Running Multi-agent AI on Kubernetes: Lessons from Imagine Learning

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.

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.

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. Miller argues that engineering velocity has become the core competitive differentiator and shares the concept of "leadership empathy," where leaders contribute to a pull request with AI to understand the new tools. This increased velocity, however, puts significant system pressure on platform engineering teams, leading to "Frankenstein-y" application footprints and a greater need for top-notch observability and optimized CI/CD pipelines to improve "iteration speed total." The unique distribution challenges of self-hosted AI applications and the difficulty of validating AI code generation, especially for templated infrastructure-as-code like Helm charts and Terraform. Unlike front-end code, the human validation loop for infrastructure-as-code is not intuitive, making the complexity of testing and validation the industry's most significant hurdle. Read the blog post:  Takeaways ✓ AI turns engineering velocity into the ultimate competitive advantage, requiring organizations to move incredibly fast. ✓ Leaders must develop "leadership empathy" by using AI tools to understand the modern developer experience. ✓ Rapid AI code generation can lead to complex, "Frankenstein-y" application architectures, increasing pressure on platform engineering for troubleshooting and observability. ✓ The biggest challenge in AI-generated code is the lack of an intuitive validation loop for infrastructure-as-code like Helm charts. ✓ Testing and validation are the key unsolved problems and future areas for discovery and job creation. Liked this podcast? Hit the like button, subscribe for more AI and platform engineering insights, and let us know in the comments: What is the biggest challenge your team faces with AI-generated code? #AI #PlatformEngineering #EngineeringVelocity #AIGeneratedCode #TestingAndValidation #Kubernetes #Replicated #TechPodcast #CloudNative