As we’ve found, writing AI-powered software is the easy part. Testing it, securing it, operating it at enterprise scale - that’s where things get interesting. “Guardrails,” all that.
Purnima lays this out: a shift from deterministic systems (input goes in, predictable output comes out) to probabilistic ones where agents wander around exploring multiple paths to get stuff done.
Sure, there’s all the freaking out about deleting data, bringing down production, exposing your precious secrets.
Posts in "Tanzu"
Securing AI
More “how do we secure this AI stuff” talk with David Zendzian on today’s live stream. He’s recently gone Claude Crazy so I wanted to get his CISO-supremo talk on thinking through the risk management for AI in enterprises. Each time I tried to come up with a problem, he was good finding the fix. Plus, we talk about some of the things we’ve learned about using our little robot buddies.
MCP Security Guide
My pal Adib Saikali wrote up an MCP security guide covering how to think about securing MCP servers in the enterprise (no lead-generation required, just a straight-up PDF download). It gets into access tiers (open, group, and user-level servers), authentication with OAuth 2.1, identity propagation models (when to use service accounts vs. forwarding user identity), and how an MCP gateway gives you a governed chokepoint for auth, observability, and capability filtering.
VMware/Broadcom at KubeCon EU 2026
Here’s Claude’s take on VMware’s stuff at KubeCon - just some light editing for me.
KubeCon + CloudNativeCon Europe 2026 ran March 23-26 in Amsterdam. Here’s what VMware by Broadcom announced.
VKS 3.6 Ships The VKS stack as seen at VMUG Connect Amsterdam 2026. VMware vSphere Kubernetes Service 3.6 shipped with Kubernetes 1.35 support, RHEL 9 compatibility, declarative performance tuning, and improved upgrade safety targeting enterprise platform teams. The day-two operations framing is the key story - VKS 3.
Does Platform Product Management & Design Really Happen? Or is it all just platform engineering? - Tanzu Catsup
Most organizations treat infrastructure as a series of projects to be “completed,” but successful platform engineering requires a permanent product mindset. In this episode, we explore why platform teams need dedicated product management to balance competing priorities—like security, cost, and developer experience—and why the “why” scales much better than the “what” in large enterprises. We also dive into the often-overlooked role of designers in creating platform tools that developers actually want to use.
Developers crave AI tools for various tasks beyond coding, but that’s only about 20% of their work. But, ops people freak out about security and control challenges, like cost, regulatory compliance, and usage tracking.
Bad advice from Wall Street on enterprise AI.
Why it's great to be a Spring developer now, and how to make it even better - State of Spring, 2026
This is a talk I give at the start of Spring workshops we do. Here is the recording. The point is to show people that being a Java and Spring developers is fantastic right now. Here’s the description:
Spring developers are in a strange position in 2026: everything is changing: AI, platform engineering, enterprise architecture. And yet Spring keeps getting stronger.
In this talk, Coté walks through why this is actually a great moment to be a Spring developer, especially in large organizations.
Your Boss Doesn’t Know What to Do With AI
Enterprise AI Has a Product-Market Fit Problem. Enterprise AI isn’t stalled because the models are weak. It’s stalled because we haven’t discovered product-market fit inside the enterprise yet.
You don’t find real AI value by theorizing in workshops. You find it by running experiments for months inside your actual systems - against real data - in a governed environment.
That requires a platform.
Without one, AI pilots turn into disconnected experiments, shadow infrastructure, and compliance risk. With one, experimentation compounds into institutional learning.
In this video, I break down:
- Why enterprise AI is still in discovery mode
- Why experimentation must be long-running, not one-off
- How governance enables innovation instead of blocking it
- Why a secure platform foundation is the baseline for AI ROI
If you’re thinking about AI strategy, platform engineering, or how to make AI experimentation safe and scalable, this is where to start.
Featured:
Enterprise AI needs new apps, enterprise AI doesn't need new platforms
Enterprises should focus on resilience and learning in AI projects by experimenting with applications rather than building new infrastructure.