AI and Infrastructure Engineering

AI and Infrastructure Engineering · Omegion Omegion I run infrastructure at high-traffic, high-responsibility scale, and small mistakes get expensive. Most days that's Kubernetes and AWS, other days a small Go tool or a side project shipped for fun, most of which didn't work out.

Home

GitHub

RSS

Tags

Introduction There’s a push right now for whole companies to adopt AI wholesale - dump every bit of context into it, write an AGENTS.md or INSTRUCTIONS.md in every repo so any project is discoverable and contributable by an agent, not just a human. The question that comes with it is the obvious one: does this make engineering redundant? Once all the context about a stack and its infrastructure is written down somewhere an agent can read it, are we next? I don’t think that’s quite the right question, because we’ve already lived through a version of it. We’ve Been Here Before Did Kubernetes kill Ansible? Kind of. I haven’t written an Ansible playbook in years, not because configuration management stopped mattering, but because Kubernetes made server management easy enough that we stopped building our own node images at all - we just use whatever the cloud provider hands us, an AWS AMI built for us, no questions asked. And when did I last SSH into a node to debug something? Mostly never. If a node’s acting up, I kill it and hope the replacement doesn’t have the same problem. The next layer up went the same way: run a container on ECS Fargate, in a Lambda, or on Cloudflare Containers, and I genuinely don’t know or care what node it landed on - but that doesn’t mean nobody’s orchestrating it, it means I still decided that workload should be a container in the first place, what image it runs, what it’s allowed to talk to, how it scales, what happens when it fails. Kubernetes and serverless containers didn’t remove that layer of decisions, they moved the unit of work up from “the machine” to “the workload,” and everything below that layer got quietly automated away. Nobody would say Kubernetes, or Fargate, or Cloudflare’s container platform, replaced infrastructure engineers. Each one replaced a specific layer of manual work - hand-building images, hand-patching boxes, knowing which node a workload landed on - and the engineers moved up to the layer above it every time. I think AI is doing the same thing again, one layer higher. What Changed Day to Day I use Claude daily to generate Helm charts and write Terraform modules. The part it actually removed from my day isn’t the thinking - it’s the lookup work. I don’t read through the AWS provider’s changelog to figure out what changed between v5 and v6 anymore; I describe what I want, in whatever shape I want the module or chart to end up, and Claude produces a version of it. It takes iteration to get it into the shape I’d actually ship, but once it’s there, it becomes the example for next time - especially with an AGENTS.md in the repo pointing at it. The same thing happened one level down a while ago: I don’t hand-write raw Kubernetes YAML any more than I hand-write Ansible modules - that’s what Helm charts are for. Increasingly, I don’t hand-write the Helm chart either. I direct what it should do, and Claude writes it. What Hasn’t Changed I still need to know what a good Terraform module or a well-structured Helm chart looks like. I still need to be able to SSH into a node when something genuinely goes wrong and killing the pod isn’t an option - the layer above doesn’t remove the layer below, it just moves how often you have to touch it. And I’m still the one deciding the actual shape of things: what the final version of a module looks like, what’s maintainable a year from now, how a chart should be deployed and versioned. AI does the time-consuming part. I still give the direction. The Skill You Trade Away The honest tradeoff: I’m faster at building and debugging things than I was two years ago, and I’m also visibly rustier at the fundamentals underneath that speed. My HCL syntax recall isn’t what it used to be. My reflexes for debugging a broken box over SSH are a little slower than when that was the only way I knew how to do it. That’s not a hypothetical cost - it’s one I can feel happening in real time, the same way plenty of engineers who came up after Kubernetes never really learned to hand-roll a server image, and were fine, because they never needed to. Where This Goes Next The part I’m less sure about is how long “I still give the direction” holds. Right now I’m the one who decides the long-term shape of a stack, because I have the context and the agent doesn’t - not really, not beyond what’s written down in a repo’s AGENTS.md. But that’s exactly...

AI and Infrastructure Engineering

Related Articles

Incident with Github.com

(no title)

Scientists reverse brain aging, with a nasal spray

Malware infects Android-based automotive head unit firmware

NHS apologises and admits Palantir have access to identifiable patient data