Operations | Monitoring | ITSM | DevOps | Cloud

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.

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.

In the previous post in this series, we covered why Virtual Machine (VM) Live Migration in Kubernetes is difficult: a VM’s IP is its identity, and the “new” VM on the destination node has to come up with the same IP, this something that Kubernetes is not known for, and on top of that, traffic has to switch over only after network security policies are in place.

Kubernetes is built for containers, and it’s been doing that since it used to run docker as an engine for its containers. But what if you want to add VMs to the mix? After all, containers are ephemeral and don’t require fixed IPs as they shift the identity toward labels, but VMs on the other hand are tied to IP addresses and in some cases MAC addresses. This brings us to this blog about VM migration and IP preservation.

There’s a question we hear constantly from platform and engineering leaders right now, “which agent SDK should we standardize on for our Kubernetes clusters?” The honest answer is that the question is slightly wrong, and the rest of this post explains why. But it’s a fair question, so let’s compare the contenders first.

We launched Lynx this week. Instead of restating the pitch, I want to explain how it’s built and why we made the architectural choices we did. If you run Kubernetes and you’re starting to put AI agents on it, this is roughly the system you’d end up designing yourself.

For a decade, one idea has guided everything we’ve built at Tigera: How do you secure a dynamic system with a lot of moving parts that is changing rapidly, with a programmatic approach? Calico has applied that idea for Global 2000 companies running the largest Kubernetes platforms in the world, securing tens of millions of mission-critical transactions every day. Today I’m excited to announce the next chapter of that work: Lynx, a unified control plane for Kubernetes-native AI agents.

You know every service in your cluster by name. You know which team owns each one, what it talks to, how it scales, where its logs go. The agents are a different story. That’s not a criticism, it’s an observation, and it’s one we keep running into. Every company we talk to is shipping agents of some kind, from scales of 10s to 1000s. Customer service bots that field tier-one tickets. Internal copilots that draft emails and summarise meetings and write the boring half of every PR.