Operations | Monitoring | ITSM | DevOps | Cloud

Monitoring Kubernetes in production is structurally different from monitoring traditional servers: pods are ephemeral, nodes autoscale, and the layered resource model—containers, pods, nodes, namespaces, deployments, services—creates an observability challenge that tools built for static infrastructure were never designed to handle.

This week marks the end of our fourth Civo Navigate London event, and it feels like a good moment to say that this one had a slightly different energy from the ones before it. Over the past four years, we have hosted 10 Civo Navigate events across North America, India, and Europe, and each one has taught us something new about what this community actually wants from a day like this. London 2026 was no exception, and I think this year's lineup pushed that a little further than usual.

Jamin Ball’s recent piece, “Systems of Record Won the SaaS Era — Clearinghouses Will Win the Agents Era,” is the cleanest articulation I’ve seen of where the durable moat goes next. His argument is simple and, I think, correct: the SaaS era rewarded whoever owned the system of record, and the agent era will reward whoever owns the clearinghouse.

We build agents to take work off engineers’ plates. Then we give those engineers a new manual job: reading failed runs and babysitting prompts. Agents will improve themselves automatically. We’re not there yet, but this is the future I’m betting on. We’ve been working on this ourselves at Komodor over the past year. We know how hard it is to turn a failure into an improvement that holds up beyond a few examples.

Kubernetes has become a standard foundation for modern containerized applications, but operating clusters still requires significant engineering work. In the CNCF’s 2025 annual cloud native survey, published in January 2026, 82% of container users reported running Kubernetes in production. As adoption matures, the question for many teams is no longer whether to use Kubernetes, but how much of the operational burden they want to own.

Table of Contents GPU sharing with NVIDIA hardware is becoming easier to adopt in Kubernetes but it hasn’t been easier to observe. Time-slicing lets multiple workloads share the same GPU. MPS allows CUDA workloads to execute concurrently. Schedulers like KAI make it easier to manage these shared environments. But sharing a GPU introduces a problem that is easy to underestimate.