Training course · Claude Code · Kubernetes & Microservices

Claude Code for Kubernetes and Microservices Teams

Agentic coding with Claude Code in the terminal and IDE: CLAUDE.md, subagents, hooks, MCP, CI pipelines and secure permissions.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

“Claude Code for Kubernetes and Microservices Teams” is designed for roles such as Platform engineers and Kubernetes administrators, Developers running microservices on Kubernetes and Site reliability engineers with cluster responsibility and shows step by step how to use Claude Code productively, safely and transparently in Kubernetes & Microservices – from the fundamentals to advanced workflows.

Claude Code is Anthropic’s agentic coding assistant, working directly in your terminal and in popular IDEs. Rather than completing individual lines, it reads your repository, plans changes across multiple files, runs commands, executes tests and prepares commits. Over two days you will learn to use it with deliberate control: clear project instructions in CLAUDE.md, careful context management, and a workflow in which you set the direction as a developer and can trace every change the agent makes back to a clear intention.

The course is built around real engineering work: diagnosing bugs, delivering features, refactoring legacy code and filling gaps in test coverage. You will configure subagents for specialised tasks, enforce quality checks with hooks, connect external systems through the Model Context Protocol (MCP) and capture team knowledge as reusable skills. Finally, you will run Claude Code headless in CI pipelines and define permissions, sandboxing and approval rules so that its use remains defensible even in security-sensitive environments.

Kubernetes has become the standard platform for microservices – and with it comes a flood of YAML manifests, Helm charts, Kustomize overlays, operators, service mesh configuration and GitOps repositories. On top of that, failures are distributed across dozens of services. AI coding assistants and agents take a lot of that complexity off your hands: they generate Helm charts with sensible defaults, diagnose CrashLoopBackOff and OOMKilled errors from events and logs, write NetworkPolicies and RBAC rules on the least-privilege principle and help define service boundaries. At the same time, an agent with cluster access needs firm limits. This course teaches you to use AI for manifests, troubleshooting and architecture work without losing control of your clusters.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • Platform engineers and Kubernetes administrators
  • Developers running microservices on Kubernetes
  • Site reliability engineers with cluster responsibility
  • Software architects designing distributed systems
  • DevOps teams introducing or scaling GitOps

Typical challenges we address

  • Unwieldy amounts of YAML, Helm values and overlays across many environments
  • Difficult troubleshooting of distributed issues spanning services, networking and resource limits
  • Security gaps from overly broad RBAC, missing NetworkPolicies and privileged containers
  • Unclear service boundaries and tight coupling in growing microservice landscapes

What you will be able to do

  • You use Claude Code for multi-step development tasks while keeping full control over every change.
  • You maintain a CLAUDE.md that effectively conveys project conventions, commands and architectural guidance to the agent.
  • You extend Claude Code with subagents, hooks, MCP servers and skills tailored to your team.
  • You integrate Claude Code into Git workflows and CI pipelines with traceable, test-backed results.
  • Create and review Helm charts, overlays and policies efficiently with AI agents.
  • Diagnose issues in clusters and microservices faster with structured AI support.
  • Restrict agents’ cluster access and enforce security and architecture rules consistently.

Course outline

Day 1

Module 1 How Claude Code works
  • Installation, sign-in and use in the terminal, VS Code and JetBrains IDEs
  • Understanding the agentic loop: read, plan, act, verify
  • Using plan mode to agree on an approach before any code changes
  • Writing effective task briefs with goal, context and acceptance criteria
Module 2 Project context with CLAUDE.md and context management
  • Structuring CLAUDE.md at project, directory and user level
  • Documenting build, test and style conventions so the agent follows them reliably
  • Managing the context window with targeted file references, compaction and fresh sessions
  • Resuming sessions and breaking long tasks into manageable stages
Module 3 Extending Claude Code with subagents, hooks, MCP and skills
  • Defining subagents for code review, research or test generation
  • Using hooks to enforce formatting, linting or checks before and after tool calls
  • Connecting MCP servers for issue trackers, databases or internal documentation
  • Creating custom slash commands and skills for recurring team workflows
Module 4 Git workflows, testing and quality assurance
  • Creating branches, meaningful commits and pull requests with Claude Code
  • Test-first work: have tests written before the implementation
  • Reviewing diffs systematically and rejecting or refining changes
  • Organising parallel work with Git worktrees and multiple sessions

Day 2

Module 5 Headless operation, CI and security
  • Running Claude Code non-interactively in scripts and CI pipelines
  • Automated reviews and issue handling in GitHub or GitLab workflows
  • Configuring permission modes, allow and deny rules, and sandboxing
  • Handling secrets, prompt injection risks and organisation-wide policies
Module 6 Practice lab: Kubernetes & Microservices
  • Creating Helm charts and Kustomize overlays
  • Debugging pods and deployments
  • RBAC and NetworkPolicies
  • Service boundaries and communication
  • Setting up observability
  • Maintaining GitOps repositories
Module 7 Data protection, quality & transfer to daily work
  • Kubeconfigs, service account tokens and secrets must never end up in prompts, and agents should only work with read-only or tightly scoped rights in non-production clusters. Check container images and charts for provenance, signatures and vulnerabilities, and take into account the supply-chain and SBOM requirements of NIS2 and the Cyber Resilience Act.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Creating Helm charts and Kustomize overlays

The agent generates Helm charts or Kustomize overlays with probes, resource requests, pod disruption budgets and security contexts, validates them with helm lint and kubeconform and documents every value.

Debugging pods and deployments

From kubectl output, events and container logs, the AI narrows down causes of CrashLoopBackOff, OOMKilled, ImagePullBackOff or failing readiness probes and proposes concrete fixes. The team reviews every proposed fix before applying it.

RBAC and NetworkPolicies

The AI analyses existing roles and traffic flows, writes least-privilege RBAC rules and creates NetworkPolicies that allow only the traffic services genuinely need. Changes are tested in a non-production cluster first.

Service boundaries and communication

The assistant helps define service boundaries along business domains, weigh synchronous against asynchronous communication and implement patterns such as outbox, circuit breaker or saga properly.

Setting up observability

Services get OpenTelemetry instrumentation, Prometheus metrics, ServiceMonitors and alert rules, plus dashboards that make latency, error rate and saturation visible. Alerts are aligned with service level objectives so that only relevant incidents escalate.

Maintaining GitOps repositories

The AI structures repositories for Argo CD or Flux, separates environments cleanly, automates image updates and explains sync failures using status output and diffs. Drift between repository and cluster becomes visible.

Possible hands-on projects

Manifest linter with a rule set

A tool that checks Kubernetes manifests for missing limits, probes, labels and insecure settings and produces a report for pull requests.

Microservice demo with tracing

Two or three small services instrumented with OpenTelemetry, deployed via Helm to a local cluster, including a dashboard for distributed traces.

Cluster hygiene report

A CLI that finds unused ConfigMaps, orphaned PersistentVolumes and deployments without resource settings and suggests clean-up actions. Nothing is deleted without human approval.

Prerequisites

You have hands-on programming experience in at least one language and are comfortable with Git and the command line. Practical experience with containers, kubectl and core Kubernetes objects, plus a working knowledge of Helm, YAML and Git.

Technical requirements

Your own laptop with admin rights, Git, a Node.js runtime and an up-to-date editor; a Claude subscription or Anthropic API access is required for the exercises.

Data protection & compliance

Kubeconfigs, service account tokens and secrets must never end up in prompts, and agents should only work with read-only or tightly scoped rights in non-production clusters. Check container images and charts for provenance, signatures and vulnerabilities, and take into account the supply-chain and SBOM requirements of NIS2 and the Cyber Resilience Act.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Wed, 9 December 2026 – Thu, 10 December 2026 Bremen Request
  • Mon, 11 January 2027 – Tue, 12 January 2027 Online Request
  • Mon, 25 January 2027 – Tue, 26 January 2027 Salzburg Request
  • Wed, 10 February 2027 – Thu, 11 February 2027 Online Request
  • Thu, 18 February 2027 – Fri, 19 February 2027 Kaiserslautern Request

Frequently asked questions

How is Claude Code different from traditional code completion?

Code completion suggests the next few lines, whereas Claude Code acts as an agent that handles whole tasks: it searches the repository, plans changes across several files, runs tests and commands, and corrects itself based on the results. You define the goal and review the outcome instead of steering every single line yourself.

Is Claude Code secure enough for enterprise use?

By default, Claude Code asks for approval before editing files or running commands. Permission rules, sandboxing, managed settings and hooks let you define precisely what the agent may do. During the course you learn to configure these mechanisms to match your security and data protection requirements and to assess risks such as prompt injection.

Does Claude Code work with my language and tech stack?

Yes. Claude Code is language-agnostic because it works with files, terminal commands and your existing build and test tooling. Whether you use Python, Java, TypeScript, Go, SAP-related code or infrastructure as code, what matters most is a well-written CLAUDE.md describing your commands and conventions. Each course variant adds the relevant domain focus.

Should an AI agent have direct access to my cluster?

Only in a controlled way. For analysis and troubleshooting, read-only rights in selected namespaces are usually enough, while changes should go through GitOps repositories and reviews. On the course you set up dedicated service accounts, local test clusters and approval processes so the agent is useful without creating production risk.

Does the course also cover microservice architecture questions?

Yes. Beyond manifests and operations, we look at how AI can support defining service boundaries, weighing communication patterns and assessing architecture options. The AI provides options and arguments, while the decision stays with the team and is documented transparently. That keeps decisions accountable.

How much does “Claude Code for Kubernetes and Microservices Teams” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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