platform engineering
Why One of Two Kubernetes Pods Missed a Secret That Already Existed
A Kubernetes Secret can look correct while running pods still hold different environments. This case shows why CSI sync timing and per-pod verification matter ...
Why CPU-Based Autoscaling Fails for Rails — and What We Used Instead
Kubernetes autoscaling works best when teams match the metric to the workload: queue latency for synchronous web traffic, queue depth for background jobs ...
Write Access Is the Easy Part: The Verification Gap in Agentic Kubernetes Remediation
Giving an AI agent the power to change a cluster is now straightforward. Confirming the change landed, did not produce unintended duplicate effects and achieved the outcome the operator actually wanted is ...
K8sGPT and the Guardrails for AI-Assisted Kubernetes Troubleshooting
A practical way for platform teams to use AI for faster Kubernetes triage without giving agents unsafe control of the cluster. The first time an AI tool gets real cluster context, the ...
Kubernetes Did Not Miss the AI Wave. It Absorbed It
Enterprise AI is settling onto the cloud native stack, and the strongest evidence is not a vendor roadmap. It is what the ecosystem has already standardized ...
Manifestly Safer, Why Kubernetes Wants Developers to Speak KYAML
KYAML gives Kubernetes developers a stricter, more predictable YAML dialect designed to reduce configuration errors, ambiguity, indentation problems and unexpected type coercion ...
Containers Became the Unit of Speed. AI Agents Are Making VMs the Unit of Trust
The container is not disappearing. But as autonomous agents generate code, install packages and invoke tools, cloud-native infrastructure is placing a VM-grade security boundary around it ...
Autoscaling AI Workloads on Kubernetes With KEDA and What it Means for Agentic Systems
KEDA can scale Kubernetes AI workloads on real demand signals such as queue depth, helping model-serving and agent workloads respond faster while reducing idle compute costs ...
Cloud-Native Complexity Is a Cost: When More Platform Layers Stop Adding Value
Cloud-native environments rarely become complex overnight. In most teams, complexity builds gradually. A platform may begin with containers and a basic deployment process, then grow to include orchestration, CI/CD, observability, security controls, ...
Kubeflow’s Graduation Is a Vote for Kubernetes as the AI Control Plane
Kubeflow’s CNCF graduation signals growing confidence in Kubernetes as a common control plane for production AI workloads, from training and pipelines to governance and inference ...
