KubernetesのAIエージェント：スコーピングは適切ですか？

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, you know, platform engineering discipline right now.

I think if anyone tells you they know the answer to this, they're not being truthful. 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 stack, right? Having appropriately scoped RBAC. These things are what's going to allow us again, 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.

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?