Training course · Windsurf · Kubernetes & Microservices

Windsurf for Kubernetes and Microservices Teams

Develop with the Windsurf AI IDE and its Cascade agent: flows, memories, rules, workflows and safe use across the team.

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

“Windsurf for Kubernetes and Microservices Teams” is a hands-on course for roles such as Platform engineers and Kubernetes administrators, Developers running microservices on Kubernetes and Site reliability engineers with cluster responsibility. Rather than learning Windsurf in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Kubernetes & Microservices.

Windsurf is an AI-powered development environment built around its agent, Cascade. Cascade understands the structure of your project, follows your actions in the editor and terminal, and works with you according to the flow principle: you and the agent take turns on the same task without having to re-explain context over and over. In this two-day course you will learn how to use Cascade effectively for features, debugging and refactoring, and how to make the most of intelligent completion in your daily development work.

To keep results consistent across a team, you steer Cascade with global and workspace rules, rely on memories for lasting project knowledge and automate recurring procedures with workflows that run from a slash command. You will connect external tools through MCP, control how terminal commands are executed and decide which data Cascade is allowed to see. Finally, you will define rules for code review, security and data protection so that Windsurf can be used responsibly within your organisation and its existing processes.

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 Cascade productively in flow for features, debugging and refactoring.
  • You steer Windsurf’s context precisely and keep sensitive content protected.
  • You maintain memories and rules that reliably enforce your project conventions.
  • You automate recurring procedures with workflows and connect external tools via MCP.
  • 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 Getting to know Windsurf and Cascade
  • Installation, importing existing editor settings and basic configuration
  • Cascade in code and chat mode: when the agent edits and when it only advises
  • The flow principle: how Cascade takes your editor and terminal actions into account
  • Intelligent completion and inline commands in everyday development
Module 2 Steering context
  • Understanding codebase indexing and referencing relevant files or directories
  • Bringing documentation, web sources and terminal output into context
  • Excluding sensitive content from context with ignore files
  • Structuring sessions and starting afresh when the topic changes
Module 3 Memories and rules for consistent results
  • Reviewing and maintaining auto-generated and manually created memories
  • Creating and versioning global and workspace rules
  • Expressing architecture, style and testing guidelines as effective rules
  • Activating rules selectively by file pattern or occasion
Module 4 Workflows and automation
  • Defining recurring procedures as workflows and launching them via slash command
  • Building workflows for release preparation, testing or code maintenance
  • Connecting MCP servers for issue trackers, databases or internal services
  • Controlling terminal command execution with allow and deny lists

Day 2

Module 5 Quality, review and governance
  • Tracing Cascade’s changes, accepting them partially or reverting them
  • Using a test-driven approach and preview features for verification
  • Team settings, data protection options and handling of secrets
  • Defining review processes and accountability for AI-generated code
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 program regularly in at least one language and are familiar with Git and a modern IDE. Practical experience with containers, kubectl and core Kubernetes objects, plus a working knowledge of Helm, YAML and Git.

Technical requirements

Your own laptop with the Windsurf editor, Git and your usual toolchain installed; a Windsurf account with access to Cascade is required.

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 Salzburg Request
  • Wed, 16 December 2026 – Thu, 17 December 2026 Online Request
  • Wed, 20 January 2027 – Thu, 21 January 2027 Bremen Request
  • Wed, 27 January 2027 – Thu, 28 January 2027 Online Request
  • Tue, 2 February 2027 – Wed, 3 February 2027 Augsburg Request

Frequently asked questions

What does Windsurf mean by flows?

Flows describe the close collaboration between you and Cascade. The agent observes what you do in the editor and terminal and takes those actions into account in its next steps. As a result you do not have to keep explaining where you are, and you can move smoothly between doing the work yourself and delegating it to the agent.

How do memories, rules and workflows differ?

Memories store knowledge that Cascade has gathered while working or that you have explicitly recorded. Rules are guidelines you write that shape the agent’s behaviour permanently. Workflows are reusable sequences of steps for recurring tasks, launched with a slash command. Together they keep results consistent across the whole team.

Can I use Windsurf with my existing projects and languages?

Yes. Windsurf is language-agnostic and works with your existing toolchain, including build tools, package managers and test frameworks. Whether you work on back end, front end, data processing or infrastructure code, what matters is giving Cascade good rules and clear context. Each course variant adds the specific focus for your field.

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 “Windsurf 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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