Swapnil Bhartiya: If you look at platform engineering today, it has become or is becoming the backbone of how modern software is getting built and being deployed. And when you look at companies size of Akamai, that’s not a small feat. Today we have with us Corey McAlliard, engineering manager at Akamai Cloud. When we look at platform engineering it’s very well defined. But when we look at Akamai and the scale at you folks were also people may not know that when it comes to cdn, Akama is a pioneer. You know, you folks kind of, you know, your founder invented it in a way. From your perspective, how do you see platform engineering?

Corey McGalliard: The most critical aspect of when I think about what we’re doing as a team is the fact that we’re taking complexity and you can’t really destroy the complexity, but you can, you can shift that from the developers who are building the products and services that we sell sell to our customers, to a team who is building the platform that enables them to go faster. Right. And so the goal of what we’ve been doing is to take the change safety, security, compliance expectations that the company has and shift them to my team so that we’re concerned about that and then gives developers the ability to just focus on delivering well built products.

Swapnil Bhartiya: So what does platform Engine look like within Akamai?

Corey McGalliard: So I’m in a very interesting position to where I get to kind of work with my team to think about how do we do this in a cloud native fashion. So historically Akamai has been around for a long time and they were actually somewhat pioneers of platform engineering. We’ve been doing this on the CDN side for years. We have practices that are over there. And now that Akamai cloud is starting to grow, we’re trying to look at how the CNCF and their tools are thinking about platforms and applying it to ourselves in the same way our customers would use lke and apply platforms on top of that or use that platform stuff that we have available for us.

Swapnil Bhartiya: Since you mentioned CNCF tool. So I would also love to know what does the CNCF or open source stack looks like inside Akamai and Akamai cloud.

Corey McGalliard: So the stack that we’re working on is obviously built on kubernetes, right? Right now we’re using a combination of crossplane, Argo CD to kind of provision clusters internally. And the way we approach it, clusters are cheap so we hand them to teams and the teams are able to build on top of that. But the core of what we’re looking at is like so you don’t just get a cluster, right. We have expectations of what that cluster looks like, allow us to get against it. Like who can communicate with it. You have the ability to focus on the observability aspects of it. Like so if you’re just going to vanilla cluster, but you get a position to where you have all of these capabilities built into it that we then hand to the engineer. Right. And so we’re trying to give you a faster time to delivery, like a faster path to production. And that’s kind of, kind of the goal. So that looks like again, Argo Crossplane. You ask me about the things we’re using. Cross plane. The observability stack is pretty typical. So Open Telemetry, Grafana, Loki, Prometheus. I’m trying to think of some of the. Oh Kyberno as a policy agent. Like so again we need to be very aware of kind of what’s happening in our cluster and write policies so that it doesn’t matter whether it’s an intern, a senior engineer or an AI agent that’s thinking about like interacting with these clusters. How do we make sure that what is applied against the cluster is like safe, safe to go out into production.

Swapnil Bhartiya: When we look at teams, you know, maybe your teams or other teams that you know because Akamai also serve a lot of customers and you partner a lot. Where do you see they are doing platform engineering wrong? Where you like they don’t fully grasp the concept of where you’re like this is how it should be done. So let’s focus first on what people are doing wrong. When it comes to platform engineering.

Corey McGalliard: That’s a really hard question. One thing that a trap that we fell into, and I’m sure other people have done this as well, is right, you try to abstract away everything, you try to make it to where it’s as simple as possible for someone to pick it up. So in kind of the way you think about this, you have the idea of, of like having all the bells and whistles, having a very like configurable flat platform. And then you have on the other side something that’s more closer to a PAAS or like a software as a service kind of mentality, right. And a lot of us when we start looking at this, oh, we just want to give it so that build it so that you can just hand us a container. There’s a lot of assumptions that go into the way that that works. It’s really complex, right. And what we fell into, we started to go down this path and our first large customer just immediately started saying, well, I need this flexibility or that flexibility. So we learned that not only do we need to kind of consider flexibility in the platform, but we also need to extend almost like modularity to our users so they can contribute back to us and that we can also pull in other expertise within the organization. It’s not all on one team, but it’s more about pulling in the expertise you have throughout the organization to build the platform. Right. So you can’t, you can’t immediately make this like one size fits all solution, but you can build a more modular way and give the ability to make the system flexible. Right. Does that make sense?

Swapnil Bhartiya: It does make sense and thanks for sharing that also. Now, one thing that I’m going to ask is, which may not make sense too much is AI, you know, so talk a bit about how is AI changing, transforming, whether for better or for worse, the whole platform engineering discipline right now.

Corey McGalliard: I think if anyone tells you they know the answer to this, they’re not being truthful. And tell me the answer. Sure. I think more of it is like, what am I seeing in the industry? And the reality of it is during the keynote yesterday here, they talked about like what AI and like the zero day findings that we’ve seen over the last few months really highlights the practices that we need and reliability and resilience that we’ve done for, should have been doing for 20 years are just getting highlighted, right? And so this is true in platform engineering, right? So having policy as code in your, in your stack, right? Having appropriately scoped rbac, these things are what’s going to allow us again to, to basically feel comfortable allowing AI agents, MCP servers inside of our stacks, pushing changes to it, right? Because everything’s appropriately scoped. It’s no different than giving an intern access to your cluster, no different than giving a senior developer access or an SRE access to your cluster. Right? And so that’s kind of the mentality is having the appropriately scoped system so that when you start enabling these services, you can feel comfortable.

Swapnil Bhartiya: How are you folks using AI, you know, internally to build a platform or of course Akamai serves a lot of customers. So how are you also building the platform to serve AI workloads?

Corey McGalliard: So today my team, I’ll speak from a today standpoint, right, because there’s some ideas we have that are kind of propagating, that are going to improve things. But today we have AI code reviews validating what we’re doing. It gives us a second or a fourth set of eyes on everything we’re pushing out. We have the ability to see if we see failures in our testing stack. We have AI review those logs. And so we test our platform end to end. We’re able to build a cluster and tear it down. And the goal is to have a very clear understanding of what failed during our testing cycle. We have AI reading these logs and kicking it out to us. That’s really cut down a lot of time of my engineers going, where’s the error? Because you have thousands of lines of lines of logs and then it helps them find exactly kind of what’s. What’s going on. So those are two really practical examples of where AI is working. And then the thing that we’re working on is using a service called K Agent. I’m not sure if you’re familiar with this, right. To extend the ability to have an agent interact with a Kubernetes cluster and have someone in a more of a support role be able to ask you questions, hey, what’s wrong with this cluster? Can you give me feedback to this cluster? And that’s all really, really new and very, very well. And the challenge we worry about is being well scoped, having appropriate rbac, right? We don’t want it to touch the cluster. We just want it to be able to give us information out of the cluster at this point. And I think that’s kind of where a lot of people are sitting, right? Like some people are really bullish and they’re like, let’s go ahead and put controllers and agents inside the cluster and let it have read write access. I’m more conservative in saying let’s have read access. Let’s get the benefit out of visibility and then we can grow and learn as the industry does and what is key.

Swapnil Bhartiya: Agent permission.

Corey McGalliard: K agent. How do I explain this? It’s basically, I want to call it an agent gateway, right? It gives you the ability to interact with agents, give it access to the cluster it’s running on and also tie into MCP servers. So it gives you a chat interface to be able to ask it questions about the cluster.

Swapnil Bhartiya: It’s kind of becomes a bridge between agents and MCP server so that the agents and the models all run outside the cluster. And it just gives you the ability to like say, here are my logs, go look at it.

Corey McGalliard: Yeah, I’m probably explaining that poorly. I’ve got very, very smart group of people who work with me and day after Kubecon last year they demoed this and it was amazing. And so we’ve been working on implementing it.

Swapnil Bhartiya: When it comes to platform engineering and companies size of a combined, what is the hardest challenge?

Corey McGalliard: I think it’s the same in any organization and it’s communication and it’s more interpersonal than it is technical tools. Everybody’s in the industry kind of understands this, that you have technology available to us. Right. With AI, it’s growing even faster. The problem is the interpersonal pieces. Right. Like so building their relationships with all the internal teams, understanding their needs. Right. And ultimately that’s what technology does. It helps us care for a neighbor well. And that’s why I’m excited about what I get to do because I get to work with this whole group of people and we get to think about what they’re building and help them do that faster and we’re able to care for them all. And that’s actually the harder piece of all of this is working with people.

Swapnil Bhartiya: People is the hardest no matter what. So the only difference is that the size of more people doing training, we have just plenty versus more. If there is an organization, teams are is starting their platform journey, platform engineering journey. What is your advice to them?

Corey McGalliard: Get involved in community. Right. So I attend the CNCF as a platform, a technical community group for platform engineering. I’m involved in this. There are like some brilliant people. And honestly the biggest benefit of coming to conferences like this and then being involved in the community group is being in a position to where I get outside of the echo chamber that I live in inside of Akamai. We all experience this. We all like live in a bubble at work. Right. And then I step out and I go to these community calls and I hear how a bank in Scotland is doing their stuff right. How like we’re all working together, driving the same direction. Right. And it encourages me to know that I’m making sane decisions. But it also gets me to think about problems differently and start there and then learn from people who’ve gone down this path and then start implementing things.

Swapnil Bhartiya: Excellent. Thank you. And if you look at CNCF landscape or CNCF ecosystem, right now, what are some of the things if you just narrow. Because they do a lot of things, narrow it down to platform engineering. What do you think that gets you excited today?

Corey McGalliard: Things that get me excited about what’s happening at CNCF today. So the observability space has always been very interesting to me. Having visibility into what’s going on inside of our systems. The work that’s being done specifically with EBPF and observability in that space, I mean, it’s really impacted the way we do networking. Right. And I have some friends who work in the observability and so the strides that I’m seeing there, the fact that we can move into a situation to where we don’t have to instrument things as granularly, but you just get information from the kernel. That’s super interesting to me. And then yeah, man, like just overall, like conversations that I’m having with people in the platform engineering space about how like it’s less focused about technology, but more focused on like a higher level of how we solve a problem. Right. And I’ve been in this space for a while and I’m really seeing as an industry we’re really coming around to like solving hard problems.

Swapnil Bhartiya: Corey, thank you so much for joining me. And not only sharing your insights on what is platform engineering, how Akamai is leveraging it, but also sharing your insight, how other teams should do it better and also focus was more on people that is also critical and also share some of the mistakes that people made that they should avoid. It was a great, insightful discussion. I would love to have you back on the show, but I really appreciate it.

Corey McGalliard: Thank you. Thanks for having me.