Imagine an AI that doesn't just answer your questions.
Instead, it proactively browses the entire web,
digs into complex topics, meticulously analyzes
data, and then creates completely original content,
all on its own. Yeah, it's really like having
a virtual assistant that actually thinks. It
executes these complex multi -step tasks, freeing
you up to just observe. or maybe grab another
coffee. That's a tempting thought. OK, let's
unpack this a bit. Welcome to the deep dive where
we try to cut through the noise and bring you
the insights that really matter. Today, we're
taking a really deep look at chat GPT's agent
mode. Right, and here's where it gets super interesting
for anyone using AI today. This isn't just your
average chatbot, not even close. We're talking
about an AI that can handle really intricate
multi -step workflows with a surprising amount
of autonomy. We'll explore what agent mode actually
is, kind of how it works under the hood, and
crucially, four pretty surprising and actionable
ways you can use it right now. And it's not all
perfect. We'll also look at its current limitations,
the reality of, say, website access, and those
really key security considerations you need to
be aware of. This deep dive is essentially about
giving you a clear roadmap, a way to really leverage
this new phase of autonomous AI. So let's start
with the basics. What exactly is agent mode?
And how is it fundamentally different from the
chat GPT we might be used to? Okay, so at its
core, it's Chat GPT acting as what you might
call a semi -autonomous AI entity. Think of an
AI entity as basically an AI that can perform
tasks independently once you give it a clear
goal. What makes Agent Mode special is how it
coordinates things. It uses various tools together
like web browsing to search the internet, its
own data analysis tools, you know, what used
to be code interpreter for crunching numbers,
that image generation with DLE3, and even connections
to external apps you use. all working together
to achieve a bigger, often pretty complex objective.
That's a really key distinction then. The core
difference is the autonomy. Before, we were almost
constantly guiding it turn by turn. With Agent
Mode, you set the overall task like a single
high -level goal and the AI takes over. It autonomously
plans. breaks down the work, decides which tool
to use when, and just keeps executing. It might
work for 10, 20 minutes, maybe longer, without
you needing to constantly jump in. That's a big
shift. It really is. Think about standard chat
GPT. It's turn -based Q &A. You ask. It answers.
It waits. Browse mode was better. It could summarize
a few search results. But agent mode, it's a
whole different beast. It's self -plans. It can
browse dozens of websites if needed, analyze
what it finds, synthesize it all, and then deliver
a complete result. It's a huge leap in let's
say, say, cognitive complexity for AI. And to
get started, you will need a chat GPT Plus account.
Users usually get a limited number of agent sessions
per month. You turn it on using the paperclip
or plus icon you already see in the chat. And
you're always in control. You can stop at any
time or even take over its browser if you need
to nudge it. OK, we've covered what it is, how
it works. But what does this shift really mean
for us? How does Agent Mode change our role when
we're working with AI? You set the goal, the
AI plans, and executes autonomously. You become
the strategist. Right, shifting roles. OK, let's
talk use cases. For complex creators, finding
fresh, trending ideas is, well, it's a constant
struggle, isn't it? Huge time sink. Agent Mode
sounds like it could become your tireless trend
researcher. Absolutely. That's one of its real
sweet spots. It's like having a dedicated research
team ready to go. But here's the key. The quality
you get out is directly tied to the quality of
your prompt. You can't just say, find me content
ideas. That's too generic. You need to be really
specific. Give the AI a clear role, like act
as a trend analyst for my personal finance YouTube
channel targeting Gen Z in Vietnam. That specificity
is crucial. So it's more like writing a detailed
brief than just asking a question. You define
the context, the exact task, maybe data collection
across Reddit, YouTube, Google Trends, specific
blogs, then the analysis, and even how you want
the recommendations formatted. Like a markdown
table with video topic, keywords, unique angle.
Precisely. Think about how you'd brief a human
analyst. You'd give them all that detail, right?
The audience, the platforms, the desired output
structure. The more detailed you are, the better
the agent understands its mission. Then, once
you hit go, you actually see it working. It systematically
visits websites, runs searches, reads articles,
takes notes. It's kind of fascinating to watch,
like seeing research happen and fast forward.
That sounds incredibly powerful. But what about
roadblocks? What happens when it hits a snag,
like a website blocking its access? That must
happen quite a bit. Oh yeah, that's a very real
thing. Lots of sites have... pretty strong anti
-bot measures. But the agent is designed to adapt.
It doesn't just give up. It logs the error, it
reevaluates its plan, and it tries alternative
routes. Maybe it looks for other sources citing
that blocked content or finds public aggregators
that summarized it. It has these little problem
-solving loops built in. You know, I still wrestle
with prompt drift myself sometimes. Trying to
make a prompt so perfect it covers every possibility.
It can get really complicated, really fast. How
do you avoid that? Yeah, that's a common challenge.
Finding that balance between detail and clarity
definitely takes some practice. But when it works,
the output can be incredibly rich. You get a
detailed report, often with really specific ideas
like investing with one to five million VND month,
ETF roadmap for beginners 2025, or BNPL boom.
How does buy now pay later harm your wallet?
These aren't just generic suggestions. They're
tailored data -driven insights based on the criteria
you set. And a good tip there is probably not
to just take the first output as final, right?
Like if the YouTube research seems a bit thin,
you can follow up. Ask it to dig deeper into
comments for audience questions. Exactly right.
It's often an iterative process. Refine, ask
again, dig deeper. This use case done well can
genuinely save you, say, three, four hours of
manual research for each piece of content. That's
a huge efficiency gain. So thinking about this
trend finding. What's the key takeaway to make
it truly effective? Just asking for trends isn't
enough. Detailed prompts and smart follow -up
commands are crucial for success. You guide the
process. Okay, let's shift gears. Conversion
rate optimization or CRO? For many smaller businesses,
getting a professional website audit feels out
of reach. It's expensive. But CRO, just improving
the percentage of visitors who take an action,
like buying something, is so important. Can agent
mode step in here? Act like a virtual CRO expert.
Yes, this is another really practical application.
And you're right, professional CRO audits cost
a lot but offer huge value. Agent mode can give
you a surprisingly detailed analysis here. The
key, again, is that detailed prompt. You'd instruct
it. Act as a UX UI expert with 10 years of experience.
And UX UI, of course, is user experience and
user interface, basically. How easy and pleasant
your site is to use. Giving it that specific
senior persona changes how it analyzes things.
And then the mission. Analyze your e -commerce
site, find the friction points, the bottlenecks,
and suggest concrete ways to improve click -through
rates or CTR and overall conversions. Absolutely.
Your prompt should detail the outlaces process.
Maybe ask it to start with foundational research
on industry best practices for your specific
niche. Then move to specific page analysis, really
looking at things like your product thumbnails,
the clarity and placement of your call -to -action
buttons, you know, those buy now or learn more
buttons, and how well you communicate product
benefits. You're guiding its focus. And crucially,
specifying the output format helps a lot, like
a structured Markdown table. product area, specific
issue, negative impact, and a suggested fix.
Exactly. And this is where it shines. Imagine
getting specific, actionable recommendations
like replace the rotating hero carousel with
a static image. They usually perform better.
Or fix cropped product thumbnails so people can
actually see what they're buying. Or always show
prices clearly on product cards. Maybe even move
ad to cart to be the main CTA on product cards.
These aren't vague ideas. They're concrete suggestions.
So, after maybe 10 -15 minutes, you get this
detailed audit. You might point out inconsistent
product images or benefit statements that are
too generic, or even a button color that just
blends into the background. These are the kinds
of small changes that can directly impact revenue.
What's the biggest risk if you don't put the
effort into a detailed prompt for this kind of
task? You'll just get vague, generic advice,
not specific, actionable solutions tailored to
your site. Okay, market research. Before launching
anything new, understanding customer pain points
is everything, isn't it? Agent mode sounds like
it could automate reading hundreds, maybe thousands
of customer reviews to find those hidden frustrations
and desires. Oh, this is a huge time saver. Huge.
And it helps take some of the human bias out
of the research process, too. You just need to
clearly define the scope. Tell it your product,
like a new smart thermos, and task it to find
common problems and wishes for similar products
already out there. That competitive angle is
vital. And you can point it towards specific
data sources, like tech blogs, YouTube reviews,
forums where people actually talk about these
things. You could even build in a backup plan.
Right? Like if we can't access Amazon reviews
directly, tell it to look for review roundup
articles instead. Exactly. You're building resilience
into its research plan. Then it gets to work.
It collects and pulls out the common complaints,
the common praises, and starts grouping them
into themes, things like battery life, durability,
connectivity issues. It also identifies sentiment
keywords of those words that show positive or
negative feelings, like frustrating or unreliable
versus delightful or robust. It understands the
emotion behind the words. And the output could
be something like a table showing the complaint
theme, maybe an example quote from real review,
the mentioned frequency, how often it comes up,
and importantly, a proposed feature solution.
Could it even generate data for a pie chart,
maybe, to visualize the biggest issues? Yeah,
absolutely. So for that smart thermos example,
it might find... Battery life shorter than expected
is, say, a 22 % complaint. And right next to
that, it suggests a solution. Increase battery
to three, four hours minimum, add USB -C fast
charging, maybe a wireless charging coaster.
Or it finds app Bluetooth sync is unreliable,
comes up 20 % of the time, and suggests use BLE
5 .3, add an offline mode, build robust reconnection
logic. It connects the problem directly to a
potential solution. Manually reading all those
reviews takes days. And honestly, it's easy to
develop confirmation bias, right? You start noticing
the complaints that match your own ideas. Absolutely.
Human bias is a killer here. Agent mode just
looks at the patterns, the frequency. It doesn't
have preconceived notions. It just says, battery
life is the biggest issue, mentioned 22 % of
the time. It gives you that objective view. How
does this directly shape product development
then? It seems like more than just a feature
list. It pinpoints real user problems, which
directly guides creating features people actually
want and need. Let's talk creative work. If you're
designing products or marketing materials, knowing
what designs are actually selling, not just what
looks cool or what you personally like, is super
important. Can agent mode help research successful
designs and even create visual inspiration, like
mood boards, those collections of images that
set a style? Exactly. This is about moving from
just subjective taste to... data -informed creativity.
But again, the prompt needs specific success
criteria. Don't just ask for popular designs.
Define popular. Maybe it's designs with over
100 sales or 50 positive reviews uploaded in
the last year. You need those metrics. So you
give it your niche, say, programming t -shirt
designs, tell the success metrics, and ask it
to collect images or detailed descriptions, and
then analyze each one. Look at the style, colors,
fonts, graphics, like a design critic crossed
with a data analyst. Precisely. The agent then
browses platforms, maybe Etsy, Redbubble, filters
based on your rules, analyzes the sales data,
the reviews, and identifies common threads among
the winners. Now initially, it might just give
you back a data table describing the designs,
like design A, works on my machine, meme, simple
text, dark shirt. Useful, but not visual. Ah,
okay. So getting the actual visual mood board
requires an extra step, doesn't it? Yes. This
is where you smartly switch tools within the
agent mode workflow. You tell it, okay, based
on that analysis table you just made, now use
daily3 to create a visual mood board. And daily
three, for listeners, is that AI model that generates
images from text descriptions. You could ask
for, say, six to eight t -shirt mock -ups showing
the top design styles using diverse colors and
fonts, all based on the sales data it just crunched.
So the result isn't just a pretty picture. It's
a mood board grounded in what's actually proven
to sell. That helps make much safer, more effective
creative choices. Less guesswork. And this whole
concept gets even more powerful when you bring
in connectors. Ah, yes. Connectors. For anyone
unfamiliar, connectors are basically chat GPT's
way of linking up and talking to other apps you
use. Right. You can connect it to things like
Google Drive, your calendar, Gmail, Slack, Microsoft
Teams, a whole range of business tools. Setting
it up is usually pretty straightforward. Go into
settings, find connectors, pick your app, and
grant the permissions it needs. Whoa. Okay, imagine
scaling this up. Once it's connected, you could
automate some really complex workflows, like
picture telling it. Every Monday morning, research
the blogs of my top three competitors, summarize
any new articles they published, analyze their
content strategy shifts, save that full report
to a Google Doc, and then email me notification
with the link. Exactly. Or, based on that trend
research you did earlier, generate 10 content
ideas, and for each one, create an event in my
Google Calendar, scheduling it for next week.
Hmm. The possibilities for automating routine
tasks are genuinely transformative. It really
opens things up. That level of integration sounds
amazing, but it also brings us back to the security
point, doesn't it? You're giving it keys to your
kingdom, essentially. Yeah, absolutely. That
power comes with responsibility, which makes
our earlier caution about data access even more
important when you start using connectors. So
what's the SQL sauce for getting that visual
mood board, not just the data points? It's that
two -step process. Analyze first. Then use daily
E3 for image generation based on the Ollis's.
OK, we've seen some incredible potential here,
but let's be honest. We need a clear eyed look
at agent modes performance right now. Its strengths.
but also its weaknesses. What does it really
nail? Definitely. It truly excels at those massive
research projects, the kind that would take a
human hours, maybe days. It's fantastic for repetitive
tasks, for spotting patterns across lots of different
data sources, and for structured analysis. And
a key thing is, it follows complex instructions
step by step without skipping things, which humans,
well, we sometimes do, especially when tasks
get tedious. That consistency is a big plus.
But it's not flawless. What are the current limitations?
Where does it still kind of struggle? Well, as
we mentioned, website blocking is a significant
one. Many major sites, especially e -commerce
and big media outlets, have strong anti -bot
shields, and the agent can't always get through.
Sometimes the results might be incomplete or
it might pull in irrelevant info. So human verification
and editing are absolutely essential. It's not
quite set it and forget it yet. And I've heard
the timing can be a bit unpredictable, too. Like
sometimes a task takes way longer than you'd
expect, and occasionally it might just Stop,
mid -task, without a clear warning, without a
frustrating. Yeah, that can happen. It's still
evolving tech. And that brings us to the critical
safety and security points. Remember, the agent
sees whatever you give it permission to see.
If you connect your Gmail, it can read your email.
If you connect Drive, it sees those files. It
can also view websites you happen to be logged
into within its own browser session. So if you're
logged into sensitive accounts, technically,
it could see that page content. OpenAI says they
don't store this interaction data long -term,
which provides some comfort. But caution is definitely
the best approach here. We'd strongly recommend
not connecting super sensitive business accounts,
things with private customer data or financial
info, at least until the technology matures more
and the security aspects are even clearer. Agreed.
Our honest take right now. Agent mode is excellent
for research, analysis, ideation tasks, where
you can easily check, refine, and ultimately
control the output. We wouldn't yet fully trust
it for tasks that could directly harm your business
if something went wrong, like, say, having it
communicate directly with clients or execute
financial transactions. Maybe down the road,
but not quite yet. So boiling it down. What's
the single most critical thing to keep in mind
when using agent mode? Always verify the results
and be extremely cautious with sensitive data
access. Trust, but verify. Sponsor. So wrapping
this up, what's the big picture here? What does
this all really mean? OK. This deep dive really
suggests that the era of, well, maybe AI employees
isn't quite the right term, but AI assistants
that can act autonomously, that era has truly
arrived. Agent mode feels like a fundamental
shift in how we approach work, moving beyond
just using tools to having genuine automated
help. Yeah, and it's crucial to frame it correctly.
It's not about AI replacing human creativity
or strategic thinking. Not at all. It's about
freeing you up from the time -consuming, often
tedious, research and execution parts of the
job so you can focus your energy on the higher
-level strategic decisions, the insights, the
things that really drive growth and innovation.
It seems clear that businesses and even individuals
who start experimenting with these autonomous
AI AI agents now are likely going to gain a significant
edge, the potential to save time, and just the
sheer scale at which it can process information.
It's undeniable. It really is about working smarter.
Definitely. So your roadmap to becoming an agent
mode master really starts right now. Try one
of the use cases we talked about. Maybe start
with the website analysis on your own site, something
you know well. Begin with relatively simple tasks,
build complexity gradually, and critically, always.
Always verify the results. Don't just trust them
blindly, especially at first. And experiment.
Play around with different prompts. Change the
role you sign the AI. Tweak the context. Adjust
the output structure. See what works best for
your specific needs and goals. And, as we keep
saying, always prioritize security when you're
thinking about connecting accounts. Be mindful
of what you're granting access to. The technology
isn't perfect, no doubt. It's still evolving
fast. But it's powerful enough. right now, today,
to be a genuine game changer in how you work,
and maybe even how you think about problems.
Just remember this framing. AI handles the heavy
lifting of research and execution. You handle
the vision, the strategy, the critical thinking,
and the human touch. That's a really powerful
combination for anyone looking to innovate and
work smarter.