The outcomes of installs and upgrades might be totally different every time, even with the very same mannequin, nevertheless it will get loads worse in case you improve or change fashions. Should you’re supporting infrastructure for 5, 10, or 20 years, you will be upgrading fashions. It’s exhausting to even think about what the world of generative AI will appear to be in 10 years, however I’m positive Gemini 3 and Claude Opus 4.5 won’t be round then.
The hazards of AI brokers enhance with complexity
Enterprise “purposes” are not single servers. Immediately they’re constellations of techniques—net entrance ends, utility tiers, databases, caches, message brokers, and extra—typically deployed in a number of copies throughout a number of deployment fashions. Even with solely a handful of service sorts and three primary footprints (packages on a standard server, picture‑primarily based hosts, and containers), the mixtures broaden into dozens of permutations earlier than anybody has written a line of enterprise logic. That complexity makes it much more tempting to ask an agent to “simply deal with it”—and much more harmful when it does.
In cloud‑native retailers, Kubernetes solely amplifies this sample. A “easy” utility may span a number of namespaces, deployments, stateful units, ingress controllers, operators, and exterior managed companies, all stitched collectively by means of YAML and Customized Useful resource Definitions (CRDs). The one sane strategy to run that at scale is to deal with the cluster as a declarative system: GitOps, immutable photographs, and YAML saved someplace exterior the cluster, and model managed. In that world, the job of an agentic AI is to not sizzling‑patch operating pods, nor the Kubernetes YAML; it’s to assist people design and check the manifests, Helm charts, and pipelines that are saved in Git.
