Operations | Monitoring | ITSM | DevOps | Cloud

Here is what nobody putting together the business case for a VM migration to Kubernetes will tell you upfront: the compute is the easy part. Moving workloads off vSphere and onto Kubernetes is conceptually straightforward. The tooling has matured. The architecture is proven. Compute moves, storage remaps, and the platform team has a plan. The network is where projects quietly stall.

Organisations are re-evaluating their VM infrastructure. The economics have shifted, the tooling has matured, and the case for running two separate platforms, one for containers, one for VMs, is getting harder to justify. Platform teams that spent years managing hypervisor infrastructure are being asked to consolidate, and most are landing on the same answer: Kubernetes. KubeVirt makes running VMs on Kubernetes possible.

Why accountability, not capability, is the real bottleneck for enterprise agentic AI, and what security leaders need to do about it before regulators force the issue. Every enterprise is building AI agents. Marketing has one summarizing campaign performance. Engineering has one triaging incidents. Customer support has one resolving tickets. Finance has one processing invoices.

Kubernetes adoption is no longer the challenge it once was. More than 82% of enterprises run containers in production, most of them on multiple Kubernetes clusters. Adoption, however, does not mean operational maturity. These are two very different things. It is one thing to deploy workloads to a cluster or two and quite another to do it securely, efficiently and at scale. This distinction matters because the gap between adoption and Kubernetes operational maturity is where risk accumulates.

If you are treating Large Language Models (LLMs) like simple question-and-answer machines, you are leaving their most transformative potential on the table. The industry has officially shifted from zero-shot prompting to structured AI agent design patterns and agentic workflows where AI iteratively reasons, uses external tools, and collaborates to solve complex engineering problems.