Okay, so get this, you sent over this YouTube video
and the title is Agents as a Service or a Go-S.
And it's basically saying that this is gonna be
even bigger than sauce.
We're talking millionaires, billionaires in the next year,
just from this new business model.
Yeah, it's pretty amazing, huge potential.
So whether you're a techie or not,
you're gonna wanna pay attention to this AOS thing
because it's gonna change everything.
Yeah, for sure.
So what's interesting about this is it's like
the next logical step in something
we've been seeing for decades.
Think about like back in the day when you had to get
software on CDs and install them on your computer
and the internet came along and sauce changed everything.
Huge.
Cloud-based solutions like Salesforce HubSpot,
it's accessible from anywhere.
But this video is saying AOS is going a step further.
So what's the difference?
So think of it this way, sauce gives you the tools
but OAS uses the tools for you.
It's like imagine a marketing agent
that not only handles your CRM
but generates leads, schedules, appointments,
follows up with people all without you lifting a finger.
Wow.
Like a whole team of tireless,
hyper-efficient employees working for you 24-7.
Okay, that's pretty incredible.
But I did notice in the video that they said
enterprise spending on Brash the last year
was only like one, two billion dollars.
Which doesn't sound like a lot when we're talking about,
you know, this huge potential.
Yeah, you're right.
But that one, two billion is 12 times higher
than it was the year before.
Really?
Yeah, so it's growing like crazy.
Okay.
So it might be small now, but it's gonna be massive soon.
Okay, so we're definitely in the early stages.
That's sure.
But it sounds like there's a huge opportunity here.
Absolutely.
So are all these AI agents the same?
So there's actually two main types.
Okay.
There's vertical agents and horizontal agents.
Okay.
Vertical agents think of them as specialists.
Okay.
And for a very specific role in the specific industry.
And so the video gives an example of Carmen,
which is an AI agent,
specifically for construction project managers.
Okay.
And it automates all those administrative tasks
that are unique to that field.
So super targeted solution for a very specific problem.
Exactly.
Okay, what about horizontal agents?
Horizontal agents are more like generalists.
Okay.
They can adapt to any niche, any function.
Okay.
Almost like a platform that you can use
to build all sorts of vertical agents on.
Gotcha.
And one example the video gave was agency swarm.
Okay.
Which is that type of framework.
Okay, I'm starting to see the difference now.
So is it a matter of like, is one better than the other?
Or?
Not necessarily.
Okay.
It really depends on what your needs are
and what your goals are.
Sure.
You can think of it as the classic trade-off
between performance and time.
Okay.
Vertical agents, they're already pre-trained for their task.
So they can deliver results quickly,
but they're not as customizable.
Okay, so it's like those fancy espresso machines
that you get.
Like they make a perfect espresso,
but if you want a smoothie, you're out of luck.
Exactly.
Yeah, okay.
Horizontal agents, on the other hand,
take a bit more setup and training.
But the payoff is you have way more flexibility
in the long run because you can tailor them
to do exactly what you need.
Okay, got it.
So it's like a specialist versus a generalist.
Exactly.
And you know, they each have their own pros and cons.
Yeah.
Cool.
The video also highlighted some really interesting
real-world IRA solutions that are already out there.
Yeah, for sure.
What were some of the ones that stood out to you?
So one that really stood out to me was 11X.
Okay.
They're developing AI-powered sales agents
that can automate the entire sales process.
Wow.
And they've raised $74 million in funding.
Wow, that's a lot of money.
It is a lot of money.
But the video also said that you don't need millions
of dollars to get started with IS.
Right, the creator even kind of implied
that maybe 11X has too much funding.
He really emphasized that the real key
is to focus on delivering value
and solving real problems, even if you're starting small.
I love that.
It's very empowering to hear that you don't need to be,
you know, some Silicon Valley hotshot to get in on this.
Yeah, exactly.
What were some of the other examples that you liked?
So we already talked about Carmen,
the AI agent for construction project managers.
Yeah.
I think it's a perfect example of how powerful
it can be to target a very specific niche.
There's also Normy, which is designed
for regulatory compliance teams.
Okay.
Shows that AS can go way beyond just sales and marketing.
There are opportunities in all kinds of industries.
Yeah, it seems like the possibilities
are pretty much endless.
Yeah.
The video also mentioned Devin,
which is a development agent that costs $500 a month.
Yeah.
I'd be curious to hear more about that one.
Yeah, Devin is intriguing,
but I honestly think that deserves a deep dive of its own.
Okay.
Maybe we can do that in a future episode.
Okay, I'm putting that on the list.
All right, cool.
So let's say someone is listening to this
and they're like, okay, I'm sold.
How do I build my own vertical agent?
Okay.
What are the first steps?
So the video laid out three key ingredients.
Okay.
You need data, you need industry expertise,
and you need resources.
Okay.
So let's start with data.
Okay.
The quality of your AI agent is completely dependent
on the data that you train it on.
So step one is to gather valuable internal data
that's relevant to the task you wanna automate.
That makes sense, but what if you don't have access
to that data right away?
That's a great question,
and the video actually addressed that.
Okay.
One suggestion was to partner with someone
who does have access to the data.
Okay.
Or even offer to build the agent for a client
in exchange for access to their data.
Oh, that's smart.
Yeah, so you're getting paid to develop your solution
and you're gathering the data you need at the same time.
Brilliant.
Okay, so data is covered.
What about industry expertise?
You need to have a really deep understanding
of your target customer, their workflow, their pain points.
You need to know their processes inside and out
so you can design an agent
that truly solves their problems.
Gotcha.
So if someone isn't an expert in the field,
they need to partner with someone who is.
Absolutely.
Okay.
And remember, you don't need like a huge investment
to get started.
Okay.
You can leverage your existing skills network
and resources to build your first agent.
Yeah.
In fact, the video emphasized that a lot of times
you can secure funding from your first client.
Okay.
Essentially getting them to pay for the development
of the solution they need.
Okay, that's a game changer.
All right, so we've got our data.
We've got our industry expertise lined up.
Right.
What are our options for actually building the agent?
So the video presented three main approaches.
You can use a framework, you can leverage a platform
or you can develop a custom solution.
Okay.
Each approach has its pros and cons.
It really just depends on your technical skills
and how complex of an agent you're building.
Okay, let's dive into those.
Okay.
So using a framework like agency swarm,
which we mentioned earlier,
can save you a lot of time and effort
because it handles some of the lower level
technical details for you.
Gotcha.
But you'll still need some development experience
to make it work.
So for people that are comfortable
with a little bit of coding,
a framework would be a good way to go.
Yeah, exactly.
Okay.
What about using a platform?
Platforms like Google Clouds, Vertex, AI,
Amazon's Bedrock are incredibly powerful.
They handle all the infrastructure for you
so you can focus on building the agent
without having to be like a coding wizard.
Right.
But they can get pretty expensive as you scale up.
Okay, so maybe not the best if you're just starting out,
but good once you get going a little bit.
Yeah, exactly.
What about the third option,
building a custom solution from scratch?
That gives you complete control.
Mm-hmm.
Complete flexibility,
but it's definitely the most challenging path.
Yeah.
You'll need a really strong development team,
a lot of technical expertise to pull it off.
So ultimate freedom,
but also requires the most effort.
Exactly.
So what did the video recommend as like the best approach?
So the creator recommended starting with a framework
or a platform,
to validate your idea and get to market quickly.
Then as you grow and your needs become more complex,
you can consider transitioning to a custom solution.
Okay, so it's like a, you know,
you start small and you scale up.
Exactly.
I like it.
All right.
So we've built our agent,
we're ready to launch it into the world.
How do we actually price this AS solution?
So that's the million dollar question, right?
Yeah.
Luckily, the video gave us four key pricing models
to consider.
Okay.
Licensing usage-based, outcome-based, and hybrid.
Okay, let's break those down.
Okay, so licensing is pretty straightforward.
Yeah.
Clients pay a one-time setup fee
or a recurring monthly subscription to use your agent.
Okay.
It's easy to explain,
but the downside is it doesn't always reflect
the actual value that the agent is delivering.
Okay.
So maybe good for the early stages,
but not ideal for the long-term.
Right.
Okay, what about usage-based pricing?
Usage-based pricing is really common in the SAWS world.
So you charge customers based on how much they use the agent.
So maybe by the number of tasks completed
or the amount of data processed,
it's very scalable, especially for larger clients.
Okay.
But it requires consistent agent use to be profitable.
So not a good fit for people that only need it occasionally.
Right.
Okay, now this outcome-based pricing,
that sounds interesting.
This is where AOS gets really exciting.
Okay.
You actually charge clients based on the results
that the agent achieves.
Okay.
So you could charge per appointment,
booked, per lay generated, per website, built.
Wow.
It's a win-win,
because your clients are only paying for tangible results.
And you're incentivized to make sure
that your agent is actually working.
Exactly.
I like that a lot.
Okay, what about the hybrid model?
A hybrid model combines elements
of multiple pricing strategies.
Right.
So for example, you might charge a base fee
plus an additional fee per outcome achieved.
Okay.
So it offers flexibility,
but it can be a little more complex to explain to clients.
Sure, okay, so lots of different options,
depending on the specific agent and the target market.
Exactly.
All right, so now let's get down to the nitty gritty.
If someone wants to ride this AS wave
and find success in 2025,
what's the roadmap look like?
So the video laid out a pretty clear step-by-step plan
that anyone can follow.
So the first step is to find your niche.
All right, how do we do that?
It's all about leveraging what you already know.
What are your existing skills,
experiences your network,
what industries are you familiar with,
what problems have you encountered
that an AI agent could solve?
Okay.
Where can you offer a unique solution
that no one else is providing?
So instead of trying to be everything to everyone,
we should focus on where we can really excel
and offer value.
Exactly.
Okay, what's next?
So once you've identified your niche,
the next step is to pinpoint the specific problem
that you wanna solve for your agent.
What makes a problem a good fit for an AS solution?
So the video highlighted a few key characteristics.
Okay.
The problem should be recurring.
Okay.
Meaning it happens frequently,
it should be dynamic.
Okay.
Meaning it involves some level of variability or complexity.
Gotcha.
And it should be something that traditional automation tools
can't really handle.
It needs to require some level of intelligence,
some decision making.
Okay, so not just like simple repetitive tasks,
but something that requires a little bit more
Exactly.
Adaptability.
Yeah.
Gotcha.
Okay, what's the next step on our roadmap?
This is where it gets interesting.
Sell first, build second.
Wait, hold on.
Do we usually build the solution
and then try to sell it?
Yeah, that's the traditional approach.
But the video argued that selling first
is a much smarter strategy.
Because if you can find a client
who is willing to pay for your solution
before you build it,
you're validating your idea,
you're reducing your risk,
you're ensuring that you're creating something
that meets a real market need.
You're getting paid to develop the solution.
Exactly.
I love that.
Okay, so we've secured our first client,
what happens next?
Time to build a minimum viable product.
Or an MVP.
An MVP.
Can you remind us what an MVP is?
An MVP is a stripped down version of your product.
Okay.
That includes just the essential features
that are needed to solve that core problem.
The goal is to get something functional
in the hands of your client as quickly as possible
so you can start gathering feedback.
Okay.
And iterating based on their needs.
So it's not about building the perfect product
right out of the gate.
Right.
It's about getting something out there
and getting feedback.
Exactly.
I like it.
What's the next step after the MVP?
So the video called a productizing your agent.
Okay, what does that mean?
Basically taking everything you learned
from building that MVP.
Yeah.
And turning it into a repeatable scalable solution.
Yeah.
Identifying those core features
that are gonna be consistent across different clients.
Yeah.
And making it really easy to customize the agent
for different needs.
Maybe through templates or configuration files.
So making it adaptable so you can onboard
new clients without having to start from scratch every time.
Exactly.
Okay, cool.
What's next?
Next step is to set up a system
for evaluating your agent's performance.
Okay.
The video called these evils.
Yeah, okay.
Emphasize that this is crucial for continuous improvement.
You need to be able to track how your agent is performing.
Identify areas where it can be optimized.
Yeah.
Make adjustments as needed.
So it's like this constant feedback loop.
Exactly.
Where your agent is always getting smarter.
Yeah.
And the final step on our roadmap.
The final step is to scale your solution.
Right.
Once you have that productized agent
that's delivering results, ramp up your marketing efforts.
Maybe hire some team members.
Get your agent in the hands of as many clients as possible.
Okay, that's where things get really exciting.
Yeah.
But before we get too caught up in all the hype,
the video shared a real world case study
to show what a success can actually look like in practice.
Yeah.
We got to hear from Chase,
an entrepreneur who's built a successful AS voice solution.
Okay.
And is generating impressive revenue.
Right.
Tell me more about Chase.
So Chase's story is a perfect example
of how to leverage niche expertise
to build a valuable AS solution.
Okay.
He combined his experience in the solar industry
with AI to create a voice agent.
Okay.
That automates the sales process for solar companies.
Oh, that's smart.
So he found a problem in an industry
that he knew really well.
Exactly.
Build a solution tailored to that specific problem.
Yeah.
Very cool.
What were some of the key takeaways from his experience?
One thing that stood out was
he didn't try to reinvent the wheel.
Okay.
He integrated his AS solution
with systems that were already out there.
Okay.
Leveraged his existing CRM automation workflows.
Gotcha.
He also focused on creating templates
to make it easy to customize his solution
for different clients.
Okay, so that echoes that productization thing
that we were talking about.
Exactly.
How else did he do?
He understood the importance of value in results.
Okay.
So instead of just charging a flat fee,
he offered two models.
Okay.
A build and release model,
and then also a growth partner model,
where he would actively manage the AS solution
for his clients.
Okay.
And he would share in the revenue that it generated.
Wow.
So he aligned his incentives with his clients' incentives.
Exactly.
That's really smart.
The video actually included a snippet
of Chase's AI agent in action.
It did.
We'd love to hear that if we can.
Yeah, absolutely.
Okay.
So first we hear Grace Chase's AI sales assistant
introducing herself.
Okay.
Then we hear Grace speaking Spanish
demonstrating her multilingual capabilities.
Wow.
Next up, she delivers an impressive rap
about the power of AI employees.
Really?
It's pretty entertaining.
That's amazing.
Okay.
And then finally, we hear a seamless transfer to Jack,
a specialized sales agent.
Okay.
Showcasing the multi-agent capabilities of his system.
Wow.
And it all come together like that,
really brings the concept to life.
It's clear that he's built something really special.
Did he share any revenue numbers?
He did his best month.
He brought in close to $100,000.
What?
Yeah.
And he's consistently seeing between $30,000 and $60,000
per month.
That's incredible.
And all without millions of dollars in funding.
Yeah.
That just proves that you don't need a ton of money
to succeed in this space.
Right.
You just need a great idea and the right skills
and the willingness to put in the work.
For sure.
This has been such an eye-opening deep dive so far.
It has.
We've gone from like the very basics of understanding
what Aries is.
Yeah.
How it's different from, you know,
all these other traditional software models
to seeing real-world examples of people building
thriving businesses with these AI agents.
Yeah.
It's really exciting stuff.
And we've only scratched the surface.
Right.
We haven't even talked about what he said.
Those horizontal agent platforms
that can build vertical agents.
From a single prompt.
Yeah.
It feels like we're on the verge of like
a massive technological shift.
Absolutely.
It's really exciting.
It is.
But there's so much more to explore.
Yeah.
We still need to talk about what this means
for the future of work.
Right.
What does this mean for the broader economy?
It's exactly.
We'll be tackling all of that and more
in part two of this episode.
So stay tuned because this deep dive
is just getting started.
Yeah.
This is just the beginning.
Welcome back.
I don't know about you, but my head is still spinning
from all the possibilities we talked about
in the first part of this deep dive.
Yeah.
There's a lot to unpack.
For sure.
Like just hearing Chase's story really
drove home the point that this AI thing is not
just some futuristic fantasy.
It's happening right now.
People are already making serious money with it.
Absolutely.
But it also makes you wonder like what
happens when these AI agents get even more powerful.
Yeah.
And sophisticated.
Well, what does that mean for the future of work?
Right.
Are we all going to be replaced by robots?
Well, the video creator definitely
had some interesting thoughts on that.
OK.
He predicted that both vertical and horizontal agents
are going to have a significant impact.
OK.
But not necessarily in a way that like eliminates
human jobs altogether.
OK.
He sees a future where some people
will gravitate towards those highly specialized vertical
solutions, while others will embrace the flexibility
and customization of those horizontal agent platforms.
So not a complete AI takeover, but a shift in how we work
and the kinds of jobs that are in demand.
Exactly.
OK.
He even suggested that A-Rest could
lead to a surge in entrepreneurship and innovation.
Oh, interesting.
Like imagine a world where anyone with a good idea
and access to the right AI tools could build and scale
a business without needing a huge team
or a ton of upfront investment.
That's a pretty amazing thought.
It is.
It's really empowering.
But let's be realistic.
It's not all sunshine and rainbows, right?
Of course not.
There have to be some potential downsides
to this whole worst thing.
Yeah, for sure.
As with any disruptive technology,
there are going to be challenges and adjustments
along the way.
Sure.
One of the biggest concerns is the potential impact
on the workforce.
Right.
If AI agents can automate entire processes,
what happens to the people who used to perform those tasks?
Yeah.
Are we going to see massive job displacement?
Will certain skills become obsolete?
Those are all valid questions.
Yeah.
And they're questions that we need to be asking
as this technology continues to evolve.
So how do we address that?
Are there things that we can do to prepare for those changes?
Well, I think first and foremost,
we need to be proactive in thinking
about how we can retrain and reskill workers
so that they can thrive in this new landscape.
Maybe we'll see a shift towards roles
that require more creativity, critical thinking,
human connection.
Right.
The things that AI can't replicate.
Exactly.
Those are the skills that are going to be in high demand.
So it's not just about learning how to code.
Right.
It's about developing those uniquely human skills.
Yeah.
I'm hoping that will be the case.
Me too.
It would be incredible to see AS create new opportunities
for people to do work that is more meaningful and fulfilling.
Yeah.
Absolutely.
The video creator also had a pretty mind-blowing prediction
about the future of these horizontal agent platforms.
Oh, yeah.
Tell me about that.
So he envisioned a future where these platforms could
essentially create highly effective vertical agents
from a single prompt, almost like magic.
OK.
Imagine being able to say, I need an agent that does X.
And the platform just builds it for you ready to go.
That sounds incredible, but also a little intimidating.
It is a little bit intimidating.
If it's that easy to create these powerful AI agents
that make it even harder for people to keep up.
It's a valid concern.
But the creator also believed that these agents will
be able to learn and improve over time.
OK.
So they essentially become even more valuable and efficient
as they gather more data and experience.
So maybe it's not about competing
with these super intelligent AI agents,
but learning how to work alongside them
and leverage their capabilities to our advantage.
I think that's a great way to look at it.
OK.
So the video also mentioned something
called thermodynamic computing.
And kind of suggested that it could have an even bigger
impact than AI and then your future.
Right.
Have you heard much about that?
To be honest, that's a relatively new concept to me.
It wasn't discussed in a lot of detail in the video.
OK.
It sounds fascinating, though.
Yeah.
It does.
Maybe it's something we can explore in a future deep dive.
I'm adding that to our list, all right.
So we've talked about the potential for NES
to disrupt industries, create new opportunities,
and even transform the nature of work itself.
It's a big deal.
It is a big deal.
But as with any powerful technology,
there are bound to be some challenges
and ethical considerations that we need to address.
For sure.
We've touched on some of those already,
like the potential for job displacement,
the need for retraining and reskilling.
Right.
But what are some of the other ethical considerations
that come into play with an AS?
Well, one concern is the potential for bias in AI
algorithms.
OK.
If these agents are trained on bias data,
they could perpetuate and even amplify existing inequalities.
That's a really important point.
It is.
So we need to make sure that the data that's
used to train these agents is representative.
Yeah.
Unbiased.
Absolutely.
So that they're not making unfair or discriminatory
decisions.
Right.
And we need to be vigilant about monitoring these agents
to make sure that they're being used ethically
and responsibly.
OK.
As they become more sophisticated,
we need to develop clear guidelines and safeguards
to prevent any unintended consequences.
It sounds like we need a combination
of technological advancements and thoughtful regulation.
I agree.
To really navigate this new landscape successfully.
Yeah.
It's going to be a group effort, for sure.
This has been such a thought provoking conversation.
It has been.
We've covered so much ground from the technical details
of what AS is to the potential impact on the workforce,
the ethical considerations that we need to address.
Right.
It's clear that AS is a powerful force that's
going to shape the future in some really profound ways.
It really is.
And as we've discussed, it's important to approach
this technology with both enthusiasm
and a healthy dose of caution.
I agree with that.
We need to embrace the possibilities
while being mindful of the potential challenges.
And most importantly, I think we need
to engage in these open and honest conversations
about how we want to shape this future together.
Right.
It's not something that's going to happen to us.
It's something that we're all creating together.
Exactly.
So let's keep learning.
Keep exploring and keep asking those tough questions.
I love that.
And in the next part of this deep dive,
we'll delve into even more fascinating aspects
of this A-V-S revolution.
So stay tuned.
Yeah.
Stick with us.
Welcome back to the deep dive.
We've covered so much ground already.
It's kind of mind blowing to think about how quickly
this is all evolving.
I know.
It's really incredible.
And it's exciting, but I'm also feeling
like a sense of responsibility.
We need to make sure that we're approaching this technology
thoughtfully and ethically.
Yeah.
Absolutely.
We're not just about the cool factor.
Right.
It's about shaping a future that benefits everybody.
Yeah, for sure.
We need to be proactive about addressing
those potential challenges and making sure
this is used for good.
Yeah.
And one of the things that we talked about last time
was the impact on the workforce.
Right.
Like, that's the big question.
Is AI going to replace human jobs?
Yeah.
That's the question on everybody's minds.
So what are your thoughts on that?
Well, I don't think it's a simple either or scenario.
OK.
AI agents are going to automate certain tasks
and processes, which could lead to some job displacement.
Yeah.
But it's also important to remember
that AI can create new opportunities
and even enhance existing roles.
So maybe it's less about AI taking over
and more about humans and AI working together in new ways.
Exactly.
Yeah.
I think the key is to focus on developing skills
that complement AI.
OK.
Things like creativity, critical thinking,
emotional intelligence, complex problem solving.
OK.
Those are going to be the schools that are in high demand.
Right.
So we need to adapt and evolve, just like we've always
done, as new technologies emerge.
Exactly.
Humans are incredibly adaptable.
That's true.
And I think it's also important to remember
that AI is ultimately a tool.
It's up to us as humans to decide how we want to use it.
Right.
And what kind of future we want to build with it.
It all comes back to us.
So how can we shape the future of AI?
I think one of the most important things
is to have these open and honest conversations
about the ethical considerations.
Right.
You know, we need to establish clear guidelines and safeguards
to make sure that these agents are used responsibly
and that we're prepared to address
any unintended consequences.
You mentioned bias in AI algorithms last time.
Right.
Is one potential concern.
So how do we ensure that these agents are fair
and don't perpetuate existing inequalities?
Yeah.
So it starts with the data that's used to train the agents.
OK.
We need to make sure that the data is representative
and unbiased.
Right.
And that it reflects the diversity of the population.
OK.
So it's not just about the technology itself,
but also about the people who are developing and deploying it.
Exactly.
We need diversity and inclusion at every stage of the process.
I love that.
OK.
So we've talked about a lot.
Covered a lot of ground.
But what's the one key takeaway that you hope our listeners will
walk away with today?
I hope they remember that the future of the cities
is not predetermined.
It's something that we are all shaping together.
How are for the choices we make and the actions that we take.
Right.
So let's be intentional about building a future.
OK.
Where AI empowers us all.
That's a great message to end on.
Yeah.
Thank you so much for joining us on this deep dive
into the world of Astier.
Oh, it's been a pleasure.
It's been an incredible journey.
It really has.
And thank you to all of our listeners for tuning in
and joining us on this exploration.
Yes.
Thank you so much for listening.
Remember, the future is what we make it.
It really is.
So let's keep learning.
Yeah.
Keep questioning.
And keep pushing the boundaries of what's possible.
Until next time, keep diving deep.