AI is part of the normal writing workflow for this blog. In a fast-moving technical field, assume that every article uses AI at some point, whether for drafting, editing, structuring, diagrams, translation, consistency checks, or proofreading.
The source material remains my own work: lab configurations, implementation notes, architecture decisions, dictated thoughts, field experience, and product analysis. I often build the labs and examples first, then use AI to turn that material into readable documentation or blog posts. AI helps with writing; it does not replace the work, judgment, or editorial responsibility behind the publication.
For blog posts and pages, the EU “Partially AI-Modified” icon and the visible disclosure near the start of the content mean:
the publication is AI-assisted or AI-reviewed
the publication has human editorial review
Constantin Korte assumes responsibility for the published text
The site does not intentionally publish deceptive AI impersonations or unlabelled deepfakes. If audio, video, images, or text are fully AI-generated or materially modified with AI in a way that could affect interpretation, the content should be labelled close to where readers encounter it.
This policy is based on the EU AI Act Article 50 transparency direction and the European Commission Code of Practice on Transparency of AI-Generated Content:
Partially AI-modified / AI transparency: This publication is AI-assisted and human-reviewed. Constantin Korte assumes editorial responsibility. See AI Transparency.
TL;DR
Constantin works on Kubernetes, VMware Cloud Foundation, private cloud operating models, and agentic engineering workflows that turn infrastructure into something teams can actually use.
I work on Kubernetes, VMware Cloud Foundation, private cloud architecture and the operating models that make platforms useful in real enterprises.
At Broadcom, my work sits between strategic customers, field architecture, product engineering, platform adoption and executive architecture conversations. I help large organizations turn infrastructure into something teams can actually consume: governed, automated, observable, secure and repeatable.
What I do
My focus is vSphere Kubernetes Service (VKS), VMware Cloud Foundation, Kubernetes, platform engineering and private cloud modernization. The practical work is rarely only about one product. It is about connecting developer workflows, Kubernetes platforms, automation, virtualization, storage, networking, security, observability and lifecycle management into an operating model that survives contact with real organizations.
I spend a lot of time translating between classic infrastructure teams, application teams, platform teams and product engineering. Good platform work needs all of those perspectives in the same room.
How I work
I like the messy middle: strategy meeting implementation, product direction meeting customer reality, and modern application ambition meeting infrastructure constraints.
A large part of the job is feedback flow. Field reality has to become product signal: adoption blockers, architecture patterns, competitive signals, lifecycle pain, security requirements and the small details that decide whether a platform is pleasant to operate or quietly avoided.
I also use agentic workflows heavily for research, architecture analysis, customer preparation, competitive positioning, content work, enablement, product feedback synthesis and engineering productivity. I care less about the hype and more about the operating model: context, rules, tooling, proof, documentation and iteration control.
Editorial note on AI
AI is part of the normal writing workflow for this blog. The source material remains my own work: lab configurations, implementation notes, architecture decisions, dictated thoughts, field experience and product analysis. I often use AI to turn that material into readable documentation and blog posts, then review and publish under my own editorial responsibility.
Platform engineering: automation, lifecycle, multi-tenancy, observability, security and day-2 operations.
Field and product bridge: turning customer reality into roadmap-relevant technical feedback.
Enablement: architecture narratives, field communities, peer learning and customer-relevant platform adoption.
Hands-on work: Shell, Python, GitOps, VCF Automation, Argo CD, Docker and the surrounding platform toolchain.
Along the way I picked up a few badges such as CKA, VCP-DCV/NV and VCAP-CMA. Certifications are useful; shipping reliable platforms people can operate is the part that matters.
LinkedIn is the best place for current role context, professional background and posts around private cloud, VCF, VKS, platform engineering and agentic work.
AI transparency: Platform Chronicles is published under human editorial responsibility by Constantin Korte.
