
The AI Kubernetes Show
The AI Kubernetes Show explores the practical challenges of running AI workloads on Kubernetes platforms. Episodes dig into real-world adoption stories, covering architecture decisions, tooling and operational pitfalls. Typical topics include GPU scheduling, model serving, autoscaling and resource management. The show is aimed at platform engineers, DevOps teams and machine learning practitioners working in cloud-native environments.
Episodes

Is It Easier to Write a Vulnerability Than to Find One?
Static analysis tools used to be right about as often as a coin flip. Kathleen Goeschel, Principal Product Security Engineer at Red Hat, spent years using machine learning to fix that, and now she's watching large language models change both sides of the fight. How fast vulnerable code gets written, and how fast it gets found.Goeschel walks through how she trained machine learning models on ag

Durable Execution for AI Agents in Kubernetes
If your AI agent restarts and loses everything it was doing, your platform team is stuck retrofitting recovery logic Kubernetes was never built for. Mark Fussell, CEO of Diagrid and creator of the Dapr project, joins the AI Kubernetes Show to explain why that's the real infrastructure problem in the agentic AI era, not the model.Fussell built the platform running Azure SQL Server at scale duri

Matt Barker (BoltMCP) on SPIFFE, AI Agent Identity, and Kubernetes
AI-generated code is 40% more likely to leak a secret. Matt Barker, co-founder of the company that built cert-manager and later led secure workload access at CyberArk, explains why SPIFFE might finally fix workload identity for AI agents.Matt and William Morgan cover why SPIFFE replaces long-lived secrets with short-lived, cryptographic workload identities, what Matt is building at BoltMCP to cont

Disaggregated Inference on Kubernetes
Every LLM inference request splits into two phases with completely different hardware profiles: pre-fill and decode. But most Kubernetes clusters still run them on the same pod. Morgan Foster, who works on the llm-d project and the AI Gateway Working Group out of Red Hat's Office of the CTO, joins William Morgan to explain what that means. Foster and Morgan get into why real-time inference wor

The State of Cloud Native AI and What’s Still Evolving
There is no agreed upon definition of what cloud native AI means yet, but if you're trying to figure out where Kubernetes AI standards are really headed, check out this episode with someone who's worked on the CNCF's first attempt to define cloud native AI. William Morgan talks with Ron Petty (RXM, CNCF founding contributor, and member of the CNCF's TCG AI) about what's actuall

Treat Testing as a Platform Service on Kubernetes
Most testing tools bolt onto a CI pipeline and hope for the best. Ole Lensmar, CTO of Testkube, built a company that changes that script. Pull test execution out of CI/CD entirely and run tests as Kubernetes workloads instead.In this episode, Lensmar walks through why Testkube uses Kubernetes as its test execution engine, running Selenium, Playwright, K6, JMeter, and Postman tests as cluster workl

LLM Inference on Kubernetes: New Primitives, Real Challenges
Running LLM inference on Kubernetes requires new primitives for routing, autoscaling, and GPU scheduling. Here's what platform engineers need to know.

Running Multi-agent AI on Kubernetes: Lessons from Imagine Learning
In this episode of The AI Kubernetes Show, Blake Romano, Staff Software Engineer at Imagine Learning, walks through what it actually looks like to build and run AI agents on Kubernetes at scale. He talks about the architecture choices, the failures, and why the organizational context you bring to the LLM matters more than which Software Development Kit (SDK) you use.Imagine Learning is a K-12 educ

AI Agents Security: Guardrails & Production on Kubernetes
Learn platform-level security patterns for AI agents on Kubernetes. Close the production gap with LLM guardrails, tool filtering, and short-lived tokens.

Platform Engineering & Kubernetes: Guardrails For AI Code
Learn how Schonfeld scaled their internal AI platform, SchonAI, using Kubernetes and established guardrails to manage AI agent code volume. Build your AI-native workflow now.

One Dependency Away: Supply Chain Security in the Age of AI
Secure your Kubernetes environment. Learn why zero trust cybersecurity is the only defense against AI agents and non-deterministic agentic software in your supply chain.

Moving from Single Agents to AI Agent Fleets
The future of software development isn't about single agents—it's about building AI agent fleets! Dive into this conversation with Okteto CEO Ramiro Berrelleza to understand how this shift is fundamentally changing platform engineering and accelerating developer productivity. In this episode of The AI Kubernetes Show, we sat down with Ramiro to discuss AI adoption and the need for constant

Why Testing and Validation are the Unsolved AI Code Challenges
Is your engineering org ready for the speed of AI? Grant Miller, CEO of Replicated, breaks down the intersection of AI and platform engineering, revealing why testing and validation are the biggest unsolved problems in the industry.In this episode of The AI Kubernetes Show, we sit down with Replicated CEO Grant Miller to discuss how the pace of AI is fundamentally reshaping software development. M

AI: Bubble or Bug? A CTO’s Perspective on Engineering in the AI Era
Is the AI boom a bubble, or is it a new technological wave? Dinesh Majrekar, CTO of Civo, breaks down the current state of software development, explains why data sovereignty is the paramount security concern, and details how AI's real value lies in increasing code auality, not just velocity.In this episode of The AI Kubernetes Show, Civo CTO Dinesh Majrekar tackles the AI bubble hype, suggest

Maintaining DevOps Integrity in the Age of AI Velocity
Is AI velocity breaking your DevOps processes? We sat down with Principal Platform Engineer Ahmed Bebars to discuss the critical balance of using AI to ship code faster while maintaining DevOps integrity in your platform engineering team.Ahmed Bebars, a principal platform engineer and CNCF ambassador, breaks down the AI's impact on SDLC and the transformation of platform engineering. In this T

AI's Double-Edged Sword: The Technical Imperative and the Path to Accessibility
Discover the dual nature of AI! Tech Lead Chris Khanoyan shares his view on the rapidly changing AI and data science landscape and the critical need for a technical foundation and the transformative power of AI accessibility for the deaf community. In this episode of The AI Kubernetes Show, we dive deep into the world of AI and data science with Chris Khanoyan, a tech lead and senior data scientis

The AI Tug-of-War: Bridging the Divide Between Platform Engineering and Data Science
Keith Maddox, co-lead of the Kubernetes AI Working Group, breaks down the architectural shifts and security challenges required to run enterprise AI agents at scale.In this The Kubernetes AI Show episode, we chat with Keith Maddox, senior principal software engineer lead at Microsoft and Istio maintainer, who shares his perspective on the convergence of data science, AI agents, and platform engine

Platform Engineering, AI, and the Two Faces of Cloud Native Sustainability
KubeCon 2025 proved that platform engineering is essential, but the next big challenge for the cloud native community is sustainability. Learn from KubeCon Co-Chair Faseela what this future looks like and what it means for your platform.In this episode of The AI Kubernetes Show, we break down the key KubeCon NA 2025 takeaways with co-chair Faseela, a cloud native developer at Ericsson. She discuss

Stop Bolting on Security: The Key to Reliable AI Agent Systems
Is your AI infrastructure safe? Marina Moore, research scientist and co-chair of CNCF Tag Security, talks about her research on AI agent isolation and how to build robust platform engineering security. Build it securely from the start!In this must-watch episode of The AI Kubernetes Show, we sit down with security expert Marina Moore to discuss the paradigm shift in AI-driven systems security. Moor

Agentic AI Security on Kubernetes: Understanding Payload Routing and Defense-in-Depth
Is your Kubernetes cluster ready for the next generation of AI workloads? Join Buoyant Tech Evangelist Flynn and Red Hat's Shane Utt from the AI Gateway Working Group as they reveal the critical shift from header-based to payload-based Kubernetes Networking for Agentic AI.In this episode of The Kubernetes AI Show, we dive deep into the challenges of running AI workloads on cloud native infrast

The Engineering Mindset for AI: From Automation to Guardrails
Is your organization ready for the rapid pace of AI adoption? We sat down with Rob Koch (Slalom Build, CNCF Deaf and Hard of Hearing Working Group Co-Chair) to discuss how engineers can master automation in the age of non-deterministic systems, focusing on real-world use cases in Kubernetes and platform engineering.In this episode of the AI Kubernetes Show, Koch provides essential insights on buil

Comcast's Platform Engineering: Guardrails and Scale in the Age of AI
The rise of AI has dramatically increased code velocity. Join us as Comcast Platform DevOps Engineer Curtis Cook breaks down the shift to platform-level controls and a "zero trust" mindset to secure the future of Platform Engineering. In this episode of the AI Kubernetes show, Curtis Cook shares his expert insights on how the accelerating pace of code development driven by AI tooling is

How Linkerd is Adapting for Stateful AI Workloads
The future of the service mesh is here! Don't miss this deep dive with Linkerd creator Oliver Gould on how to conquer the toughest challenges of running stateful AI workloads in production.In this episode of the AI Kubernetes Show, Buoyant CTO Oliver Gould reveals how Linkerd is adapting its battle-tested features to the new demands of the AI workloads era. He emphasizes that the cost of AI in

Navigating the AI Era at Bloomberg
Don't miss this deep dive with Bloomberg's Alexa Griffith on how a financial firm is navigating the cultural and technological shift to Generative AI. Learn the critical role of platform engineering and Kubernetes in building a secure, scalable, and future-proof AI infrastructure.In this episode of the AI Kubernetes Show, Senior Software Engineer Alexa Griffith discusses Bloomberg's jo

Enterprise, Open Source, and AI: A Guide to Thriving at the Intersection
In this episode of The AI Kubernetes Show, we talked with Troy Connor, Senior Software Engineer at Microsoft and a maintainer of Porter and Kubernetes controller-runtime maintainer (two CNCF projects), about the balance between enterprise work and open source contribution and the impact of AI on software development and community maintenance. This post summarizes our discussion, live from KubeCon

The Overdelegation Trap: The Call for AI Training, DevX, and Creativity
On this episode of The AI Kubernetes Show, we chatted with Diana Todea, Developer Experience Engineer at Victoria Metrics and Co-chair for the CNCF Neurodiversity Working Group. This post summarizes our discussion, which covered the impact of AI, generational shifts, and the role of Developer Experience (DevX).

OpenSSF CTO on AI, Open Source Security, and the Human Element
In this AI Kubernetes Show episode, we chat with OpenSSF CTO and Linux Foundation Chief Security Architect Christopher Robinson, or CRob, as everyone calls him. We explore the potential and the inherent risks of integrating AI into the open source security landscape.

Platform Engineering's AI and Observability Playbook
Learn how structured observability data and the platform team enable fast AI troubleshooting. Balance AI automation with essential developer craftsmanship.

Will Prompt Engineering Be the Next Programming Language?
Learn how prompt engineering and a cutting-edge multi-agent workflow pipeline are reshaping software dev. Get the framework for safe AI integration.

WebAssembly, Fuzz Testing, and the Agentic Future
Bailey Hayes discusses how AI is reshaping platform engineering, covering WebAssembly (Wasm) for scale and security, agentic workloads, non-deterministic programming, and essential API fuzz testing.
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