Terraform Kubernetes Governance for AI Development Environments

Believe they said - but believe is not doing. I'm asking myself when we became the stooge of AI and Automation. The classical developement moves towards architecture, orchestration and promting (the abstraction layer?) and obviously some Dev-Ops. Are we ending up as persons - using one or many additional tools or AIs - in the area of already having tons of tools, moving so dramatically fast, that you must adapt with shorter iteration cycles. Good and Bad.

AI makes building microservices easy, but managing the resulting complexity is the real challenge. Don't let rapid code generation turn your architecture into a distributed monolith.

Cloud-native changed how we build software. AI-native will change it again. In this session, Pini Reznik explores the shift from cloud-native to AI-native and what it means for architecture, teams, and the way we deliver software. The key idea is that AI isn’t just another layer. It requires rethinking how systems are designed and how organizations operate. 👉 Watch the session here: https://lnkd.in/d4SQiSdJ

Let’s simplify the debate. Most CEOs do not care about: * 31 sovereignty criteria * Kubernetes architectures * attestation terminology * AI framework diagrams They care about: * Can we use AI safely? * Do we stay compliant? * Who is liable if something fails? * Can we move fast without losing control? That is the real market. The future will not belong to the most complex architecture. It will belong to the companies that solve multiple problems without forcing organisations to rebuild everything.

How much time does your team waste writing boilerplate Terraform code right after finalizing an architecture diagram on a whiteboard? I’m exploring an idea to completely automate this step using Agentic AI. The concept: take a photo of the whiteboard sketch, and let AI agents extract the AWS topology and generate the IaC for you in minutes. Before I spend my weekend building the prototype, I’d love to hear from you. Is this a bottleneck in your current workflow? Would you actually use a tool like this? Let's discuss in the comments. 👇

Been experimenting with persistent runtime coordination and replayable execution infrastructure for AI-native engineering workflows. Started as a lightweight workflow/context system. Gradually evolved into: append-only execution journaling deterministic replay reconstruction event-driven runtime coordination distributed worker orchestration recovery-oriented execution flow Recently spent time restructuring the repository: operational architecture docs runtime flow visibility AI-readable context layers deterministic replay commentary system visualization mapping One of the more interesting realizations during this build: Most AI workflows today are still fundamentally stateless. Context disappears. Execution history disappears. Operational reasoning disappears. I’ve been exploring what happens when engineering workflows become persistent, replayable and operationally recoverable instead. Still experimental. But the direction is getting very interesting. GitHub: https://lnkd.in/gmGXyDvW

Claude Code is quietly becoming the Swiss Army knife of AI development. From structured decision-making frameworks to multi-agent orchestration tools like Omar, developers are building increasingly sophisticated systems on top of it. What's fascinating is how quickly the community is pushing beyond basic code generation—we're seeing everything from ARC AGI game completion to perception capabilities emerge. The real question isn't whether Claude Code will replace traditional development workflows, but how fast teams can adapt to building with AI-native architecture. What's the most ambitious Claude Code project you've seen or built?

Saw this “NextGen Enterprise AI Architecture” model on LinkedIn recently and it genuinely stood out from the usual AI buzzword diagrams. What I liked most was the separation between: • Governance Plane • Runtime / AI Platform • Human-in-the-Loop Controls • AI Ops / Lifecycle Management • Enterprise Outcomes That’s the direction enterprise AI needs to move toward — especially in regulated industries like banking and insurance. Too many organisations are still treating AI as isolated copilots or experimental tools. In reality, enterprise AI needs to become: ✔ Governed ✔ Observable ✔ Secure ✔ Composable ✔ Enterprise-owned ✔ Operationally resilient However, from an Enterprise Architecture perspective, there are still some major gaps that many AI reference architectures continue to miss: 🔹 AI Security Architecture (prompt injection protection, model isolation, AI DLP, tenant boundaries, AI identity) 🔹 Enterprise Data Architecture (RAG pipelines, vector databases, lineage, semantic layers, metadata governance) 🔹 AI Resilience & DR (multi-region failover, inference continuity, fallback models, resilience testing) 🔹 AI FinOps (token governance, GPU optimisation, cost visibility, chargeback/showback) 🔹 Enterprise Integration (API orchestration, event-driven AI, workflow integration, system-of-record connectivity) 🔹 AI Identity & Trust (agent permissions, delegated authority, RBAC for autonomous agents) The future of Enterprise AI won’t be won by whoever deploys the most copilots. It will be won by organisations that can operationalise AI securely, govern it properly, integrate it deeply, and scale it responsibly. The industry is clearly moving from: “AI experiments” ➡️ to “Enterprise AI Operating Models”. Interesting times ahead for Enterprise Architects, Cloud Architects, Security Architects, and Data Leaders. #EnterpriseArchitecture #AI #CloudArchitecture #Azure #AWS #DataArchitecture #CyberSecurity #AIOps #EnterpriseAI #DigitalTransformation #ZeroTrust #Cloud #Architecture #AIGovernance #FinOps

The Wien Software Architecture Meetup is getting together, because AI is accelerating faster than most architectures were built to handle. That's worth talking about in person. On June 16 in Vienna, we’re meeting to dig into the real questions: What does the future of SDLC actually look like? How do you keep mass-generated code maintainable? And what’s the human cost of all this acceleration? Topics on the table: AI Burnout, Supervising AI in Production, Brain Drain & Talent Shifts, Future of SDLC, Future Interfaces, Mass Code & Maintainability, Security by Architecture. For engineers, architects and tech leads who’d rather have a real conversation than sit through another slide deck. Tuesday, June 16, 6:00 PM at Accenture Future Camp, Vienna.   Register: https://lnkd.in/dpNdkXr5

AI workloads are forcing a rethink of how platforms operate - or risk becoming the bottleneck. Traditional pipelines no longer hold up when flexibility, scale, and rapid iteration are required. In his talk, Andrey Gering from DoorLoop will share how they turned an EKS cluster into a self-serve execution platform for AI workloads, where engineers define jobs with a simple YAML while the platform handles orchestration, scaling, and infrastructure behind the scenes. From Argo Workflows and GitOps to dynamic autoscaling with Karpenter, this session shows how to make Kubernetes truly AI-engineer-friendly and ready for what’s next. Join Andrey's talk at PlatforMa 2026: https://lnkd.in/dptCXSh5