FDE: The $1M/Year AI Job Explained

I know it's crazy, but there are people making a million dollars a year as Ford deploy engineers. But what exactly is an

deploy engineers. But what exactly is an FDE? I know you've probably seen it, but

FDE? I know you've probably seen it, but I feel like a lot of people aren't clear as to what it is and how they could become one. [music] Well, in this

become one. [music] Well, in this episode, I brought on my friend Voss.

And Voss is a leading expert when it comes [music] to FTEEs with his company, Veric Agents. And in this episode, he

Veric Agents. And in this episode, he gives you his entire playbook, how you could become an FTE in 30 days. Now,

this episode is for people who want to become an FTE, but also for people who are just interested in what it [music] means, how they can actually use FTEEs

in their business to be make more money, to be more productive. And I just think this is the clearest episode, the clearest piece of content on the

internet, the clearest master class for how to understand clearly what an FDE is, how you could become one, and why it

matters. Enjoy the episode, and I'll see

matters. Enjoy the episode, and I'll see you at the end.

[music] Voss is here. And Voss, by the end of this episode, what are people going to learn?

They're going to learn exactly how to break into forward deployed engineering or become a better FTE in 30 days. The

full road map for AI forward deployed engineering.

And I don't feel like this has been shared anywhere. I feel like the term

shared anywhere. I feel like the term forward deploy engineer is just like on my X feed everywhere. So, what I'm hoping for, Voss, is for you to clearly

explain what this means and just like all the concepts to it and just break it down for me in a clear, easy to understand way. So, so I could uh so I

understand way. So, so I could uh so I could, you know, learn from it, but so others could learn from it, too.

Absolutely. And there's a lot of different definitions. Everyone has

different definitions. Everyone has their own. And I'm going to give you

their own. And I'm going to give you what I think is the clearest explanation.

Okay, let's do it.

Sweet. So, yeah, this has never been shared before. our team put together uh

shared before. our team put together uh it's how to break into FD in 30 days.

Let's start off with uh recent developments and the facts of today. The

reality is every company can now buy intelligence. You know, you have a

intelligence. You know, you have a frontier model being released every single day. Just yesterday we had Kimmy

single day. Just yesterday we had Kimmy 3 being released. Uh last week was Fable uh 5, you know, or GPT 5.6 Soul. Every

company can now buy intelligence and the reality is intelligence is becoming commoditized. So the same foundational

commoditized. So the same foundational capability is becoming available to anybody who can pay for it and that's most companies today. So you'll see here is a graphic where every company has

access to the same foundational model.

So if everyone can access it, intelligence can no longer be the mode.

I think there was this huge theory that you know people will be priced out of intelligence and that could be the case in the future but the reality is today everyone has access to the same tools.

If you go and talk to 50 different enterprise clients, they're all using the same stack. They're all using cloud codeex. They're using cursor for model

codeex. They're using cursor for model agnosticism. Uh they're using GitHub

agnosticism. Uh they're using GitHub copilot. It's all the same thing. So the

copilot. It's all the same thing. So the

reality is everyone has the same capability in terms of what intelligence tap they have access to.

So where does the advantage go? It goes

into deployment. So the edge is no longer who has the intelligence. It's

where, how, and why they use it. And

that is the role of an AI for deployed engineer. It's allowing companies to

engineer. It's allowing companies to harness and take advantage of AI intelligence or software previously to make sure that they apply it the best to

their specific company context. Every

single company is different in terms of how their business is structured, what processes they have, how things run, etc. And the job of an FD is to make sure that the intelligence which is

general is specifically applied to this company in a way that benefits them the most. And the advantage will start to

most. And the advantage will start to become who has that best bridge that connection between their own processes and the intelligence stack that they have access to.

And Voss this was a term that correct me if I'm wrong was popularized by the Palunteer team. Right.

Palunteer team. Right.

That's right. So can you know can you tell me about like how Palunteer I think how Palunteer works with FDES? I mean I feel like for a lot of people Palunteer is this black box. Can you can you go a

little more into that?

Yeah it's funny. So I lived in New York for a few years and I had a ton of buddies who are Palunteer for deployed engineers. So I have a little bit of

engineers. So I have a little bit of insight into this.

Um without sharing what I what I think is proprietary. Palunteer has an

is proprietary. Palunteer has an ontology. They have a software stack

ontology. They have a software stack that they work off of and this is full of connectors to software but also data links which allow people to which allow enterprises to pipe their data into a

unified interface and what Palanteer FDES then do is they'll actually be deployed on site. So this is either like enterprise customers or the military the government um and learn their workflows

and then spin up kind of workflows dashboards you know agents that will solve the problem for these companies.

So Palunteer really popularized the idea of essentially which was what was consulting but for the software space they coined the term for a deployment engineer and it really started to allow their business to take off. They had a

centralized platform but its beauty was not in like how tech forward it was. It

was it was in how customizable it was and then these forward deployment engineers would go on site customize it per client and it would really solve their pain points better than a generalized servers.

Cool. And I guess like the the thesis is that if it works for Palunteer, it can work for everyone else, right?

That is sort of the thesis. Yeah. And

the Palunteer I think really solved it when it came to the data age where you wanted to unify your data sources and then visualize it in more unique ways. I

think the AI age is going to demand that 100 times more where every single company is going to need customized agents. And uh that's actually what

agents. And uh that's actually what we're here to solve as well with our with our OS. But everyone is sort of coming to this same realization that forward deployed engineers are a massive reason why AI is going to be powerful

for businesses.

Cool. Let's keep going.

Sweet.

So someone has to decide where intelligence belongs not just where it's applied. And that person is a forward

applied. And that person is a forward deployed engineer as well. So there's

kind of three stages to a forward deployed engineer's involvement at a company. The first is understanding the

company. The first is understanding the business reality. So it's how the work

business reality. So it's how the work actually happens today. And I think this is the part that gets glossed over by people who are you know very deeply in tech. You know in Silicon Valley we have

tech. You know in Silicon Valley we have a mindset where like okay well the beauties in the software like it doesn't really matter what the business processes are. But I can tell you

processes are. But I can tell you firsthand every business is so different even in terms of like the same process across multiple businesses. Let's go

like accounts payable for example or or sales for example at two different companies. The way that they do that is

companies. The way that they do that is so different. One has a 10-step process,

so different. One has a 10-step process, one has a 30-step process. One is using Salesforce Gong uh and uh Chili Piper.

The other one is using HubSpot and uh Apollo and Clay. There's the software differences, but there's also the process differences, and there's the things that matter most to the business

being very different. Um when things go wrong, like exception handling, the way that that's being done at a company needs to be documented as well. So Ford

deployed engineers go on site, they'll either interview people or just observe them work or get, you know, access to their systems like their ERPs, their CRM to figure this stuff out. And this is

where the bulk of the time goes in my opinion. Um, it cannot be understated

opinion. Um, it cannot be understated how important this is both in terms of understanding the business, but also to bring that business along the journey.

And this is where communication and analytical ability is incredibly important for a forwardlook engineer.

Um, the way I like to think of an FTE is the best combination of someone very deeply technical who can understand it, but also someone with fantastic communication ability and the ability to

get the information out of people that they need to. Um, and this is all-encompassing for business reality.

And you know, you say the FDE goes on site. When you say onsite, is that like

site. When you say onsite, is that like literally like walks in the office and starts like speaking to people and stuff like that or do you mean like you know it can it can certainly be done remotely?

Yeah.

Um and you know there's there's certain times where that's you know required because a company itself is remote or like the people at the function are not all in one office but I will say a large majority of the time it is on site. I

know that Palunteer does this very heavily. uh we do this as well and it's

heavily. uh we do this as well and it's not just because you know you can't get the information remotely but it's it's it's actually more so because the relationship that you build with the

person like you know you're on site you're part of the team you'll uncover far more information that way right like if somebody will simp if you schedule a 1-hour meeting for example somebody will

walk you through what they think is the job but if you're on site with them for the full 8 10 hours whatever it is you're actually experiencing the job like when something goes wrong that's

not really documented in an SOP or like a in a word doc or a Google doc somewhere you're going to see that play out. Um you know even consultants of

out. Um you know even consultants of McKenzie they do this they'll go on site to a mine and they'll sit with the miners and they'll watch them do their work uh because it's so much more powerful to establish that relationship

and get the information that way.

Cool.

Yeah. The second step is FD judgment which is where does intelligence belong and where does it not? I think when the AI wave first started, we saw a lot of, you know, let's just slap AI everywhere.

Let's let's give everything to the model and let's let's let it figure it out.

Um, and this is what led to token maxing and hallucinations. Then you have the

and hallucinations. Then you have the the MIT stat that 95% of generative AI pilots fail. Um again now the industry

pilots fail. Um again now the industry is sort of shifting gears and they're realizing that okay we actually need to be very selective about where we apply this intelligence and we also need to be

selective about how we design the new stack for this intelligence to play out.

So again for example you have a 10step workflow it might be that you know that workflow should not be changed by AI.

You know maybe it's too risky or maybe it's not high enough ROI. Maybe it's

already pretty automated. Why do we need to do bring AI into it? Um, it also could be that of those 10 steps, only three of them actually need judgment,

right? So, categorization of this, you

right? So, categorization of this, you know, lead in a CRM tool, that might be a little bit more non-deterministic. So,

we're going to bring in an LLM there for for judgment. But the rest can be solved

for judgment. But the rest can be solved with with, you know, if then else statements. It can be solved with API

statements. It can be solved with API calls. And this sort of judgment is

calls. And this sort of judgment is actually, you know, far more complex than I'm making it out to be even. But

it belongs with the FDE. So the FDE both has again the business reality which is the consulting style like communication style approach, but also the technical judgment so that they can determine

based on their technical background.

Okay, I think that this design is going to be, you know, risky. We're going to have 80% accuracy. It's not worth it.

Versus this other, you know, workflow will will have a much higher ROI. will

be able to build it much faster. It's

lower risk, etc. And that sort of back and forth judgment is where an FD really shines. It's bridging the gap between

shines. It's bridging the gap between the business and the technology.

If you, you know, just curious like if if someone listening to this like wanted to become a foreign deployed engineer in New York City that's doing this sort of stuff, like how much money could they

make?

A lot of money. You have uh no idea how expensive it's gotten. Both from a we're hiring perspective, but also in terms of what the market's demanding. This is the hottest role in technology right now. I

mean, you could make anywhere from 150,000 base with considerable equity to I've seen roles go up as high as a million dollars a year. And I'm not joking. Um, these are extremely well

joking. Um, these are extremely well compensated roles if you are the best combination of consulting and technology.

Cool.

Um, deployed AI system. Finally, the

last step is actually going out and building the software itself. This is

where it varies wildly company by company. For example, at Palunteer even

company. For example, at Palunteer even they have some FTEEs that you know you're not actually writing code. You're

mostly like spinning up workflows like text. You're chatting with the software

text. You're chatting with the software of the Palunteer ontology to create like some dashboards and to the extent you're writing code it's SQL. But there's other companies where you are fully writing

like production code either on-site with a client or you'll go back and and do this but it varies wildly. So there are some FTE roles where you're going to be writing production code. You need to have a background in software

engineering and like really be confident in your ability there. There's other FTE roles where it's far more technically light and you can you know chat to build um on top of an existing platform. So

this is the part that varies wildly. But

either way, you need to have a very good understanding of the software because when the client has an issue with, hey, this doesn't work right or we have an issue in production, it's basically your ass on the line and you have to know who

to call, what to do, and how to fix it.

Totally.

In summary, FTEEs are in demand because they control how intelligence enters the business, how it's used, and that is where all the value is today in the AI age. Everyone is coming to this

age. Everyone is coming to this consensus, and that's why FDS are extremely valuable. Well, it's also in

extremely valuable. Well, it's also in demand because it's new as well. Like

this, like there wasn't intell super intelligence on tap five years ago. So,

not only is the idea of super intelligence on tap just absolutely absurd. Many trillion dollars of, you

absurd. Many trillion dollars of, you know, money is going to be changing hands over the next few years. But the

idea that now you need a person to actually like people are realizing hey you actually need judgment and hey you actually need to like you know be a systems thinker and hey like actually

token maxing isn't the best strategy.

Like there was like a moment in time where token maxing like people were people basically were agreeing that token maxing was the strategy. It was

just like hey these models are so good let's just let them do their thing. That

was like the thinking.

Yeah, that was a a funny period in time.

I would argue we're still not fully out of that. But yeah, um yeah, you're

of that. But yeah, um yeah, you're totally right. I mean, this is a brave

totally right. I mean, this is a brave new world for everyone involved. And

I mean, I I have horror stories of of people of of like seuite executives I've talked to who have blown through their entire $10 million claw budget in like 3 months. It was supposed to last them a

months. It was supposed to last them a year, but because they gave it to everybody, it's token maxing and everyone's spinning up whatever they need. And and and the sad reality is it

need. And and and the sad reality is it didn't really move the needle for the business either. And it's because that

business either. And it's because that business didn't really invest heavily into forward engineering. And so I think you're totally right.

Cool. Let's keep going.

Sweet. So we we've already alluded to this, you know, pretty heavily prior, but I want to really make sure I hammer down this point, which is that there's two uh sort of streams of of kinds of

judgment that are required, and it's very rare in a in a single person. So I

think the the unfortunate reality is and this is what's going to happen. It's

already starting to happen is as you know we go from the token maxing let's go all in on token maxing go to now the same thing on FD let's go go go let's hire a bunch of fes I want to be very

clear about what the role really demands. I think there's a lot of fees

demands. I think there's a lot of fees who are you know unfortunately neither the best communicators and neither the best software engineers. I would

strongly urge them to strengthen both of those skills. It's both the

those skills. It's both the understanding of workflows, cost, incentives risk adoption business value, um, you know, the politics of the internals of a company. These are all

things that you have to manage and consultants here are incredibly strong, right? You'll talk to McKenzie

right? You'll talk to McKenzie engagement managers, BCG, Bane engagement managers, they'll be very good at this side. Um, the other side is what they might need some more support in. And same thing software engineers

in. And same thing software engineers will be very good at the right side with models systems APIs data code reliability uh eval guardrails you know more AI centered terms harnesses post

training fine-tuning these are things that are more on the technical side of the aisle and software is usually very good at this but they need to also then kind of drift towards the business side um by understanding the left side of the

aisle and FTE is the best combination of both of these it's not an average combination it's not the worst combination of both where you know you're not the best communicator but you also can't code. It is truly the best of

both and that is the milliondoll hire where the FD can turn business understanding into working software end to end. They can do both sides

to end. They can do both sides perfectly.

Yeah. I mean put another way it's like if you understand art and you understand science and you could speak both you have what it takes to become a the

million-dollar FD. The the hard part is

million-dollar FD. The the hard part is like usually the people that are good at good at science are sort of good at science and the people that are good at

art are kind of good at art. Um but

there you know there is some overlap in the ven diagram.

Absolutely. And that's why it's such a rare role. But I also firmly believe and

rare role. But I also firmly believe and that's kind of the whole point of this presentation is that you can become this. It's it's not out of the realm of

this. It's it's not out of the realm of possibility to become much better at both of these things. It just you need it cleanly laid out. you need a road map and that's what I hope that I can provide by the end of this call.

Cool. All right. All right, boss.

Let's go. [laughter]

So, first you know for example, it's understand how the work is really done.

Um because the documented process is very rarely the the real process, right? So,

an email might arrive. Now, this is extremely simple, but the reality is it sounds like a clean trigger, but it's it's far more complicated than that. It

arrives from 40 plus senders. No two of them are formatted alike. the data is different. Some of it's in a PDF, some

different. Some of it's in a PDF, some of it's in a screenshot, some of it's in an Excel spreadsheet, or it's buried in a forwarded thread. It's it's far more complex than it makes out to be. So, if

you didn't look into this, if you weren't an FD and you just asked the person for, hey, what's the first step of the workflow? They'll tell you an email arrives. And all of a sudden,

email arrives. And all of a sudden, you're building for a system that doesn't map to reality versus the reality, which is that it's so complicated. And half of them are

complicated. And half of them are exceptions. It's the same as last time.

exceptions. It's the same as last time.

It ignores the second attachment. Sarah

already signed off on this one. There's

no consistent subject line, so you can't route without actually going into it.

And usually the reality of how to play this is in one person's head. So one

person will know like, okay, yeah, well, when I see this email from this person, they'll send it to this this vendor or to this part of our procurement team.

But that's not written down. And if you don't sit with that person, kind of coax this all out of them, they're not going to remember to even tell you. um you

would think about like at your job today I asked people viewing this how easy is it for you to really write down every single exception that [laughter] might h happen in your job I was a software

engineer at meta and if people ask me for my job I'd say well yeah I code all day I'll get a task and I'll I'll work on it but that's not the reality right the reality is I have meetings you know this happens something breaks in prod I have to go fix it that's what we're

getting at here the second step is oh it's copied into a spreadsheet same thing here you get the idea one's a real one two are stale data validation. It's

rekey by hand, columns drift. Same thing

with checking into an internal system. I

won't get into all this. You get the idea. Every step is extremely

idea. Every step is extremely complicated. Um, so that's what

complicated. Um, so that's what understanding the work really means. It

takes time and it takes effort to sit with a person responsible and sometimes multiple people responsible.

Usually, usually it's multiple people.

Very, very often. Yeah. I mean, if this company has like 5,000 10,000 people working in it, chances are you have a lot of people working on the same thing.

Then you decide how the work should operate when intelligence is built in.

So again, where does the terministic software live in? Where does the agent act? Where does the human approved?

act? Where does the human approved?

Where does the record get updated? I

think the best combination of sorry, the best solution of AI for most companies is a very good combination of deterministic software. Probably the

deterministic software. Probably the majority of it is that, but then obviously the the judgment that API calls to LM can provide. And then

finally, human loop.

So this is just a fancy little little digest, but it's really the agent that can then be deployed into existing systems. It's doing the first half, which is intake, validation, agent drafting, and then you have a human in

the loop for approval. This is something that I strongly recommend my FDES to push for in a in a in an agent implementation. Once you hit approve,

implementation. Once you hit approve, it'll then go through the latter half of the steps, and that's what you're building. So the job of an FTE when

building. So the job of an FTE when you're building has three parts. It's

obviously auditing, then creating evaluation suites to make sure the system behaves correctly. This is

extremely important in the AI age. And

then finally, deployment, which is both handholding the client to make sure they're adopting it, it's working well for them, but then also the software side, making sure that nothing breaks.

You're monitoring all the metrics that matter. You're monitoring KPIs, SLAs's,

matter. You're monitoring KPIs, SLAs's, and everything needs to be topnotch for somebody to really trust you as an FD.

And every stage is a prerequisite for the next. So for an eval so prove the

the next. So for an eval so prove the system behaves correctly. So in a scenario where uh the outcome you know

is non-deterministic meaning like it's tough to say what success looks like how do you create an eval or can you create

an eval for more creative task or tasks that are hard to like understand if it's successful or not.

Yeah. for for obviously for you know more non-deterministic tasks it's much harder right it's much easier to say okay was this email categorized correctly because we have 10,000 previous emails to go off of and it'll

be the basis for our email set but even for tasks where it is non-deterministic like for example creating a presentation right there's a million different ways to do it and

sort of the beauty is in the eye of the beholder where you know one thing looks good to me might look bad to you um here it's very helpful to have as much previous data as possible. Obviously, if

you have 5,000 previous presentations to go off of, it makes it a lot easier to create this golden data set of what we think matters. You know, you can say,

think matters. You know, you can say, you know, always put the logo in the top left, always have larger font of this styling, etc., etc. But this is obviously where you'll never get to a

perfect result with just evals. need

human in the loop feedback to make sure that going forward you at least have a feedback mechanism that improves your harness if not like post- train uh or fine-tunes the model that you're working

under. So on one hand like get as much

under. So on one hand like get as much data as you can and kind of determine like what looks good, what looks bad, identify like what matters to you, but also then always bake in human loop

feedback because even with a good data set, even with good evals, you'll need to have them constantly improved. And

that's where that feedback mechanism comes into play.

And I've noticed like in this entire presentation, this entire podcast, we haven't really spoken about which LLM to use. Are you like basically agnostic in

use. Are you like basically agnostic in terms of like working with Anthropic or OpenAI or Google or like if you're an FDE basically, how do you think about

which LLM to work with?

Yeah, great question actually. Um,

something I probably should have touched on. Uh, we as a company are extremely

on. Uh, we as a company are extremely model agnostic. So we think our value

model agnostic. So we think our value lies in our ability to be switching from one model to the next and making sure that your accuracy only improves, your cost only goes down and you're not marrying to one intelligence provider,

which we think will be an asset going forward. Um, you don't want to

forward. Um, you don't want to monopolize your intelligence, your inference layer. That being said, if I

inference layer. That being said, if I was an FTE today or if I was trying to become the best FTE today, I would stick to one model and one agent building platform. OpenAI has one, Cloud has one,

platform. OpenAI has one, Cloud has one, agent SDK, etc. every single model provider has one. Get very very good at one of them because that will be the foundation that you then you know okay

let's let's try out claude tomorrow if I'm already good at open AI's you know Asian building platform okay I feel more confident about that let's go to Kimmy 3 or GLM 5.2 too. Let's see what the open source models can do. Let's build a

proprietary harness. That's how I would

proprietary harness. That's how I would go about it. Um, but I wouldn't really worry about being model agnostic when you're starting out as an FD because that's again not where your value lies.

Your value lies in how good are you at understanding both sides of the aisle because that can then apply to any model.

Totally. And it's we're getting to a point where the models are very similar in a lot of ways.

Yeah. And a lot of the big players are, you know, they have like Google will have their frontier model, but they'll also have an open source model, for example. And so now you're getting to

example. And so now you're getting to this place where it's like, okay, you can play with their open source model, you can play with their um, you know, frontier model. And and so I I expect

frontier model. And and so I I expect that the arrow of progress around LLMs is they're going to have a bunch of different products for you to play with.

So yeah, I agree. Like if you want, you know, pick pick an ecosystem, bet on an ecosystem that you believe in for whatever reason, be the best at that

ecosystem. And then as you become the

ecosystem. And then as you become the best, then it's like, okay, if you want and you're working with a client, for example, and for whatever reason,

another model makes more sense, then great. You know, you

great. You know, you Yeah, you can go and recommend that.

Absolutely.

Yeah, I would say that that's exactly right.

And then just to really hammer the last point in, your ability to determine what model is best for a task relies on your understanding of various different models. You're benchmarking them along

models. You're benchmarking them along the way. But to your point, don't put

the way. But to your point, don't put the card ahead of the horse. Like really

get good at one before you then try to venture out and make that understanding.

I would totally agree.

Yeah. I mean, that's like Yeah. You don't want to like hammer a

Yeah. You don't want to like hammer a specific, you know, model and you don't understand what the system and the set of tasks are. It's like

are. It's like the equivalent of, you know, you're a waiter and you just hand someone a glass of pino noir and they're like, I didn't

ask for that. You know, you you know, a good restaurant has a sleier and and and sier's job is to understand what is your pallet? Um, do you like dry wines? Do

pallet? Um, do you like dry wines? Do

you like wines from, you know, uh, France, you know, southern France or northern France or, you know, Yeah.

I'm not the biggest wine guy, so I, you [laughter] know, the analogy might break down, but that idea, I think, of just like understanding what people want first and then deploy makes a lot of

sense.

Yeah. I mean, honestly, I thought that was pretty good. Like, familiarity of of agents is an FTE. Like you really go in, figure out what they want and then you give it to them, right? You might give everybody pen noir. It might work for some of them, but it's not going to work

for most. And that's why again most AI

for most. And that's why again most AI pilots fail 100%.

Cool. All right, let's let's keep going.

Sweet. This is again more of the same.

Find the workflow worth rebuilding. I'm

going to link this I'll have Greg link this this document, you know, in the in the in the channel. So, if you guys want to dive in deeper in here, but the idea is again the same. collect the context, trace the FD findings, figure out the

bottlenecks, the repetitive work, the judgment points, all that stuff, and then produce the operating map. And this

back and forth is why having the understanding of the business and the tech is super important.

Cool. By the way, if you go back to the audit, like if you want to be an FDE, um you know, we just had an episode with um

Corey Ganim who he came on the podcast and he basically he was he talked about selling audits as a way to to learn about someone's business and then deploy

AI afterwards. Like you can sell the

AI afterwards. Like you can sell the audit, right? like

audit, right? like in charge for the audit and then the implementation you can charge like a monthly fee or you can charge a onetime fee. How should people think about that?

fee. How should people think about that?

Yeah. So actually we're in this exact business of you know implementing AI across the largest companies on the planet and we require every single engagement to start with an audit which

you know obviously costs money to the business. Um this is extremely valuable.

business. Um this is extremely valuable.

I think again like there's a lot of misunderstanding like you know you can just throw AI on the on the on the on the uh the company. The audit is worth so much money to a business. I mean

we've had companies that tell us like the audit was worth 10 times what they paid for. It's it's better than McKenzie

paid for. It's it's better than McKenzie because it's so telling. AI is so new like you said no one really understands how to go about an audit. But if you're able to say like look here is in your

department here are all the different workflows and we've mapped them out very cleanly right we have the full steps the back and forth the exception handling we're going to map that out for you and we're also going to tell you what we

think is worth automating versus what isn't give them that priority map give them that matrix that ROI matrix and then go ahead and show them how you would build it and show them the ROI

show them the use case that is worth so much money to a company I I think this is something that even most consulting firms are not able to figure out and this is where you have an edge if you are really up to date on AI and you

actually know live and breathe it yourself. Um the audit is worth a ton of

yourself. Um the audit is worth a ton of money to a business.

Totally. It's also a chance for you to like build trust with them.

Yeah.

You know what I mean? and and and show them how you work and underpromise and overd deliver and and and it gets their creative juices flowing around like okay

I didn't realize that you know cuz you're producing like an operating map right so you didn't realize like oh hey like you know I never thought about that use case or I didn't think that you know

this would produce this expected business value um maybe it's worth investing in absolutely it's funny you When we first started, you know, the company, it was

last year. This is before FDUs were

last year. This is before FDUs were really a big thing. Um, we used to call the audit the medicine that neither one of us wants to take, right? Like a lot of companies are like, "Oh, like, do I

have to do an audit? Can't you just like token max and start building?" But it really is so valuable and they realize that as the audit goes on. So, I totally agree.

Yeah. We um cuz we we we also have an agency called LCA. Um and

LCA is well known for building like working with the biggest companies on the planet and then taking their products from a product perspective and bringing them into the AI age. So like

from a you work with like a Dropbox and what is an AI first version of Dropbox or Slack and AI first version of Slack look like? And we started doing audits

look like? And we started doing audits as like, hey, let's audit your product first. What we noticed was the word

first. What we noticed was the word audit was a tough pill for people to swallow.

And we just rebranded audit as a sprint.

So it would be like it was a design sprint. We just kind of like and we

sprint. We just kind of like and we like, you know, brought in the the the concept of an audit um with it. Um so we noticed that that that worked better.

So, um just just a little uh tip for folks.

Super helpful. Yeah. [laughter]

For some reason, people have an allergic reaction to the word audit. Um

well, I mean, they think of they think of like a tax audit.

Yeah. Yeah. Fair enough. The AI audit doesn't have the same ring to it for sure. [snorts]

sure. [snorts] Yeah. Cool. All right.

Yeah. Cool. All right.

Let's keep going. Again, deterministic

software versus an agent versus a human in control. I'm not going to beat a dead

in control. I'm not going to beat a dead horse. You got to prioritize the high

horse. You got to prioritize the high volume workflows where the improvement is large enough to matter. That's your

job as an FD is to figure that out firsthand.

Um, eval turn non-determinism into evidence. You got to make sure that you

evidence. You got to make sure that you have the right data, the required steps, it matches the expert, um, and it's safe to act on and you got to make this kind of matrix and wherever you feel like it's not safe to act on, you know, you

route it to a human and you create an evaluation report, right? So you have 50 runs and 41 of them passed. Of the

nine that didn't, let's investigate why.

You know, five of them had missing data, four of them had the wrong record pulled. And then you use that to improve

pulled. And then you use that to improve the system. Greg, you touched on this

the system. Greg, you touched on this earlier, like how do you set up evals?

This is sort of the the framework that I would use.

This is cool. By the way, yeah, just it it helps to just have like a a decision tree, a matrix when you're thinking about things. again like

because everything is so new you could go a million different ways but you know I'm sure there's other ways to do this but this is ours and this is just how we kind of think about things at a high level it depends on a case by case basis

for sure so how do you make it deploy and work inside the business this is again phase three the first one is the audit the second one was eval third is deployment one we really preach about like

integrating with what already exists um I think a lot of AI folks are forcing migrations to like new software and And the reality is, and this is a tip that I give to all my FDE, you have an edge if

you're able to build on top of their systems. So, you know, one of our clients, for example, said that they spent, you know, a couple of years, a couple million dollars moving to Netswuite, which is an ERP software. And

if your AI solution is like, hey, we have to make you move off of Netswuite, they're going to tell you to get lost.

But instead if you're saying which is what we do build on top of Netswuite make it much better and integrate that Netswuite with your Salesforce with your SAP with your Concur Expensify Gong uh

you know every other piece of software workday that is a much more powerful system and that is where all the value lies than if you can test it in a controlled

environment and really scale up from you know deployment to shadow mode to increasing autonomy. to then being

increasing autonomy. to then being deployed in production. That is going to be your edge as well where you're not forcing a massive shift. You're kind of walking them through that journey. And

to our point earlier of why you meet them in person, it's because it's a lot easier to kind of guide them along that journey if you've met them face to face versus if you're just a guy behind a computer screen saying, "Hey, now we're going to flip a switch and AI is going

to run your business." That's a a much different much more uh polarizing approach.

I mean, it makes sense, right? like

you're you did the audit and then if you're going to pitch to them, hey, you've been working with this software stack for the last 20 years. All of a sudden, go switch to this thing and it's

going to cost you a bunch of money and there's just like so many unknowns. Like

that is a tough pitch to sell. You know

what I mean? And like you want to if you're pitching anything, you want to pitch something that feels like you're fishing with dynamite. So, it's like how can you, you know, how can you fish with

dynamite? You just say like, "Hey,

dynamite? You just say like, "Hey, you're you have this system and this stack. You're it's it's worked for you.

stack. You're it's it's worked for you.

Um, I'm going to make it better and it's going to help you all be more efficient.

It's it's going to help you uh reach customers faster. It's going to drive

customers faster. It's going to drive value for customers. It could increase revenue." Like when you start saying

revenue." Like when you start saying things like that, it's like, okay, no-brainer, no brainer, no brainer.

Also, you have to keep in mind that you're pitching to people at a company.

And people at a company, I'll say the thing that people don't say, which is they don't want to get fired, right? Like they want to get promoted

right? Like they want to get promoted actually. So your job is to help them

actually. So your job is to help them get promoted. How do you help them get

get promoted. How do you help them get promoted is probably not by moving from one ERP to another ERP that may be marginally better. You help them get

marginally better. You help them get promoted by driving value cost effectively. If you can drive value

cost effectively. If you can drive value cost effectively, everyone's high five high-fiving, right? Cuz when performance

high-fiving, right? Cuz when performance reviews comes around, you know, you the the employee, the executive can point to I worked on this project. Yes, I worked

with an outside agency like LCA or Veric Agents or individual FDE um freelancer. Um but as long as you

um freelancer. Um but as long as you help them do that, that's what's going to help them get promoted.

Yeah, totally. And and and just like to again like double down on that, they view you as a risk, right? They can just sit by and let things stay the same and

status quo. They'll be fine. But if they

status quo. They'll be fine. But if they instead bring in an FTE who's going to change stuff up and maybe it fails, like they're worried if this fails, it's a terrible look on me. Forget like

migrating to another ERP. Even just you being involved at all is a risk to them.

So you have to de-risk this as much as possible for them if you really want to sell yourself into a company. And what I strongly recommend is like do the audit for free. like get your foot in the

for free. like get your foot in the door, prove value there, come up with a plan, and then only get paid when you really prove measurable value. That is

what I strongly because that derisks the whole thing. Your first few customers,

whole thing. Your first few customers, if you're really starting this out, will teach you so much. They are genuinely worth more to you than you are to them.

But after you have one, two, three of those, then you can start charging for this because you're going to be leagues and miles ahead of everyone else in the space. I promise you it's still so

space. I promise you it's still so early. I know this from our company as

early. I know this from our company as well. There is so much demand for people

well. There is so much demand for people who really know how to do this. And

quite frankly, there aren't that many people who know how to do this. Get

started, get your feet wet, um, and really prove that you know what you're doing. And that's d-risking the whole

doing. And that's d-risking the whole thing for them.

Totally. And it's just going to give you the confidence too, you know.

Yeah.

Right. Which is important.

Yeah. And you'll know what matters to them when you're selling. like you can touch on different aspects that speak better to this person in the function.

Um it's all really valuable. If I had to put one page cemented burned in everyone's brain, it's this one which is you go from audit to eval to deployment.

There's some steps in the way you build, you observe and you improve and the loop runs again because once you improve one system, the next one becomes extremely clear. There's always interconnected

clear. There's always interconnected bottlenecks where one workflow is impacted by something upstream and it frees up something downstream. And this

is why AI is so pervasive in an organization. It's because once you have

organization. It's because once you have it in one place, you're going to need it everywhere else so that you're not just, you know, 10xing one workflow, 10xing another, you're 100xing the entire

business as a whole. Um, and that's your job as an FTE is to go from audit to eval to deployment over and over again.

And the next stage that I'm going to show is the 30-day plan of how you can get there from zero to one. If if I was starting from scratch, how would I go about it?

And you did this, by the way. You you

started from scratch, right? I didn't

know this, but you were you're an engineer at Meta, right? And then you sort of learned how to do this. So,

you're speaking from experience.

Yeah, absolutely. I was an engineer at Meta for um uh a few years working on a different a couple different products, but I was never a consultant. I never

really understood what mattered to businesses as deeply as I do now. And we

got started again just by by doing it.

Um and we we we had this thesis that like AI needs to be applied and it allows us to get ahead of the curve. Um

but you never learn by, you know, reading and and you only learn by doing it. So the goal from this is is to do like if I could condense what I

did over a year and really had the biggest learnings, the biggest wins in just 30 days. This is what I would do.

So the first step is build an agent that can complete a real loop, right? Build

an agent that's actually useful as a workflow. So like ask Chad PT what is

workflow. So like ask Chad PT what is one real enterprise workflow in a function of the back office, right? It

could be finance, it could be HR, it could be procurement, logistics, it could even be for it, it could be sales, anything. Get the workflow in as

anything. Get the workflow in as granular of a detail as possible and build an agent for it. Um, even today, it's very hard to build agents, right?

We think that it's a solved science.

It's not. There's a thousand different ways to do this. Everyone has different definitions of an agent. My definition

is this. If I give you a task, can you solve it in as much detail and as high enough accuracy as possible. It's different than me

possible. It's different than me prompting Claude to go do it. It's far

more in the background and it has much more of a repetitive motion where I'm not relying on somebody prompting perfectly to make it happen. I can

prompt like an idiot and it will still happen. That's my kind of requirement

happen. That's my kind of requirement for you when you're solving for this.

There's a bunch of different, you know, aspects to this, but if you have 7 days to work on this, you'll be able to pick it up. agent looping, then tool usage,

it up. agent looping, then tool usage, then guard rails, then context and memory, then the audit trail, which is incredibly important. I want to hammer

incredibly important. I want to hammer down here a little bit. If you can't show the client what the agent is doing, they will never trust you. There's a big fear in AI agents today that okay, it's going to go off and do something. It's

horrible. There's a lot of fear-mongering as well. I won't say from who, but everyone knows who I'm talking about. [snorts] Um, you need to show

about. [snorts] Um, you need to show that the agent traces are logged everything. And this is a software

everything. And this is a software engineering problem. So, if you can do

engineering problem. So, if you can do that, you're a step ahead. And again,

there's a full day for each of these things. Um, to clarify, if you're, you

things. Um, to clarify, if you're, you know, working 12 hours a day, I don't expect you to be able to pick all this stuff up perfectly on each every single day, but this is what I mean by like a 30-day plan. You can space it out as you

30-day plan. You can space it out as you need. It's not like you have to get it

need. It's not like you have to get it done in 30 days. Then a real workflow, then a checkpoint. The checkpoint, the last day is you have a working agent with tools, guardrails, deliberate memory, and a full audit trail for one

task. You might not even understand the

task. You might not even understand the task the best, but this is just to get you well-versed in building agents.

Fair.

The second week is turning that demo into a system that can recover. Again,

very heavily on the engineering side.

So, a defined JSON schema not free form text. You're validating schema. You have

text. You're validating schema. You have

failure modes and again I want to call it failure modes. exception handling and we also see in 13th days failure handling is also extremely extremely

important. This is where going in deep

important. This is where going in deep into a client matters because if you understand hey when something goes wrong how does it go wrong and let me build

the agent around that is extremely important. It's far more effective than

important. It's far more effective than you're building an agent that solves for just the happy path. It's called the happy path. If you're building for the

happy path. If you're building for the unhappy paths, the 1,500 different ways it can go wrong, your agent is worth a million times more. The way I say it is this. There's only one way that

this. There's only one way that something can go right, but there's a thousand different ways something can go wrong. So, if you're only building for

wrong. So, if you're only building for the way it goes right, you're worth nothing. If you're solving for all the

nothing. If you're solving for all the exceptions, that's where you are worth something as an agent.

That's week two.

Then week three is where you start to make it measurable and economically viable. Right? So you'll have the retry

viable. Right? So you'll have the retry logic. Yes, this is more engineering.

logic. Yes, this is more engineering.

You'll have the golden data set for evals. You'll make sure that it improves

evals. You'll make sure that it improves over time. But you'll also start

over time. But you'll also start understanding, okay, this is what we talked about earlier, Greg. Looking at

cheaper models for subtasks, looking at, you know, less soda, less Frontier models. Can we get this job done with a

models. Can we get this job done with a Gemini Flash or can we get it the job done with a a Muse Spark or probably not a Llama 4, but you know, there's other models that can that can be a good fit

there. Um, this is where you start to do

there. Um, this is where you start to do more or more of this test, which is we have an agent now. Now, let's try to optimize it. Let's try to measure, okay,

optimize it. Let's try to measure, okay, how much is it really moving the needle?

If I deploy this in production, how much time am I saving? How much risk am I mitigating? How much revenue uplift do I

mitigating? How much revenue uplift do I have? There's only three buckets of me

have? There's only three buckets of me measurement that matter for a business.

It's those three, revenue uplift, risk mitigation, and cost savings. So, you

need to measure your agent across all three of those buckets. And at this checkpoint, you have, you know, an evaluated agent with known failure modes, measured costs, and a golden data

set. And the final week is defend the

set. And the final week is defend the system like an FTE, which is all of the business around it. It's the pain points, it's why AI belongs, it's the architecture behind it, it's the

iterations. is you know when you first

iterations. is you know when you first built the agent it got this wrong but then it improved over time accuracy went from you know 70% to 95%. and the

economics around it. So how much time did you save the error reduce again risk revenue costs? We talked about this and

revenue costs? We talked about this and you rehearsse this as an engineer. So

what was the architecture? What were the decisions that you made? And then you also rehears this as a VP. Like what was the problem that you solved? What was

the outcome? What was the evidence? What

was the risk? And this week is where you're going to know, you know, was the system that you built worth the salt?

Was it worth the investment? How much

could you charge for this when you build it for a customer? That's what this week is for. And I strongly recommend that

is for. And I strongly recommend that during this week, you pitch your agent to businesses because they will tell you like, you know, did I get you you'll pitch them, did I get this right? Did I

get the economics right? Am I thinking about this the right way? And they'll

tell you point blank, no. Or I want it built in this way. and you'll start to see like okay now for my next FD engagement starting out with an audit when you're actually embedded with a

customer you'll learn much more about that obviously this is 30 days is you're not embedded with a customer because you can't because you have to become an FTE first that's when you can finally start to pitch yourself and be involved in a

company so if I had to zoom out this would be the 30 days it's doing the job before you have the title so on day 30 you understand forward deploy engineering but you also have evidence that you can

do it and if you pitch this to a company, they'll be much more likely to give you a shot. And that's my goal.

Foss, [snorts] this is ex this is perfect. Like, this is exactly what I

perfect. Like, this is exactly what I would recommend, too. What would be so cool is if is if you actually taught people how to

do this, right? Um, and like spent 30 days with people to actually do this.

Um, maybe maybe we do it together. Just

an idea. Uh if people are interested, I'll just include a link uh in the pinned comment on YouTube. Um I'm just curious if people

people are interested in like cuz it might it might feel overwhelming for people to do this on their own. I mean, I still think you could do it on your own, by the way, but I wonder and and by the way, I'm not

promising anything. I'm just curious,

promising anything. I'm just curious, are people into into some sort of program for this?

They don't teach you this at school.

They don't. They should. Uh I'm sure we'll have university courses on FTE soon. But yeah, people want to

soon. But yeah, people want to [laughter] like it just takes so long. I

remember I was in computer science school in 2008.

No, 2009 um and at university. And I remember the app store had just come out.

It was so clear in 2009 that mobile apps was the next wave. Just like it's so clear right now that AI agents and AI is the is the is not even the next wave is

the wave. And I just remember the

the wave. And I just remember the textbooks at the time and I went to a top university textbooks at the time and the course

material was like you know building old school software and I remember going to a teacher a professor well-known guy and saying why can't we why can't you teach

us how to build an Objective C and to build for the app store and he was just like yeah it's just not in the textbook.

just not in the textbook. And that's

when I like I was like, I'm going to drop out of this. I [laughter] I'm dropping out because like I don't want to learn yesterday's stuff. I want to

learn tomorrow's stuff. Now, there's

always the argument to be made that you need foundational work. And so, like I learned a lot in university around like foundational stuff around maths and physics and stuff like that. And I

actually think that that stuff was really helpful just in like learning how to think, but like the actual tactical

stuff did not really learn.

Yeah, I I I do, you know, I want to say like this time is different like just because it's so powerful like AI is so like you said it's the wave that I'm hopeful that universities are going to

pick up sooner than later, but for some reason I feel like you're right. I don't

think it's going to happen anytime soon.

Yeah.

Well, there you have it, folks.

What what FDEES are.

Um how to become one. Uh a 30-day plan.

Uh Vos, anything else you want to share?

I think, you know, like you said, you might not find this in university, but you're absolutely going to find it on YouTube. like Greg is teaching

YouTube. like Greg is teaching everything that you need to know and Twitter as well. Those two sources are going to be where everything is released. I mean, even Mark Zuckerberg

released. I mean, even Mark Zuckerberg had to come back to to Twitter to announce, you know, the latest model for Meta. That's where everything's

Meta. That's where everything's happening. Study the game there. Um, and

happening. Study the game there. Um, and

you've got a great coach right in front of you with Greg. So hopefully that, you know, people are really taking advantage of this time where there's a significant alpha from going out, learning, doing it

yourself, being scrappy with it versus waiting for a university to come by and and and teach you this cuz that's not going to happen anytime soon.

And it's free, right? You can listen to this and apply. It's free. So it's just like why not, right? Yeah.

Um, Vos, thank you for being generous with your with your, as we say on the channel, the sauce and the tactics and just like breaking this down so clearly.

Um, I've been following you for a couple years now almost. And uh, you're a must follow. I'll include links on where you

follow. I'll include links on where you can follow Voss um, from Veric Agents in the show notes, in the description. you

know, please comment what you what you thought of this episode because I enjoyed myself with Voss. I'd like to have him back on the podcast again.

Hopefully, he he's down to come back on.

Uh, but please let us know. I read every single comment. Uh, you want to like and

single comment. Uh, you want to like and subscribe for more of this in your feed?

Voss, any last words for for the people?

Greg, you're a legend. Thank you for having me on. To the people, I believe in you. I really believe this is a

in you. I really believe this is a fundamental shift in in how work is done. And you are if you're if you're

done. And you are if you're if you're listening to Greg and you're and you're you're on this p you're watching this, you're already a step ahead. I'll be

reading every single comment, too. If

any questions you have for me, let me know. But I would say like go out and

know. But I would say like go out and get it. Go out and get the job done.

get it. Go out and get the job done.

Make make the most of of your ability to understand AI. And it's still so early,

understand AI. And it's still so early, so get ahead of it while you can. Greg,

thank you for having me on, man. You're

a legend.

Amen. All right. Catch you next time.

Kiss.