Everyone keeps saying the same thing.

AI is coming for your job.

And I'm here to tell you that's not necessarily true.

But what is true is that the people who understand how AI is actually implemented are about to outpace everyone else here in IT.

And the wild part is that most engineers have never even heard of the technology that's quietly making it all possible.

Today I'm joined by someone who's not only talking about the future of automation, he's actually building the systems that power it.

William Collins has spent over 20 years in enterprise IT startups automation, and is now helping shape how AI is actually integrated into real networks.

This isn't theory.

This is someone who's in the code, in the conversations, and in the rooms where this future is being created.

By the end of this episode, you're going to understand exactly what is coming, what skills are needed next, and why MCP might be the single most important acronym you learned this year.

Welcome to the show, Williams.

Thank you so much for taking the time to join us today.

Yeah, glad to be here.

Love the channel of all the work you're doing.

I really appreciate it.

And, you know, kind of before we kick things off, do you want to give us a little bit of background about who you are, what you're doing, and why the folks at home should should listen to this?

Awesome.

Yeah.

So just to get started.

So what I do today, so I'm, I'm the director of tech evangelism at a company called itential.

And we're really a network automation and orchestration company or just general infrastructure really.

We can automate anything and my job is essentially to help bridge that gap between, I guess what our technology can do and the real world problems that network engineers and IT leaders are trying to solve.

And I also host the Cloud Gambit podcast, which recently got adopted into the packet pushers family.

But as far as like my background, I started working in tech really in high school back before I knew, well, actually really back before I knew you could make money and a career out of it.

I kind of had a something that happened early on in life that had me sitting in a house with nothing else to do.

So I got into like building computers, and that's kind of where I started.

Yeah, I, I, I think I definitely had a similar background.

You know, I started building computers from a young age and got a really kind of passion from technology for technology there.

Didn't end up transposing until a career until later in life.

But that passion kind of really stuck with me as well though.

Yeah, same here.

And it's, it's funny because it's almost kind of like a builder, a tinker mindset, You know, you just building things is fun, making things work is fun.

And in contrast, hey, networks, we want things to work.

Like there's nothing more satisfying than, you know, building a Greenfield network or fixing a problem in brownfield.

When you have different overlays and abstractions, you get into the nuts and bolts.

You have to go through troubleshooting, maybe throw your red Cape on for a second.

It feels real good.

Networking is super fun to work in.

It is, and I think that's what drew me to networking so much.

But you know, to kick things off here, I want to hit on something that you said during our, our pre podcast chat that kind of really stuck with me.

You know, you, you said that the hype around AIA right now is so, so thick that you could cut it with a butter knife and that that is definitely the truth I feel like right now.

But what do you see in the market right now?

What's, what is real?

What is all, you know, what is fluff?

I feel like AI, like you and I were at Cisco Live and AI was just vomited over everything whether we wanted it there or not.

What do you think is going on in the space right now?

I'm just gonna go completely transparent and speak from the the heart on this one.

So historically, if you look at so you have suppliers, you have vendors, they need to sell software, they have to sell things, you know, to, you know, in order to continue to exist and be a healthy company.

But then you have enterprises, you know, down the mid market and smaller companies that are buying technology and they have to be able to adopt it.

So one thing I'm seeing out there right now is it's so hard for enterprises and just the the companies that are buying this stuff to understand what actually something is.

What are we buying?

What is the value proposition?

What does ROI look like?

How do you put that in a budget?

Is billing going to be kind of like cloud computing is like what does all this stuff look like?

And then you have companies up and coming that are just trying to sell things.

And a lot of times what you have is, you know, historically going way, way back, marketing teams a lot of times will just go out there and, you know, say anything to try to sell something.

Yeah, our product kind of does everything.

It just does all the things.

And then that causes confusion.

Well, AI has kind of taken that problem and put it on steroids because now so many teams and so many different sellers are using AI to basically say, hey, take what I do and put it in a marketing strategy to sell to like all these different verticals.

And then you just get all this stuff.

So I, I think there's just a lot of confusion out there, which is common.

Of course, it's common this early in the hive cycle.

We're still so early with AI.

And what does AI even mean?

What does it mean if you're working in infrastructure, if you're a network engineer?

Like, what does any of this stuff actually mean?

What is the value to you?

You know, how deep do you have to go down the rabbit hole for learning?

You know, what are you supposed to learn?

Where do you start?

There's just so much confusion.

But it's starting to get wrangled in because there's only so long that these wheels can spin that the real use cases end up, you know, falling through the cracks.

And that's where, you know, I think it's important to focus.

No, I'm 100%.

You know, I kind of feel like this is a repeat of what happened when cloud computing came out and you kind of touched on that a little bit.

I felt like when cloud computing was just debuting, everything was moving to the crowd.

They were throwing everything that they could at the cloud and seeing what was stuck and and then that kind of died off a little bit and the good things stayed.

You know, I'm not saying cloud was just, you know, a hype and, you know, a buzzword for a it was.

But you know, what I mean is that the things that were actually useful stayed in the cloud and then some things kind of started to fall back down to where they belong, in all honesty.

And I feel like that's the same thing.

What's going on with AI is everyone's throwing everything at AI, seeing what sticks, and the good things that are actually making our lives, that are actually doing good are the things that's going to actually be around and help move the industry forward.

I totally agree, you know, and it, it just, it always, if you think about zooming out, it's always like so important to zoom out because I was talking to someone, just a few, I'd say like two or three months ago and, and they worked for a pretty big company.

It was like 25% through rolling out SD Wan like they had just kind of gotten started.

And I was just thinking in my mind like, wow, we did that.

Like we were done, like at one company I worked at, we were done like 10 years ago.

And it.

It it's just, it's been around like so long, but you have this diffusion of innovations like how it proliferates through the market.

You have like your your game changers that are basically building these things like the Googles of the world.

Like they aren't even looking at where the puck is going.

They're, you know, constructing the building, the power, they're doing all the things.

They're engineering hardware, they're just building silicon.

They're doing all the things.

But then you have the companies that are looking at, OK, where is that puck going?

That's where we want to be all the way down to maybe some of the laggards that maybe they don't have the expertise or maybe they don't have the budget to get things done, you know, which is a challenge in and of itself.

So yeah, it takes a while for these technologies to proliferate through the market, and AI is going to be no different.

No one 100% you know when funny is, you know, when you first start talking about, you know, that company implementing SD Wan, I'm like, man, that's a that's a buzzword I haven't heard in a while.

It's just like baked into everything.

Now it's just, it is like, it's just like another part of our day, But I remember when it also came out too, It's just like, Oh, we got to do, we got to implement SD Wan.

I'm like, and you know, at the organization, I was like, I'm like, why?

It actually doesn't make sense for us, but you know, we we had to implement it just because it was the new thing.

So IA 100% agree.

And I'm just out of curiosity, really quick for those listening, you know, what advice do you have for like people that are kind of getting overwhelmed at this point by just how prevalent AI is in everything right now?

You know, it's funny, funny you say that because my answer to that question has changed so much in like the past month even.

So first of all, no matter what, like saying AI is like saying like the medical field.

Like what does that even mean?

Like you have, you have folks that build robots that do surgery, you have folks that use the robots, you have doctors actually doing surgery, they're doing different performing operations on different parts of the body, etcetera.

And you have RNS, you know, LPNS, you have so many people that are expertise in different things that contribute to like a higher goal, you know, to execute on something.

So one of the things I, I used to recommend is always like, learn some, you know, at least one or two things rather deeply.

So if you're in network engineering, that might be like TCPIP or BGP, like learn something that's fundamental that isn't going away.

And as you try to navigate the confusion of everything else that's stacked on top of that, one thing I found, particularly when I was working on the enterprise side, that was really useful is, OK, what's a problem that I'm solving or that I, I have to work on day-to-day with my job today?

Is there some way that I can figure out, OK, this AIMCP stuff, can it fit into what I'm doing?

And also there's tons of ways to lab this stuff like free tiers that you can run things on in the cloud, cheap subscriptions, just buying some tokens, like it's not super expensive to just start labbing and figuring things out.

So I'm always I'm a big believer in like practice by doing experiment, you know, that's one of the best ways to learn.

And then lastly, I actually recently wrote a prompt I just started using like clod code here in the past two months, like just trying to figure it out and, and start using it to optimize some of my workflow.

But just going in and, and, and just figuring it out and I, I built this prompt that that basically said, Hey, I am AI, forget how I worded it.

It was like, I am a college graduate from computer science, yadda, yadda, yadda.

I built like a little profile.

I, my focus is network engineering.

This is the year.

These are the big innovations.

What is actually real and applicable to my career that I should be focusing on and that what it spit out?

Was.

Actually really powerful, really useful.

So use those prompts like don't just go in like Leroy Jenkins and say, hey, what do I need to learn today 'cause that is not gonna get you anything valuable.

It's gonna just start assuming and hallucinating and guessing on all sorts of stuff.

You know, the, the power of good prompting is huge.

You know, I, I, I'm in a crossroads here.

My employer is not very AI friendly.

They're they're scared.

You know, the, the big boxes are scared of it.

Where me on the other hand, I'm so intrigued.

I'm in some, I'm in some large language model.

Every day I'm, I'm doing different things with it.

I just find it fascinating.

So I have to do it at home.

You know, I have my home lab networks and things that I tinker with, but it, I, I learned early on, you can't just give it some, some Joe Schmo like basic prompt, like, Hey, do this for me, because it's going to just completely go off the rails and you're going to be fighting it to, to do what you actually need to do.

So the power of, you know, good prompting is huge and it can be used in so many different ways.

And I love that example you just gave because, you know, a lot of people are using it to help learn what they need to for this career field to help try to stay ahead of the curve.

And with AI, it can be difficult when those hallucinations happen.

And you might not know whether or not it's hallucinating.

It can really lead you down the wrong path.

And then you're sitting, you know, in a job interview you're in, you're spouting the stuff that you think you know, and they're like, are you from Mars?

Like what?

What are you talking about?

That's not how that works.

And, you know, I bring that example up because I seen it, You know, I'm a hiring manager and I had someone tell me something.

I'm like, no, like, where did you learn that?

And he's like, oh, well, I, I used AI and it told me that's true.

I'm like, go back.

And yeah, exactly, exactly.

And I just, I had to, I had to hold myself back from laughing because like, I felt bad for this person because like, they had been, I don't know how they've been using it.

But yeah, that's, that's a that's a huge thing to also consider.

You know with AI it's I still feel it's so much in its embassy stages and who's late hallucinations can really run wild sometimes.

Yeah, and even using it when you should.

And I'll tell you even the other day, so I was working on AI had this a spine leaf topology that I'd built out in container lab and I forget what one of my spines or all of my spines, I can't remember.

There was some problem.

And I was like, so tempted to just dump the configs and chat or in an LLM to, to figure out what the problem was.

And I'm like, I can't do this.

I can't do this for everything.

So I sat there and I did network troubleshooting until I found the issue, which took a little longer than I was like.

But if that keeps it kind of keeps your edge in place a little bit because I don't want to like lose.

I don't want to make my, my job in some of my things so easy to where I'm just using it for everything.

And I'm doing that to be intentional.

Not that I don't want to.

Start time back.

Yeah, exactly.

No, absolutely.

Because that's what's going to keep you from being replaced by just someone who can prompt, you know, your job all through, you know, away.

So, you know, having those skills in front of mind.

Exactly.

And yeah, Speaking of context, you mentioned MCP, which was the other part of my, I forget where I was going with that answer, but like MCP is definitely a model context protocol.

It's been the game changer for AI as far as being adoptable I think in enterprise.

So.

Absolutely.

And that's was literally the next thing I was going to ask you is, you know, MCP is kind of the hot new thing for automation.

And, and I I feel like a lot of IT pros still don't understand it or even quite understand, yeah, what it is.

You know, they may have never heard of an MCP server.

What it does do you can you kind of break it down what MCP?

And like like I said in the beginning, why it's probably one of the most important things when it comes to AI and networking that people should really Start learning.

Yeah, absolutely.

So MCP has become so important for the, the success of making, you know, AI actually work at an enterprise scale to enterprise level.

And one thing I heard a lot of when I first started looking at MCP was, hey, it's just a wrapper around an API.

It's not anything else.

It's just nothing beyond, you know, the existing, you know, function calling mechanisms and things we build.

And I, I dug in, you know, because I kind of wanted to believe that because believe it or not, Dakota, like whenever anything new comes out that I have to learn, my first reaction usually is like not another thing, another thing.

I.

Just don't have time for all these things.

It's a little crazy.

So as I started digging into MCP and I guess we should start out with a oh, like an explanation of like why what is MCP or why does it exist?

So it kind of just hit its one year anniversary.

So it's only a year old.

So it's got its one year onesie threw a birthday party for it.

It was great.

Everybody loved it.

So MCP is basically it's an open protocol.

It's open source.

You can actually go to the GitHub repo and look at it.

And if you look at the GitHub repo that I think the official definition is it's a open source protocol that enables integration between LLM applications and an external data sources and tools.

So basically a way to securely use an LLM against infrastructure that that you haven't managed.

So if you, you can think about it like, so MMCP is kind of to AI like what BGP is to routing.

So it's a, it's a standardized way for different systems to exchange information and capabilities so they can work together.

And I guess going beyond that, like what is the problem it solves?

So imagine if every router vendor invented their own routing protocol that only worked with their equipment, yadda yadda yadda.

We all know that never ended well.

But that's essentially kind of where AI integrations were like pre MCP.

So every AI model had its own way of connecting tools, databases, external systems, etcetera.

You know, you got all these developers, you know, writing all this custom glue code everywhere for integrations.

Cause developers going to develop, that's what they do.

They build a lot of stuff.

And then what happened was MCP kind of got shimmed in there and it creates a common control plane between AI models and the tools and data they need to access.

So instead of like a, it's kind of like going from hub and spoke to full mesh if that kind of makes sense and making integrations a lot easier.

No, absolutely.

It's funny, the first time I actually heard the turn MCP was at the attentional booth at Cisco Live.

That's kind of where I got introduced to it.

I'm like, what do you mean?

It it's the bridgeway almost from AI to your network and it it didn't like, I still, to be honest with you, don't fully understand it because I'm not in the weeds enough with it.

You know, it's not something that I can necessarily implement yet into my networks, but it's I can definitely see how it's really going to help shape the future and bridge the gap there, you know, and I guess you know.

Just to kind of talk about what you guys at itential are doing with MCP, it kind of intrigued me.

Do you mind kind of diving into that a little bit?

Yeah.

So first of all, the way that itential was approaching AI was one of the reasons I just started working at itential this year, earlier this year.

And one of the things I loved so much is like when LLMS sort of came to market and everybody jumped on just every little thing every, you know, all tons of vendors had these chat bots that would allow you to chat with like not very much documentation.

You know, there was just a chat bot for everything and all these different things.

And I loved how I, Tenchel didn't just jump on that wagon immediately, but they were thinking about and I remember in my interviews, it was one of the things that was important to me was, hey, we're not just going to go and try and roll anything out to market.

We want to wait until like a real use case comes up and really build around that real use case to provide real value to our customers.

So what that kind of looked like is, and again, we've been on a aggressive innovation trajectory this year.

I've had a front row seat.

It's been crazy exciting and just awesome.

But it, it kind of started out in May 2025.

Yeah, we're still in, we're not 2026 yet, but it's close.

Yeah, starting to get the wires crossed.

But we, we launched the our MCP server in Prague, Czech Republic at an event called Autocon 3.

So the core idea was deceptively simple but powerful.

And it was that, you know, enterprises are adopting, they're trying to adopt this agent stuff, LOMS, they're trying to use copilots and it's going so fast.

But in our minds, nobody had really solved the let's use network analogies all day.

So the last mile problem, let's just say last mile problem, how do you let AI actually do things in production infrastructure?

That just makes me nervous.

Yeah, exactly.

But you've got to do it without losing governance, compliance, auditability.

You have to have those things.

So how do you do that?

And the MCP server was our answer there.

And yeah, I mean that that's the census when you just started.

Like how do we let AI into the production?

I'm just like, my stomach started churning.

I was just like, you know, cuz I've seen, you know, AI hallucinate when I've used it to help me troubleshoot the problems.

But yeah, MCP kind of puts those guard rails in a sense into on AI.

And do you mind kind of diving a little bit deeper on like how how that happens 'cause I, you know, just for, I know I'm curious.

So I'm sure that the audience listening is.

Yeah.

So again, the MCP server was our answer to try and solve for that.

And so as we discussed, you know Anthropic came to market with model context protocol as a standard.

So it acts as a control layer between the AI systems and the potential platform.

So every, there's so many words for things now it's like out of control.

But for every, I would say like AI generated action, whether it's like a configuration change, a compliance check or something for remediation, like a remediation workflow, it gets routed through a like a policy enforced workflow.

So the existing workflows, validations and approval systems that you already had in place, the governance you already had in place with potential, like all those guard rails are actually still there and still working.

It's just everything.

On top of that, how you interact with them, how you're able to integrate them with other things is much more simple with MCP and the exposing and filtering and dynamic calling of different tools in tandem with other tools.

So this you can think of MCP is kind of like the, the catalyst to get things really going to where using agents is a Gentek AI thing can become like real life.

So we recently at Autocon 4, which was a few weeks ago actually, we, we launched our Flow AI, which is a full orchestration platform.

And this takes everything we learned from the MCP server and kind of extends it into what we're calling AI to Action Continuum.

So going into, I guess, staying kind of more focused on MCP, but like you kind of need that.

You need safe integration, you need things like Oauth under the hood, you need guard rails.

You need all of those things to really understand and define what is contextual and like where you know where the basically where the D mark is from reasoning to determinism.

So what I was talking about earlier with those, those workflows that go through and, you know, test something or they validate something or they do a specific thing, they include guardrails.

Those things are, are deterministic workflows, but all the things that require so much reasoning that a human would have to do above that layer, that often takes a ton of time.

And now a good comparison to that would be like finding an available IP address in a range in some system that you use.

Well, there's many different checks to where you can determine if an IP address is being used.

You can look in that system and see, oh, it's there, it's reserved.

It's not reserved.

You can do checks to make sure it's not reachable from different networks within your organization.

There's lots of stuff you can do.

So you can think about all that advanced reasoning being the agentic stuff on top and, and really the Sky's the limit with the the agent stuff.

There's so many different very cool use cases.

But yeah, we we don't have to go there for the conversation.

It's, it's, it's fascinating that those things is what excite me about the future of automation.

And to I think the untrained ear, that sounds like it's going to replace people.

But to me, it feels like it's just more tools to make your workflow better and to actually allow you to do the things we've been dreaming about for years.

You know, the, the, the things that we've been begging for.

You can actually now start implementing some of those things, but I want to backtrack a little bit before that.

You mentioned something that really fascinating.

You know, we have these start-ups right now that are building the tech that these enterprise companies want, you know, but enterprises often can't really adapt it without the right foundation.

And I kind of feel like MCP is again bridging that gap for them.

You know, it's helping put those securities in place so it can actually be adopted by more organizations and, you know, become more mainstream.

Am I right there?

Am I misunderstanding it?

No, that's true, definitely.

But it's like the whole analogy of, you know, how do you eat an elephant?

Like one bite at a time.

There's so many things that you have to do.

And one of the things that we're big on identical is it's it's a, you know, these things aren't absolute must haves, but it's a good idea to have some foundational things in place with automation first.

Like get some of these wheels turning and think of like, OK, like if you're in that spot where you're maybe a team of three people and you're the only ones doing network automation for the entire company.

And if one of you left, you have problems and it's back to doing a lot of manual things and a lot of things break, that's that's a problem.

And you're going to run into that same problem.

If you start to just throw AI on top of things, it's actually maybe going to make some things worse.

So there are some fundamental mechanics that are, I don't want to say they're absolute must haves, but they make a lot of sense, you know, before you start embarking on this journey.

And one of those things I would say is, you know, taking a platform centric approach to how you build and maintain infrastructure.

So that way you can really orchestrate across like Federated systems, like typical big, you know, like enterprise companies have.

So it's not just Tay me, William, I need to automate like 30 switches or something and that's it.

I'm in my own little world.

It goes beyond that because you have ticketing systems, you have to update, you have, you know, monitoring systems and things you have to onboard, you have other teams that you have to update.

And you know, that's one of the beauties of like where this MCP paradigm fits in because, you know, one of our partners Selector AI, they do telemetry and they take all this contextual data from network devices and do just a lot of triage and, and let you know sort of the the problem behind the issue.

So it's like you plug in their MCP and they're the visibility and kind of the first face of the troubleshooting phase.

And then you lock our MCP in next to that and we do auto remediation for said problem based on all this contextual data that normally a human would have to get.

So if you think about looking at the, the routing table for BGP, like let's say you had a BGP flapping issue, there's a lot of different things that can make BGP flap, whether it's upstream or something, an optic or some problem on the site.

Well, going through and figuring those things out, like when I worked in OPS before, we could just adjust BGP or like shut down a neighbor to, you know, for stability sake, we would have to go through and check a bunch of stuff and we'd have to document it in a ticket before we could do it.

Well, imagine if that busy part of the human aspect of that work was already taken care of.

Like the ticket was already documented, you knew what the issue was, and then you could just run one of a few remediations either automatically or with the human in the loop.

So that becomes a really big time saving is what I'm getting at is utilizing these tools, you're saving yourself time.

And that's the biggest complaint I had as a network engineer for as long as I was a network engineer is I don't have time because I'm firefighting.

But what happens if you could just limit that firefighting by 50%?

That's a lot.

That's a lot of time.

Yeah.

I mean, especially when you have organizations that are working with really small teams, you know, for years I was the only guy in, you know, working for this ISP, managing our network operations center.

And I found, found myself constantly putting out fires.

And I can never do things to advanced systems to I was, I was always fixing problems.

I was never able to prevent them because as soon as I got one thing fixed, oh gosh, this thing's gone fire now.

And so I, I could definitely see how having these systems because, you know, things aren't going to necessarily change there where, you know, it's not just like I'm going to snap my finger and then have 20 employees under me.

That's just not how businesses work anymore.

But having the proper tools that help me do my job better is, is huge.

And you know, that's just, I feel like 1 use case like for MCP and you know, you're in the weeds more you, you see how MCP is being used, you know, other than, you know, trying to help make our lives easier, what are some other advantages that you see businesses like how are they implementing it to help further along their organization?

Well, one thing that I've seen a lot.

So usually it's like when when you adopt something like network automation, like I remember when network automation started to gain steam.

And the first time, yeah, the first time I was actually allowed officially to use network automation in in a big network, it was all read only.

Like we weren't making changes.

We were pulling specific types of data into a machine.

We'd built some Python based on like SOAP, XML craziness back in the day before Russ stuff was popular.

And then we would transform that data that we pulled from these different places in the network and then we would populate it in a dashboard.

And all of this took a lot of lines of code and a lot of different things to get just that one thing working and that dashboard up for our network operations center that showed this, these top things that we, we wanted to see at all times.

And, and there were things at the time that we would like, we were actually waiting on a custom integration with one of our monitoring vendors that we had.

And you know, we just waited for a while and we just decided to build it.

Now MCP is really useful in that instead of, if you think about how a Restful API works, if I'm building something like that's based on REST, you know, MCP being a protocol.

REST is not a protocol.

REST is actually an architecture style framework that uses a protocol.

It uses HTTP under the hood.

But Restful APIs, they're stateless by design.

So every request is a self-contained request.

There's no server side session state or anything.

So when I'm building something with MCP to do something useful like that, I'm saying, OK, I have a Restful call to like maybe pull a list of devices from inventory.

And then I'm going to have another call that's going to filter this other device type, OS type maybe, or something along those lines.

And then from there I'm going to pull the config from that device.

I have a separate call for that.

You know, there's, so it's like this Lego building of different Restful APIs to get to your, you know, outcome where MCP is more like it's not built for humans to write software against like REST.

It's more for AI models to interact with these external systems.

And it's stateful by design.

So it maintains connection throughout the duration of the session.

So you have things now to where it's easier to integrate and pull data from System A and System B and System C and then maybe to like send a report to your boss daily with your company's brand guidelines stamped to the report so it looks official.

So with MCP, you know, this is hypothetical.

I could be in my chat host and I could say, hey, I want to pull the software version or the status of these certs or this or that.

I want to pull that data from this subset of devices.

Once you do that, I want you to put these in a table, categorized or stack ranked this way.

And then I want you to generate this in APDF and I'm going to provide you brand guidelines.

And then from there I have the other MCP for my mailbox or something else.

I want you to mail that to this person, which is my boss.

I want you to do this like every other day or something so I can type that in with human, you know, just text and get that outcome at the end or the end of the tunnel.

So and doing that manually or building something via REST with some of the custom things we need to see would be very challenging for most network engineers so doing.

Huge time consuming too.

Yeah, exactly.

Time.

The thing that you can't get back.

Yeah.

No, and IIA 100% see the value in that because you know, a lot of network engineers are already spread thin, you know, they're trying to do they're trying to do everything and just being able to buy back that time to build those systems.

And then honestly, there's a chance that maybe even build it better than you could have in the 1st place just because you know you, you only can know so many things.

You know, we, there's, there's only so much knowledge a human can take on.

And so by being able to do multiple things that maybe you don't quite have all the knowledge on, you're making yourself a more qualified job candidate as well.

Because now all of a sudden you've unlocked this other skill set without having to invest years to learn like a whole nother coding language and all this other thing just by being able to communicate in a human way with these systems now.

Yeah, yeah.

And it compounds too.

So when you think about back to your comments earlier about the job loss, that's one thing I hear all the time and is someone who recently started to really take a stab at vibe coding here in the past two months or so, three months, you cannot.

I mean, you can build a very small prototype.

I mean, maybe you can get maybe 20% of the way there, but once your code base gets too big, it's really hard.

And once you have to start rationalizing some very complicated design decisions, the AI and the way that AI works with tokens and quadratic complexity of tokens and like losing context over long, there's just a lot of reasons why it's really hard for someone that doesn't have coding experience to build a full-fledged application 'cause it's really hard.

Apps are hard to maintain.

Things change and there's so many moving pieces and it is a tool, full stop.

It's not.

So I'll tell you what it it may take your job if you're not looking or trying to leverage any of this stuff to make yourself better.

But if you're using this stuff and you're getting in the weeds and you're, you know, leveraging it to make yourself better, make yourself more productive, producing more, more efficiency, that's going to be winning going forward.

So those are the folks that are really going to thrive, I think.

You know, I, I was actually doing a live stream last night on my my channel and that question got brought up is like, what does the future look like in AI?

And the people who are leveraging AI as the tool that it is are going to thrive.

This is this is just the natural evolution of tech.

This is just an, you know, this is how the industry's evolved over years.

You know, new systems got implemented and people lost their jobs because of that.

But then that new system that got implemented opened up like 5 other style of jobs.

Like it's just the shift, the natural progression of the industry.

Something this industry is always in innovating, it's always evolving.

And if you're in tech, you have to constantly be evolved with it.

You can't just stop learning.

You have to be lifelong learners.

You have to constantly be educating yourself on the latest and greatest things.

And not all these technologies work out.

You know, we've seen plenty of these buzzwords that come out and just die and tank, but, and you still have to learn them because what if it is the next big thing and you have to be willing to evolve with it.

And if you evolve with it and you, you, you get excited about these things and these new opportunities, you're going to excel.

You're going to be writing your own check by the end of the day because you're going to be light years ahead of the crowd.

You know, another thing that came up during that live stream is that, you know, job market saturation, that there's so many people going after these jobs.

You can't get a job.

And I have someone complaining that, you know, they, they just find it bad that, you know, someone with a college education can't even get an unpaid internship.

And I'm like, that's not the problem.

You're looking at this wrong.

Like, what are you doing to make yourself stand out from the crowd?

How are you differentiating yourself?

You can't no longer say I got a college degree.

I should be good enough.

You know why these people should just be tripping over hand to foot to offer me these jobs.

That's not how it works.

And maybe, you know, 20 years ago, then there's a chance that might work that way.

But I even no, I don't feel like it was that way.

I feel like you still had to market yourself.

You had to work on your personal branding.

You had to be that diamond in the rough and convince the employer why you are the one.

And by leveraging these tools, you know, there's still plenty of people, like I mentioned in the beginning, who've never even heard of the term MCP.

This is going to be what's going to make you stand out to those big organizations and get those large paychecks.

I mean, I, I, I hate seeing talking about the money part because if you're in tech for money, you're, you're in the wrong industry.

But that's what people resonate with the most.

You know what, I, you know what I'm saying?

So.

Yeah, you mean you're right about the job saturation.

Right now it's on believable and every that's another thing is AAI generated resumes.

I ran into that at one of my last roles when that first started and I was a hiring manager.

Every resume was AI generated.

It seems like they were all experts at all the things and you would get into like 5 minutes of the interview or 10 minutes and you'd realize that like half of the stuff was like they didn't even know half the stuff.

Like it was just AI generated.

And I talked to someone actually and they said that what's happening is there.

There's actually tools out there that will do this for you, I guess.

But you put in the job description and say this is my resume template build and it will.

Yep, just build.

It out for you and then you.

I've had, I've had those companies reach out to sponsor the channel and I'm like, no, that's, that's, that's, that's counter, you know, intuitive, that's counterproductive.

You know, I, I, I had this issue my, my day job.

We hired someone, you know, they, they resume looked great.

So I brought him in for an interview.

He talked all the right stuff during the interview.

So I'm like, you're hired, get him into the job.

I'm like, OK, can you SSH into the switch?

And he's like, I don't, I don't know how to do that.

I'm like what, like how do you not know how to SSH into a, a switch?

Your resume says you're well experienced in that.

Like, yeah, we, we, we brought him in on a Monday and handed him his final check on a Friday.

Like, you know, and that, that's on me because I didn't push it enough, You know, I didn't try to probe into him enough.

I just was like, oh man, this guy's got to be the guy for the job.

But yeah, we like you mentioned there, there's those tools out there.

But you know, if you're able to actually lab the skills and demonstrate, you know, these skills, that's, that's the biggest advice I give is, you know, building a home lab or, you know, or, you know, using it at the cloud platforms, you know, there's, there's free tiers for all of this and you can absolutely 100% practice all this stuff and then document it, show how you are doing it.

Again, it's it's, it's major.

Blog about it.

Create videos to be the next Dakota, the AI Dakota, right?

You know, I am totally for that.

If anyone wants to go out and create their own YouTube channel talking about their journey, what they're doing, giving advice like you that that is you're going to stay like, you know, you, you submit a resume and you go for an interview and like, Oh yeah, you want to see what I'm doing here, go to this YouTube channel and it just has your everything you're doing.

It's just going to be, I'm going to hire that person in a heartbeat.

I'm like, that's amazing.

I can actually see you do the job.

You're hired like, yeah, you know, that's great.

And, you know, YouTube's not for everyone.

You know, I, I understand that, but there's still different ways you can, you can go about it.

You know, it is really amazing.

You know what you can do?

It's.

A living resume.

Absolutely, yeah, you know, and I'm glad we brought that up because, you know, there's, there's multiple sides to this coin.

You know what you can do now with AI and it's actually living and breathing in the networks that we're using is amazing.

But I again, it, it is, it's a bit scary to some people when they don't think ahead on how it's making them a better network engineer and how they're actually using it.

So, you know, we, I think it's great that we talked about how you should be demonstrating those skills when you're looking for those jobs.

And you know, for people who are wanting to future proof their careers right now, what should they Start learning?

You know, what do they need to start experiencing experimenting with today?

So that's a really good question.

That's a a great question.

So the first thing rule #1 of getting experienced club is don't look at all the hype.

Don't start with all the hype.

Just stop and and make sure that you have a few foundational things that you are continually studying on.

If it's network engineering, I did a lot of BGPTCPIPDNS.

Those are important things to know.

Don't take shortcuts.

Learning those things is going to pay dividends over time, I promise you.

And the second thing is, you know, again, avoid the hype, but start doing these things at home.

Like you said, I have a, I've had a few different AI labs over the years, but right now I have a few RTX fifty 90s in the basement with and Linux is the other thing.

Learn Linux.

Use Linux as much as you possibly can because that is like everything runs on Linux at this point.

It's crazy.

So learn Linux, you know, learn some Python.

That's also very good if you can and then just get to work, start doing real things.

There's so many different tools out there that make this easy, AKA container Lab.

We have a free tool called Torero that you can use for automation.

I've containerized all that.

So it's really simple to deploy in tandem with Container Lab.

You can load things on it, run Python against real networking things and you know, you can insert an MCP server and ask questions and figure out, I mean, hey B, you know, experiment, ask AI to build you the OSPF network and manage it, build an agent to manage it.

Say to to the point to where you can say I need to do redistribution between this and this, put together the plan, deploy the config and then build your own guardrails.

Like all that stuff you can do for practically free, cause MCP is something that you can just, it doesn't have to be a public service.

You can write it or build a private MCP that runs over a command line tool if you want it.

So it's just really interesting all the different things, but start with the real use cases.

If you think about where the real problems are that you know about and then start working on small solutions for those problems.

Those problems are probably personified in in larger networks and larger, you know, businesses of course, so.

Absolutely.

And you know, I kind of want a nerd out for a second because you know, you guys over there at I Tenshall have built your own MCP server and we've talked about it a little bit, but your MCP server is open source, which kind of blew my mind.

You know, it got mentioned, you know, mentioned to me at Cisco Live when I was at your booth that you guys are putting this stuff out open source that anyone can use and start playing with.

And can you kind of give us a walkthrough on how my intentions MCP server is actually built?

Because you've had first hand, you've been the one working on developing it and everything.

Yeah.

So the thing about our MCP, that's really different.

So one of the patterns, and there's a little bit of knowledge that comes along with this and there's a reason why there's so many MCP servers out there today.

And the reason for that is a lot of MCP servers were created by basically just taking like an entire open API schema file and just feeding that into a tool that basically converts your schema file into an MCP server that you then just put on GitHub.

So that's why like, I mean, it's kind of like the early days of REST APIs, like you just had so many APIs that just popped out of nowhere and there was like a proliferation of them.

But now you're seeing it's so crazy with MCP because most folks have REST APIs and they're just putting it through the machine and it's popping out the other end.

So there's a few problems with that.

I'd say the first problem is there's when you, when you do it with just passing in the schema, there is a ton of glue code that is required to actually make the thing work.

And by a ton of glue code, I mean a ton of of glue code like you have to account for, you know, the agent loop.

You have to account for writing like very, very customized logic for, for filtering tools, for categorization of things like how things are dynamically selected and launched.

And if you pass in like an API schema, you could be passing in and generating like 100 + 500 tools.

And then if you're calling them all every time you have a, you know, there's so many words for things.

They call it context window bloat where your context window's just blown up.

And this in addition, is going to cause super degraded agent performance decision paralysis.

You're going to get things out the other end that just do not make sense.

So when you know, and I can't take credit for any of the MCP design originally this is all Peter Spurgata, the architect who brought actually brought Ansible for networking the market.

But he went in with a pragmatic approach of saying, OK, you can't just build something that exposes everything and cross your fingers.

You need logic behind how this is structured.

You need all the, you know, when you think of Oauth and all the existing value that we provide today that our customers have built, how do you build on top of that to make sure MCPS layered on that in a secure way that honors all the guard rails, all the compliance and all the logic that you've built in?

How do you dynamically launched with tags that the categories of tools that you want to use and how do you also generate that or you leverage that on a persona basis?

So like with our MCP, if you were to plug it in to like clawed desktop or anything LLM or something or another, you connect the MCP and it's controlling what you have access to within our platform.

And then you just, let's just keep it simple.

Say you have like a an operator persona or an engineer persona or an architect persona or something.

Well the operator persona may be able to do like read only things.

They may be able to reset certain adapters, visibility stuff, health check stuff.

So when they leverage the MCP, it's the only thing they have access to.

They don't have access to push config or do anything that might be dangerous.

It like a a level 1 operations level, but then when you get a little bit higher to that network engineer, how do you leverage the power of this MCP server in tandem with our platform as a network engineer, when you need to do some more complicate, you know, maybe you want to back up some ad hoc configs on some stuff that you don't have in your existing process.

Or maybe you're getting into just taking context from a partner like selector typing it through with itential and and doing some auto remediation with certain things.

So having that persona based approach is something we really focused on and being able to control, filter and categorize the tools that are actually exposed in tandem with like all the industry best practices with O auth with the way we do role based access control at itential.

So the way that this sort of plays through is like starting at the least the the least thing that would cause any sort of issue.

Again, doing the read only stuff and then slowly giving the AIA little bit more, a little bit more, a little bit more.

And then when you get towards the end of actually like pushing config changes like the other day with our MCPI actually connected our like our gateway product to our platform, our gateway manager platform.

And then from there, I just started creating services with the MCP, like a bunch of Hello world services.

And then I stitched them together and then, you know, I ran them and then I did concatenation with the different ways that each thing said Hello world.

And I was just kind of experimenting and it was really easy to do, you know, building this stuff, you know, through MCP and through a chat interface, you know, because it's like, however way that I want to think about doing it, I can do it that way that fits my the world that I live in.

So yeah, a lot of advanced features, not just your run-of-the-mill pass through the tool and out pops out an MCP server design.

You know that that is 100%.

I feel like the future.

And, you know, Speaking of the future, I kind of want to pick your brain about what we're going to see over the next couple years.

But before we kind of go down that rabbit hole, if people want to connect with you, if they want to check out I Tension, if they want to connect with you personally, where can they find you?

Where can they learn more about I Tension, you know?

Yeah, absolutely.

So I tension.com and I would go to the GitHub slash I tension, which I'm sure you can link in the show notes.

I would look at the MCP server again, it's open, check it out.

And then we announced our Flow AI product.

I can give you a link for that as well.

So that's just got announced this month.

And if you want to find me, you can find me at William Hyphen Collins on LinkedIn.

And then I have a link tree which kind of links to everything else.

It's macro engineered.

So like on YouTube, Instagram, TikTok, all those places.

That's my handle, so you can find me.

And then of course, the Cloud Gamut, if you go to Packet Pushers, you can find the Cloud Gamut podcast that I host with Yvonne, Yvonne Sharp.

Awesome.

Yeah, we'll definitely make sure and link to all those resources down in the show notes below.

Now, again, like I was saying, I kind of want to talk.

Pick your brain.

You know, you are definitely born in the weeds than I am.

What do you see the next?

You know, I normally I'd ask what the next five years look like, but I think that's too far out.

I think too much is going to happen between now and then.

What are you seeing the future look like in the next year or two of, you know, AI and networking?

You know what, what are some new things that excite you so much?

I know that's a loaded question because we we can never possibly know how fast it's evolving, but.

Yeah, I'm always like shrewd when I think about these things and I usually underestimate, but I think with AI that's going to be a little bit different.

So I think MCP really making it to Main Street now has really because AI, this AI agentic stuff was kind of Catching Fire a little bit and gaining some momentum.

Pre, pre MCP, people were talking about agents, it was kind of a thing.

But now MCP has actually rapidly accelerated the ability to do that.

So what I see in the short term, and I've actually worked with customers and talked with customers that are actually doing this today in production, some of them are ISPs, but bringing in and integrating something like MCP into their process with what is known as.

So you have like human completely in the loop.

So basically human running the loop, doing all the things completely manual.

And then you have something that's human on the loop, which you're bringing AI in to do things, but the human is really involved in the whole process, watching each change, vetting stuff before it happens.

Like the AI has control to do some stuff, but not all the things.

And then you have this human out of the loop, which is everybody's dream.

Like, hey, you know, we don't need humans anymore.

Get rid of your knock and you know, blah, blah, blah.

Which for some things way out in the future, I think it's going to be a thing not for.

There's many parts the tech stack that AI will never touch, especially with certain verticals and certain technologies.

It's just reality, let's be real.

So I think short term, we're going to see more human on the loop use cases where you're doing a lot of read only stuff, you're testing it out in your lab in the data center and companies are trying to figure out how to do it.

And then there's also going to be a proliferation of customers or I mean, companies like Itential that are rolling out a lot of good innovation and productizing a lot of what AI can do for the enterprise into a package that you can buy, use, and, you know, have some sort of influence on the road map.

And then you're going to have start-ups coming to market that are doing brand new things that are, you know, productizing AI and packaging it up to where an enterprise can buy it, use it, get an SLA of, you know, all the things that enterprises do.

So really short term, a lot of human in the loop use cases, small things.

And then going into the future like 5, maybe three years and beyond, I bet you will probably see more agentic human out of the loop use cases as well.

Yeah, and I feel like having that human in the loop makes it easier for organizations to adopt, you know, there's a little bit less fear of, you know, you know, Skynet at that point.

Just put it in layman's turn, you know, because.

You got to build trust.

Yeah, right.

Absolutely.

You have to make sure you know you're doing the right things because, yeah, it is scary when you kind of think about what AI could do with things went off the guardrails if you didn't build those guardrails properly for it.

So having that human loop definitely makes it a more palatable thing for organizations to, to to adopt.

And I definitely see that becoming the future.

That's what that's what's coming down the pipe now.

That's what we're seeing as of right now.

I feel like slowly but surely it's becoming more in mainstream.

But yeah, IIA, 100% agree with your your predictions there.

I, I really appreciate you.

You've dropped so much knowledge, so many so many useful tidbits that I feel like some people are gonna have to rewatch this episode again to get them all.

But I really appreciate you taking the time to join us today and just offer so much great information.

Absolutely.

Thank you.

I had a blast to get into chat and nerd out on these things.

Yeah.

So thanks for having me.

Absolutely.

And again, I'd absolutely love to have you back in the future because I feel like there are so many more things we could have dived deeper into that we just can't even cover it in the amount of time we have.

Wide topics, yeah.

Yeah, exactly.

Everyone, I really hope you enjoyed this episode.

I hope you took some some useful knowledge away, some motivation to go out there and start practicing and learning these things because this is the future.

This is how you're going to advance your careers nowadays and just go out there and Start learning, start tinkering with things and start messing with things.

Everyone again, until next time, keep learning.