AI-Powered GitOps and Platform Engineering Workshop

AI is rapidly becoming part of the platform engineering toolkit, but many teams struggle to move beyond experimentation into meaningful adoption. The challenge is rarely the model itself it is ensuring that agents have access to accurate, current, and relevant operational knowledge.

In this hands-on workshop, attendees will explore where AI delivers measurable value in GitOps and platform engineering environments, how knowledge freshness directly impacts result quality and cost, and how to integrate AI-assisted workflows into deployment, validation, and review processes without sacrificing engineering judgment.

Through demonstrations and guided labs, participants will work with AI-assisted GitOps workflows, operational automation patterns, and context engineering techniques that can be applied immediately within production platform teams.

By the end of the workshop, attendees will understand how to identify suitable AI use cases, structure knowledge for effective agent interactions, implement AI-assisted validation processes, and convert repeated agent tasks into durable team-owned tooling.

Who this Workshop is for?

This workshop is designed for intermediate-to-advanced practitioners who already work with Kubernetes and GitOps in production or near-production environments.

Typical attendees include:

• Platform Engineers
• DevOps Engineers
• Infrastructure Engineers
• Site Reliability Engineers (SREs)
• Technical Leads
• Platform Architects
• Engineering Managers evaluating AI adoption strategies

The workshop is particularly valuable for:

• Individual contributors seeking practical techniques for using AI agents in infrastructure and operations workflows.

• Technical leaders evaluating where AI can generate sustainable value and where traditional approaches remain more effective.

This workshop is not intended for Kubernetes beginners or audiences seeking a conceptual overview of AI without hands-on implementation experience.

What you will learn

By the end of this workshop, attendees will be able to:

• Evaluate platform engineering workflows to determine where AI can provide meaningful value.
• Understand the relationship between knowledge freshness, context quality, response accuracy, and token consumption.
• Design context engineering approaches that improve AI effectiveness in operational environments.
• Implement AI-assisted validation and review processes within GitOps workflows.
• Use AI techniques to support deployment validation, drift detection, and infrastructure change reviews.
• Incorporate operational knowledge, runbooks, and incident history into AI-assisted workflows.
• Transform repetitive AI interactions into reusable internal tooling and automation.

Prerequisites

Attendees should have:

• Working knowledge of Kubernetes fundamentals
• Experience using GitOps tools such as Argo CD or Flux
• Familiarity with Git-based workflows
• Understanding of CI/CD practices
• Ability to read and interpret YAML manifests
• Experience with Helm or Kustomize

For hands-on activities, attendees should have:

• Git installed
• kubectl installed
• A local Kubernetes environment (kind, minikube, or k3d)
• Access to the sample repository provided before the workshop
• Required AI tooling access (details to be confirmed)

Key Takeaways from this Workshop:

• Distinguish between AI use cases that deliver value and those that do not.

• Improve AI effectiveness through context engineering and knowledge freshness strategies.

• Implement AI-assisted GitOps validation and review workflows.

• Incorporate AI into operational processes while maintaining human judgment.

• Build reusable tooling from successful AI-assisted workflows.