Jonathan Anthony
Latest from Jonathan Anthony
AI agents are changing how enterprises create, move and govern information, and data observability is turning into a prerequisite rather than a nice-to-have. Models get most of the attention, but the data beneath them decides whether they hold up in production. That …
A decade in, KubeVirt pushes virtual machines into the AI agent era
Confidential computing is drawing fresh interest as enterprises weigh where their most sensitive AI workloads can safely run. That shift is giving virtual machines a new role alongside containers on Kubernetes, rather than a slow retirement. The timing lines up with a …
Google’s GKE Labs turns reinforcement learning into a Kubernetes workload
Model fine-tuning through reinforcement learning is fast becoming one of the most demanding jobs in AI infrastructure. It mixes training, inference and sandboxed agent work in a single loop, often with research code tangled into the systems beneath it. Google LLC has …
Neoclouds face a shakeout as cloud-native software skills decide who survives
AI infrastructure clouds have multiplied as GPU capital pours into new data centers, but standing up capacity is proving easier than running it at scale. With Kubernetes now hardened into the operating layer for AI factories, the cloud-native community holds expertise that …
Enterprises, not a handful of giants, are becoming the GPU buyers that matter
Enterprises building AI-native applications are discovering that raw GPU capacity is only half the equation. As Kubernetes users move from cloud-native to AI-native stacks, the question is shifting from who has the most accelerators to who can run distributed, secure workloads at …
Kubernetes had settled into quiet maturity until AI blew the doors back open
Kubernetes has grown from a maturing container orchestrator into the agent substrate that enterprises are counting on to run artificial intelligence. The shift revives old security ideas such as perimeters while raising the stakes for portability across clouds. Infrastructure providers are racing …
AI agents carry memory and storage like people, so their data needs guarding while in use
As AI agents move into production on Kubernetes, cloud engineering teams at financial companies face a security problem that encryption alone can’t solve. Container orchestration has become the default foundation for AI workloads, and the data those agents use is now the …
Agents may soon be the main workload, and most of the time they’re doing nothing
As AI agents take on more enterprise work, agent sandboxing has become a basic requirement rather than an afterthought. Every agent that writes code, runs tools or drives a browser needs hard walls around it. Those walls also can’t come at the …
Oracle wants to pull stalled AI agents out of demo mode and into production
Companies want AI agents faster than they can build the AI architecture to run them. Most can put together a convincing demo. Far fewer can move agents into production without exposing data, hurting uptime or blowing their budgets. That gap is pushing …
Oracle’s multicloud approach: Put the database wherever AI pipelines already run
Enterprises racing to put AI agents into production are learning that real-time data pipelines matter as much as the models. An agent working from a stale subset of corporate records delivers far less than one wired into live, mission-critical data. That shift …