Training course · Cursor · Kubernetes & Microservices

Cursor for Kubernetes and Microservices Teams

Work productively with the AI code editor Cursor: agent mode, rules, codebase context, Tab completion, background agents and review.

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

“Cursor 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 Cursor in the abstract, you work on tasks from your daily routine – with examples, exercises and templates from Kubernetes & Microservices.

Cursor is an AI code editor built on VS Code that weaves language models deeply into everyday development. Alongside context-aware Tab completion, it offers an agent mode that searches files on its own, plans changes across the whole project, runs terminal commands and fixes errors iteratively. In this two-day course you will learn to use Cursor with purpose: choosing the right interaction mode, steering context deliberately and combining AI assistance with your existing workflow of version control, automated testing and peer code review.

A key focus is making Cursor work for teams. Project rules let you capture architectural decisions, naming conventions and testing strategies so that the agent produces consistent results. You will learn to make good use of codebase indexing, targeted references to files, documentation and symbols, and MCP integrations. You will also delegate tasks to background agents, review their output as pull requests and set up a review process that takes quality, security and data protection seriously across the organisation.

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 choose the right Cursor mode for each task and use it efficiently.
  • You steer codebase context precisely and get more accurate suggestions as a result.
  • You write project rules that reliably enforce your team’s conventions.
  • You use agent mode and background agents for features and refactorings with verifiable 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 Setting up Cursor and understanding its modes
  • Installation and migration of VS Code settings and extensions
  • Differences between Tab completion, inline edits, Ask mode and Agent mode
  • Choosing a model per task: balancing speed, depth and cost
  • Privacy mode and organisation-level settings
Module 2 Steering codebase context
  • How codebase indexing works and where its limits lie
  • Referencing files, folders, symbols, documentation and web sources precisely
  • Using ignore files to exclude sensitive or irrelevant content
  • Avoiding overlong chats and restarting tasks with fresh context
Module 3 Rules for consistent team results
  • Creating and versioning project rules and scoping them by file pattern
  • Expressing architecture, style and testing conventions as effective rules
  • Separating user rules from team rules
  • Refining rules iteratively based on the agent’s typical mistakes
Module 4 Agent mode in practice
  • Building features step by step with plan, implementation and tests
  • Running multi-file refactorings safely with checkpoints
  • Approving terminal commands deliberately and feeding error output back
  • Connecting MCP servers for issue trackers, databases or browser testing

Day 2

Module 5 Background agents, review and governance
  • Delegating tasks to background agents and running them in parallel
  • Reviewing results as branches or pull requests and steering follow-ups
  • Using AI-assisted code review alongside human review
  • Addressing security risks, secrets and licensing in enterprise use
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 an IDE such as VS Code. Practical experience with containers, kubectl and core Kubernetes objects, plus a working knowledge of Helm, YAML and Git.

Technical requirements

Your own laptop with Cursor, Git and your usual development environment installed; a Cursor account with access to agent features is recommended.

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.

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Thu, 10 December 2026 – Fri, 11 December 2026 Stuttgart Request
  • Wed, 13 January 2027 – Thu, 14 January 2027 Online Request
  • Thu, 28 January 2027 – Fri, 29 January 2027 Berlin Request
  • Thu, 11 February 2027 – Fri, 12 February 2027 Online Request
  • Thu, 18 February 2027 – Fri, 19 February 2027 Munich Request

Frequently asked questions

Do I need prior VS Code experience to use Cursor?

It helps, but it is not essential. Cursor is built on VS Code, so navigation, shortcuts and extensions feel familiar and existing settings can be imported. If you currently work with JetBrains IDEs or other editors, a short introduction during the course gets you up to speed quickly so you can concentrate on the AI features.

How do I prevent confidential code from reaching third parties?

Cursor offers a privacy mode and organisation settings that restrict how code is stored by the provider. You can also use ignore files to exclude sensitive areas from indexing and context. In the course we discuss how to align these options with internal policies, data protection requirements and sound handling of secrets.

What do rules add compared with well-written prompts?

A prompt applies to one request only, whereas rules persist and apply to the whole team. They capture architectural decisions, preferred libraries, testing strategies and style guidelines, so nobody has to repeat that information and the agent produces code that fits your codebase. Rules are versioned with the project and evolve together with it.

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 “Cursor 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.

More courses for Kubernetes and Microservices Teams

More Cursor courses

All 32 Cursor courses

Cursor · MunichCursor · SalzburgCursor · BerlinCursor · AugsburgCursor · HanoverCursor · BremenCursor · StuttgartCursor · KaiserslauternCursor · Online