Why Testing and Validation are the Unsolved AI Code Challenges

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 workloads instead of asking teams to adopt new tools. He and podcast host William Morgan dig into who should own testing (developers own functional tests,  while the platform team owns performance, security, and chaos testing). Lensmar's core recommendation is to treat testing and quality as a platform capability, the same way you already treat CI and CD.  More AI-generated code means more tests are needed, intelligent test selection can keep pipelines fast as test counts grow, and AI is already useful for triaging failed test logs using Kubernetes and Grafana MCP servers.   TAKEAWAYS:  ✓ Where testing ownership splits between developers and the platform team, and where it doesn't  ✓ Why "testing as a platform service" should get the same priority as CI and CD  ✓ How intelligent test selection works, and why you should never rely on it alone  ✓ Where AI already adds value today: triaging failed test logs with Kubernetes and Grafana MCP servers  ✓ Why testing the AI components of your application (agents, evals) is becoming its own discipline. Read the blog post at:

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 [https://www.imaginelearning.com/] is a K-12 education company building digital platforms for students and educators, and Blake has been driving AI and platform engineering initiatives there.

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.