Spotlight

The Four-Layer Stack for Running Agentic AI on Kubernetes

Dave R - Microsoft Azure & AI MVP☁️

This article explains how to deploy agentic AI on Kubernetes using a four-layer architectural stack.

It details model inference, fine-tuning, and agent orchestration using tools like KAITO, Ray, and MCP.

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Why Kubernetes Needs to Learn GPUs
Why Kubernetes Needs to Learn GPUs

Kube Signals starts where the keynote ends: with the trends that platform teams will have to operationalize next.

In this special episode, Brian Teller speaks with Saiyam Pathak about his KubeCon India keynote and the shift from developer platforms to AI factories. They examine what GPU scarcity, shared accelerators, and AI workloads mean after the conference slides meet real infrastructure.

In this interview:

  • Why GPU infrastructure is becoming a platform-engineering concern
  • How DRA, HAMI, MIG, and MPS change GPU allocation and utilization
  • Where isolation, scheduling, and observability become harder for AI platforms
  • Which cloud-native AI trends and projects platform engineers need to watch

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    • Salary: $37 to $485.65K a year

    • Location: based in the office in Arlington, VA, USA

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