Operations | Monitoring | ITSM | DevOps | Cloud

"Connection pool exhausted" is one of PostgreSQL's most misleading production errors. The database is often healthy, while your application waits behind full connection pools. In Kubernetes, every pod and replica maintains its own pool, amplifying issues like connection leaks and slow queries across the cluster.

For many organizations, modernizing their VMs before migrating them is not a realistic option, especially when external events trigger the migration. Mapping dependencies and refactoring network configurations before the deadline is impractical, forcing VMs to move as they are. The mechanics of moving a VM are largely solved.

Most of our runtime security habits were built for deterministic workloads. A service does what its code says: review the code, sign the image, and its behavior is bounded. Agents are different. An agent’s behavior emerges from a model reasoning over whatever lands in its context window, and some of that context comes from places we don’t fully control — a retrieved document, a tool’s output, a user’s prompt.

Every so often the ground under enterprise IT moves. It’s moving now. Across industries, organizations are consolidating fragmented infrastructure onto a single, self-hosted platform capable of running both containers and virtual machines side by side. The motivation is simple: simplify operations, lower cost and reallocate resources & budget to AI initiatives. Kubernetes is emerging as the primary platform for many of these workloads.

Google has quietly rolled out a new beta feature for Google Kubernetes Engine (GKE): the MultidimPodAutoscaler, or MPA. Announced in early July 2026, MPA gives GKE users a single custom resource that scales both horizontally and vertically at the same time.

A KubeVirt VM runs inside a pod. That is the trick that lets Kubernetes schedule a VM like any other workload. It also means the VM and the pod have different lifecycles. The VM is long-lived and has a stable identity. The pod is disposable. When the VM reboots, gets evicted, or live-migrates, the pod underneath is destroyed and a new one is created.

Most cloud-native cost tools are built to find and address waste reactively. Underutilized nodes, oversized requests, and idle workloads are revealed in the utilization data, the fixes are well documented, and the initial savings these tools drive are very real. But what we’ve seen consistently across clusters is a different category of blocker, one that quietly prevents consolidation and strands capacity your autoscaler can never reach. They don’t surface in dashboards as obvious waste.

Coding agents changed implementation economics faster than they changed confidence. They let us produce more code, more quickly, but they did not make reviewers any better at understanding system-wide consequences. In our Kubernetes automation stack, that gap became impossible to ignore once AI started generating meaningful amounts of controller code.