Operations | Monitoring | ITSM | DevOps | Cloud

At Tigera, we spend a lot of time thinking about agent security: identity, policy, runtime controls, and the record left behind after an agent acts. Coding agents create an interesting problem because, in most organizations, they didn’t arrive through the front door. Few companies ran a platform evaluation and rolled Claude Code out to 500 developers. Developers installed it themselves.

The conversation around sovereign cloud in India is getting louder, which is welcome, but as more providers enter the space, I keep seeing the same architectural pattern, and it's worth being direct about what it misses. The pattern is familiar... take a cloud platform, host it in India, manage it end to end, call it sovereign, and let data residency carry the rest of the argument. That works for web apps and standard enterprise workloads.

Table of Contents Autoscaling and policy-driven resource management are two sides of the same coin. Autoscaling adjusts capacity as demand changes, while policies define the boundaries it operates within: who can use how much, which workloads can be changed, and what safeguards must be respected. Without autoscaling, clusters are either overprovisioned or overwhelmed. Without policies, autoscaling can create runaway costs, noisy neighbors, or disruptive changes to critical services.

Table of Contents Resource contention happens when workloads compete for more capacity than a node or cluster can provide. For CPU and memory, the symptoms are familiar: CPU throttling, OOM kills, and noisy neighbors slowing latency-sensitive services. These are largely solved problems, addressed by accurate requests and limits, Quality of Service classes, and autoscalers like VPA and HPA that adjust sizing and replicas as demand changes. GPU contention is different, and far more expensive to get wrong.

Replay and recap of our live session: an AI agent reads a Heroku Rails app and deploys the full stack to AWS through Qovery from one prompt. Chapters, timestamps, the four ways teams leave Heroku, and the steps a human should still own. Romaric founded Qovery to make Kubernetes accessible to every engineering team. He writes about platform strategy, developer experience, and the future of cloud infrastructure.

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.