Build and validate a governed AI-native developer platform with Claude, Kubernetes, GitOps, policy controls, observability, and reproducible workflows. Key Features Control Claude with specifications, permissions, tests, and auditable workflows Build governed agent and model infrastructure on Kubernetes with GitOps Create a Backstage self-service path from developer request to traced agent Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionBuild agentic DevOps workflows without bypassing the controls your Kubernetes platform already depends on. This book shows you how to use Claude as a controlled platform-engineering worker while introducing agents, model serving, and developer self-service through reproducible GitOps workflows, explicit trust boundaries, policy checks, and testable completion gates. You’ll establish a cloud-native foundation with Argo CD, cert-manager, OpenBao, External Secrets Operator, Kyverno, Prometheus, Grafana, Loki, Tempo, and OpenTelemetry. You’ll then add governed AI traffic using Gateway API, kgateway, agentgateway, kagent, MCP tools, and LLM Guard before serving an OpenAI-compatible model with KServe and vLLM. The hands-on approach shows you how to constrain Claude with specifications, permissions, audit hooks, tests, and Git checkpoints. You’ll trace agent and model activity, diagnose failures from evidence, and turn operational fixes into reusable tests. You’ll also build a Backstage template and Argo CD ApplicationSet that provide a governed path from developer request to running agent. By the end of the book, you’ll be able to build and validate an AI-native internal developer platform in phases, route agent and model activity through existing platform controls, and prepare the architecture for production use.What you will learn Control Claude with specifications, permissions, and test gates Design an AI-native IDP with clear ownership and trust boundaries Build a reproducible Kubernetes foundation with A
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