Hey everyone, been lurking here for a bit. I'm a junior DevOps engineer, and we just rolled out Cursor to about 20 engineers on our internal Kubernetes platform to help with development and debugging.
The goal was to speed up writing Helm charts, debugging deployments, and maybe even some YAML generation. But, predictably, some stuff broke 😅. I'm trying to understand the common pitfalls when integrating an AI assistant into a live K8s dev environment.
Here's a specific example that tripped us up. An engineer asked Cursor to write a simple Kubernetes `Job` manifest to run a database migration. It generated something like:
```yaml
apiVersion: batch/v1
kind: Job
metadata:
name: db-migration
spec:
template:
spec:
containers:
- name: migrator
image: our-registry/db-migrator:latest
env:
- name: DATABASE_URL
valueFrom:
secretKeyRef:
name: db-secret
key: connection-string
restartPolicy: Never
backoffLimit: 4
```
Looks okay at first glance, right? But it failed on our cluster. The issue was that it didn't specify any resource `requests` or `limits`. Our platform has admission controllers that *require* them, so the pod was never scheduled. The error was kinda cryptic if you weren't looking for it.
I'm curious:
* Has anyone else seen AI-generated configs missing critical platform-specific constraints (like resources, node selectors, or security contexts)?
* Do you have a checklist or validation step you run AI-suggested K8s configs through before applying?
* More broadly, what broke in your rollout? Was it more about bad configs, or engineers trusting the output too much without review?
I'm using this to build a better internal guide for my team. Any war stories or practical advice would be super helpful!
Learning by breaking
Oh yeah, the resource constraints issue is a classic. Admission controllers will get you every time. It's not just about limits either, it's that the AI doesn't know your team's internal baselines. One team might use 'small: 100m CPU', another uses 'medium: 500m'.
We saw something similar where Cursor generated a `readinessProbe` with a `periodSeconds` of 1, which was way too aggressive and caused constant pod restarts under load. The AI picks sensible defaults for a tutorial, not for a specific, scaled platform.
Have you considered building a small internal knowledge base or pre-set snippets for Cursor to reference? Could help steer it toward your actual platform guardrails.
✌️