like something a top graphic designer
made, but the speed and comprehensiveness must
be a huge advantage. Precisely. While the design
might be basic, it excels at speed you can get
that first draft in minutes, not hours. It offers
comprehensiveness too, often pulling in perspectives
or data points you might have overlooked. And
it ensures consistent formatting throughout.
which is nice. The best approach, really, is
to use this AI -generated content as a solid
foundation, a starting point. Then you come in
with your human insights, refine the narrative,
and use proper design tools like Canva or PowerPoint
to make it look polished. So for presentations
and docs, it's really about getting speed over
initial perfection, right? Exactly. Agents give
you a comprehensive head start, saving valuable
human time for refinement. This is all happening
now, but the next frontier seems even closer
to home, literally. We're talking about agent
browser technology, the idea of agents running
directly within your own web browser, not just
on some remote virtual machine in the cloud.
Yeah, this is really interesting. Companies like
Perplexity are pushing hard in this direction.
The capabilities are pretty impressive because
they run locally. These agents can perform automations
much faster with lower latency. They can securely
access the services you're already logged into,
your email, your social media accounts, your
work tools, which allows for much more seamless
integration into your personal workflow. You
could potentially perform bulk operations much
more easily, like cleaning up thousands of spam
emails, or mass unfollowing inactive accounts,
or curating content feeds based on your logged
in history. And they could provide real -time
assistance, offering contextual suggestions or
actions as you're browsing different sites. Okay,
so what might this look like in terms of practical
daily use cases? Well, you could imagine setting
up automations like, every morning, check my
inbox, delete emails from known spam domains,
automatically unsubscribe from newsletters I
haven't opened in three months, and flag emails
from my boss as important. or for content curation.
Every day at 8 a .m. find the top five trending
YouTube videos about sustainable urban development
and put the links and summaries in a note for
me, even social media management. Take this blog
post draft, schedule it to post on my LinkedIn
and Twitter tomorrow at 9 a .m. sharp, and monitor
mentions of my brand name, flagging any potentially
negative comments from my review here. The potential
for streamlining our daily digital grind is pretty
huge. So what's the main advantage then of having
these agents run locally in your browser? Greater
speed and seamless integration with your personal
logged -in accounts. Sponsor. Okay, as these
agent capabilities keep growing, and they are
growing fast, responsible automation and managing
the risks involved become just critically important.
It feels a bit like giving a very powerful, very
fast tool to someone who hasn't used it before.
You absolutely need guardrails. Absolutely. You
have to be careful financially, for instance.
You must always be the final approver for any
transaction. Never, ever give an agent unrestricted
access to financial accounts or payment methods.
For things like investment decisions, sure, agents
can analyze data, but they completely lack human
context, judgment, and risk tolerance. Don't
let them make those calls. Be really cautious
about data security when instructing them to
access sensitive platforms. And like we said
before, using dedicated test accounts for experimenting
with agents is just smart practice to limit any
potential damage if things go wrong. Those are
crucial precautions. And thinking about prompt
engineering, which we've touched on a few times,
it's not just about getting good results. It's
fundamental to safety and control too, isn't
it? It's absolutely central to control. You need
refined control through your prompts. Be extremely
specific. Leave no room for ambiguity. Set clear
boundaries, explicit stopping conditions, quantity
limits, negative constraints. Don't do X. Use
examples and references whenever possible to
show the agent exactly the kind of output you
want. And definitely start by testing with low
stakes, easily reversible tasks. Research, maybe
summarizing articles, drafting content before
you even think about moving to high impact automations
like client outreach or actual purchasing. and
monitor continuously, especially when you're
first setting things up. Be ready to jump in
and stop it if needed. It also sounds like there
are still definite technical limitations we need
to be aware of, right? What can they not do effectively
right now? Yeah. There are limits. Current agents
generally can't use desktop software. They're
mostly confined to browser -based applications
and websites. Their output quality for really
complex creative tasks, like nuanced writing
or sophisticated design, is still pretty basic.
They struggle significantly with nuanced decisions
that require human judgment, empathy, or deep
cultural understanding. And in very long, multi
-step workflows, they can sometimes sort of forget
their original objectives or lose the long -term
context of the task. So putting it bluntly, what's
the biggest risk if we're not careful with these
agents? Losing financial control or seriously
damaging your brand reputation. OK. The real
power, then, it seems, starts to unlock when
you begin combining these different agent capabilities
together. Building your own automation systems
for personal use or for business, it's about
creating workflows. That's exactly the vision.
To set up, say, your own daily automation system,
you'd first identify those repetitive tasks you
do all the time. Maybe checking competitor pricing,
compiling social media reports, filtering your
email inbox. Then you assess their automation
potential. Are they mostly browser -based? Are
they rule -driven enough for an agent? Then you
design the workflow, which means writing those
detailed step -by -step prompts we talked about.
Then you test and refine. Start simple, maybe
just one or two steps, and gradually increase
the complexity as you get more confident. And
always... Always monitor the performance, track
the success rate, see where it messes up, and
use that feedback to improve your prompts. So
thinking bigger picture for agencies or small
businesses, what could this really mean in terms
of transforming how they operate? Oh, it could
be huge. It could mean truly scalable service
delivery. Imagine an SEO agency that can suddenly
generate detailed technical audit reports for
100 clients every month almost simultaneously
without needing to triple their staff. It can
enable a real shift towards outcome -based pricing.
If you can automate much of the repetitive execution
work, you can stop billing just for hours worked
and start focusing on delivering specific results
for clients. Rapid prototyping for new service
ideas becomes much easier and cheaper. And it
can improve quality control by ensuring consistent
output for tasks like keyword research or market
analysis. How could agents really transform a
small business bottom line? They allow for rapid
scaling and a stronger focus on outcomes, not
just ours. Looking ahead, this whole agent revolution
seems poised to profoundly reshape not just tasks,
but the job market itself and even core business
models. We're not just talking about new tools.
It feels like we're talking about entirely new
ways of working. I think that's right. We'll
almost certainly see new job categories emerge.
Things like AI agent supervisor, senior prompt
engineer, maybe workflow automation specialist,
titles we haven't even thought of yet. Business
models will continue to shift, likely accelerating
the move away from just selling software licenses
towards selling access to outcome -based services
powered by agents. Personal branding becomes
even more important as your individual expertise
becomes more discoverable and verifiable by agents
searching for specific skills. And overall, businesses
that effectively leverage agents could experience
significantly accelerated growth by reducing
personnel bottlenecks and operational costs.
So for individuals listening, what are the crucial
non -automatable skills we should be focusing
on cultivating to thrive in this coming agent
era? Okay, number one is deep domain expertise.
Humans still need to provide the direction, the
goals, the context, the AI executes. Semantic
precision is vital, the ability to articulate
complex requirements clearly and unambiguously
to an AI agent. That's prompt engineering, but
at a higher level. Strategic thinking becomes
critical, deciding what is actually valuable
to automate versus what absolutely needs that
human touch or judgment. Adaptability, just being
willing to constantly learn and experiment with
these tools as they evolve almost weekly. And
finally, risk assessment, evaluating when automation
is appropriate and safe and when it's just too
risky for a particular task. And you had to pick
one. What's the single most important skill people
need to cultivate to stay competitive? Strategic
thinking combined with that precise communication
skill for directing agents. So today we've really
gone on a deep dive into this emerging world
of AI agents, and it feels like a true paradigm
shift. We're moving from just responsive chat
to genuinely proactive action, giving AI these
digital hands and eyes. Yeah, and this isn't
just some far -off theory anymore. It's happening
now. It's automating everything from, you know,
online purchases and professional outreach to
even complex content creation workflows. The
ability to effectively define goals and direct
these agents is becoming like a new kind of superpower.
But, as we discussed, with that power comes significant
responsibility. We learned how critically important
it is to use specific prompts, to set clear boundaries,
and to always maintain human oversight and control,
especially when dealing with sensitive tasks
like finances or brand reputation. Exactly. The
future here isn't really about AI replacing humans
entirely. It's much more about humans learning
how to effectively team up with AI. Our domain
expertise, our strategic thinking, our ability
need to communicate precisely those skills are
going to be more valuable than ever in directing
these powerful tools. The era of AI agents isn't
just on the horizon. It feels like it's already
here, actively reshaping how we work, how we
learn, how we interact with the digital world.
The question really isn't whether you should
start using them, but maybe how quickly you can
master them to stay ahead of the curve. Indeed.
You can think of it this way. The future of work,
a lot of it, is being written right now in code
and in prompts, and AI agents are kind of holding
the pen. Our role as humans is to guide that
hand effectively. So maybe a final thought for
everyone listening. What's one specific repetitive
task in your daily routine that an AI agent could
potentially tackle for you, even just partially,
starting this week? Think about it, maybe do
a little research, and start experimenting in
a low -risk way. We really hope this Deep Dive
has given you a clearer picture of what AI agents
are and maybe some actionable first steps for
starting to integrate them into your world. The
key is to stay curious, stay adaptable, and keep
asking how these rapidly evolving technologies
can best serve your specific goals. Thank you
so much for joining us on the Deep Dive. Until
next time, keep exploring, keep learning, and
keep asking the big questions. Out to your own
music.