Platform Engineering & Kubernetes: Guardrails For AI Code

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

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 experimentation in the current "Cambrian explosion" of AI tooling. Berrelleza highlights the move from single-threaded AI tools to large, asynchronous AI agent fleets, which solves the bottleneck of waiting for a single AI response. This agentic model is a game-changer, with some early adopters seeing a massive increase in output. Organizations need to adapt for AI-native workflows, because the focus on traditional metrics like measuring code production (lines of code, number of PRs) for AI is flawed. Instead, organizations should identify and focus their AI projects on their real constraints, such as slow CI workflows. Ramiro also addresses the disproportionate challenge of open source maintainer overload caused by AI-generated contributions, proposing a policy of "human-proof code." Finally, AI agents are presented as a powerful technical context multiplier for everyone from sales engineers to the CEO, significantly speeding up the onboarding process and improving communication across the organization.  Read the blog post:  Takeaways ✓ The future is moving from single-threaded AI tools to "AI agent fleets" to solve productivity bottlenecks. ✓ Traditional metrics like lines of code or PR count are now ineffective for measuring AI-driven developer productivity. ✓ The new focus for AI investment should be on organizational bottlenecks, such as optimizing slow CI workflows. ✓ Open source projects should adopt policies like "human-proof code" to manage maintainer overload from AI contributions. ✓ AI agents can serve as a technical context multiplier, speeding up onboarding and improving organization-wide understanding of complex code. Hit the like button, subscribe for more content on platform engineering and AI, and ring the notification bell.  What is the biggest productivity bottleneck you've solved with AI agents? Let us know in the comments! #AIAgentFleets #PlatformEngineering #DeveloperProductivity #Kubernetes #KubeCon #Okteto #AgenticAI #OpenSource #SoftwareDevelopment #TechTrends