Seasoned techies recall with nostalgia a simpler time when the “single-stack” model of enterprise computing held as the default approach to solving interoperability headaches. Back then IT decision-makers were prepared to forfeit some choice and flexibility in how they specified IT systems in return for the availability and manageability that came with a homogenous operating environment. With the subsequent rise of multi-clouds, application-specific hardware, ever-changing business requirements, and the race for AI, the old “one-size-fits-all” playbook has been largely abandoned. Most IT estates have embraced technological diversity, some by design, some through standardization, some by inertia. Modern enterprise environments have become innately heterogeneous – and innately complex. They run a diverse combo of system software, hardware devices, cloud services, legacy applications, et al . The advance of AI has brought standard CPUs, specialized GPUs, fast networking fabrics, distributed data pipelines, and new deployment models that span private clouds, public clouds, edge – and ever-pressing ROI expectations. But that doesn’t mean that companies’ existing applications and data simply disappear. One trying but inevitable outcome of rapid AI deployment is the so-called “VM-container divide”. This is where critical business applications tipped to transition to containers continue to run on virtual machines, because cloud-native services are taking precedence, nabbing container and Kubernetes availability. So companies face the challenge of operationalizing AI, without being forced into complex migrations or creating new infrastructure silos. Dealing with this technological schism poses acute challenges for IT management. Many new AI deployments are going straight to containers, while existing data and applications – which the AI needs to access – continue to run on VMs. Running separate stacks for VMs and containers creates a number of issues, such as duplicate capacity plans, separate support contracts, and fragmented governance. And after years of trying to get beyond the pitfalls of siloed data, exasperated enterprises are now having to deal with siloed infrastructure. Separately managed VM and container environments often mean inefficient resource utilization. This in turn can lead to “stranded capacity” – installed computing resources that cannot be used to support live workloads. VM clusters are frequently underutilized while container pools (like Kubernetes) are overprovisioned, with no easy way to share resources without manual intervention. Furthermore, VM/container infrastructure fragmentation can create an operations bottleneck for agentic AI. Infrastructure fragmentation can complicate the deployment, integration, governance, and scaling of agents thus making it harder to operationalize and scale agentic AI securely and consistently. When AI agents cannot autonomously communicate across environments, their utility droops from autonomous problem-solvers to plain localised macros. Containers are gaining because of the widespread embrace of AI-enabled applications. Eighty-five percent of IT executives polled by Nutanix’s most recent Enterprise Cloud Index survey think that AI is accelerating their organization’s adoption of containers in a meaningful way; 29 percent think it is speeding-up adoption “to a great extent”. “AI is accelerating the shift to containers because machine learning models require strict dependency packing, while critical applications and data remain spread across both virtualised and containerised environments,” says Harsha Kotikela, Senior Director of Product & Solutions Marketing at Nutanix. Agents are multi-tier by nature he explains. “Inference is typically GPU-intensive and container-based, while agents are often CPU-intensive and container-based, and existing enterprise applications and data largely remain in VM environments. The challenge is to bring these tiers together so agents can work with enterprise applications and data, without forcing migration or creating additional infrastructure silos.” The application landscape, meanwhile, has become just as diverse. Many organisations simultaneously operate traditional enterprise applications running on VMs, containerised apps, distributed AI applications and intelligent agents, with workloads deployed across private cloud, public cloud, and edge. Each environment has its own management tools, operational processes, security rules and governance models. “Management capability has struggled to scale up commensurately,” says Kotikela, but adding more consoles or building separate operational teams for every technology stack does not necessarily solve this complexity: “In many cases, it increases it.” However, Kotikela challenges the notion that the predicament enterprises face in straddling the VM-container divide is intractable. Rather, the challenge is to move towards a unified operating model that allows them to operationalize agentic AI while retaining flexibility across their existing and new environments…