Beyond Fixed Node Allocation: Composable Infrastructure and Dynamic Resource Allocation for AI Agent Fleets

When scaling AI agent orchestration pipelines across production Kubernetes clusters, platform engineering teams almost always hit an operational wall. It rarely starts with container runtime panics or scheduler crashes. Instead, it manifests as severe resource stranding: worker nodes sitting at high host memory allocation while expensive acceleration hardware sits completely idle, locked to host nodes whose CPUs are choked by background task management.

When I led a migration from legacy virtual machines to Kubernetes in 2024, we encountered unexpected networking issues that required tuning of the network hops and configuration. That experience taught me that fitting new workload paradigms into old infrastructure boundaries always forces hidden trade-offs. With modern agentic AI workloads, we are attempting to run thousands of short-lived, event-driven agents on fixed node pools designed for static microservices. The software layer has attempted to optimize around this mismatch, but software density tricks eventually run into physical hardware limits.

To scale agent fleets efficiently, we must look beyond fixed node allocations. Combining software-level sandboxing with Kubernetes Dynamic Resource Allocation (DRA) and Composable Disaggregated Infrastructure provides the architectural path forward.

The Limits of Software-Level Sandbox Density

To handle bursts of asynchronous execution, cloud providers have heavily invested in software sandbox density. Google reports that GKE Agent Sandbox usage grew 7x in under four weeks. According to Google, GKE Agent Sandbox increases agent density by 44% compared to microVMs, enabling up to 88 OpenClaw agents to run inside the same VM before failure.

At the container level, techniques like GKE Pod snapshots allow checkpointing idle agents to release CPU and memory back to the cluster. When these agents need to wake up for incoming user queries, Agent Sandbox warm pools can be used to maintain sub-second startup times for latency-sensitive agents.

These software optimizations are valuable for density when agents rely solely on lightweight CPU tasks or general host memory. However, AI agent orchestration increasingly demands immediate access to hardware accelerators for localized model inference, embedding generation, and rapid vector search.

When an idle agent holding an accelerator reference is snapshotted or frozen, software mechanisms can free up host RAM and virtual CPU cycles, but they cannot dynamically unbind the physical PCIe bus from the underlying motherboard. The accelerator remains trapped on that specific worker node. If adjacent nodes in the cluster need GPU acceleration for heavy LLM workload scaling, the scheduler cannot move that physical card across host boundaries. Software sandboxing optimizes multi-tenant process density, but it leaves hardware capacity rigidly stranded.

Massive Workloads Require Hardware Flexibility

The scale of enterprise AI platform operations makes hardware stranding unacceptably expensive. Microsoft reported that AT&T processed approximately 1 trillion tokens for OTel2.0 development. Operating workloads of this magnitude requires platforms capable of serving heterogeneous hardware demands without forcing platform engineers to over-provision static host node pools. To support varied compute patterns, Microsoft Foundry Managed Compute supports heterogeneous GPU architectures including AMD Instinct MI300X.

In a traditional Kubernetes topology, supporting heterogeneous hardware means maintaining separate, static node pools for each device type. When an agent workflow requires an accelerator for a brief processing step, the pod must be scheduled onto a specialized node pool. This introduces node scaling latencies, increases host overhead, and leaves hardware idle whenever agent task queues drain.

This is where the paradigm must shift from fixed worker nodes to Composable Disaggregated Infrastructure.

Rather than permanently attaching PCIe devices to physical host motherboards, composable infrastructure decouples compute, memory, and acceleration into disaggregated resource pools connected over high-speed fabrics. Through CNCF initiatives like CoHDI, Kubernetes can directly orchestrate physical hardware disaggregation. The CoHDI specification allows for the dynamic, host-level attachment and detachment of PCIe devices by leveraging Kubernetes DRA to manage these resources within a composable disaggregated infrastructure.

As a platform architect designing distributed systems for a large technology company, I regularly evaluate whether operational complexity pays off in production. Holding CKA, CKAD, and CKS Kubernetes certifications gives me deep appreciation for how Kubernetes resource management primitives evolve. Having spent more than twenty years building and operating production software systems, I have learned that physical hardware boundaries inevitably dictate scaling limits. Software-level warm pools and pod snapshotting solve half of the equation; Kubernetes DRA paired with composable hardware solves the physical half.

Declarative Device Allocation with Kubernetes DRA

Kubernetes Dynamic Resource Allocation (DRA) replaces generic extended resources (nvidia.com/gpu) with structured resource claims. Paired with CoHDI, a pod can request acceleration devices that are dynamically attached to the host node at runtime and cleanly detached when the workload completes.

apiVersion: resource.k8s.io/v1alpha3
kind: ResourceClaimTemplate
metadata:
  name: cohdi-accelerator-template
  namespace: agent-platform
spec:
  spec:
    devices:
      requests:
        - name: dedicated-accelerator
          deviceClassName: cohdi-pcie-device
---
apiVersion: apps/v1
kind: Deployment
metadata:
  name: agent-inference-worker
  namespace: agent-platform
spec:
  selector:
    matchLabels:
      app: agent-inference-worker
  template:
    metadata:
      labels:
        app: agent-inference-worker
    spec:
      containers:
        - name: agent-runner
          image: agent-runner:latest
          command: ["/bin/sh", "-c", "tail -f /dev/null"]
          resources:
            claims:
              - name: accelerator-claim
      resourceClaims:
        - name: accelerator-claim
          resourceClaimTemplateName: cohdi-accelerator-template

How to Verify Dynamic Attachment and Detachment

To verify that Kubernetes DRA and the underlying disaggregated fabric are correctly attaching and detaching PCIe devices:

• Deploy the manifest: Submit the resource claim template and worker deployment to your cluster using kubectl apply -f agent-infrastructure.yaml.
• Verify claim creation: Inspect the status of the dynamically generated resource claims by running kubectl get resourceclaims -n agent-platform. Confirm that the claim status transitions to allocated once the pod is scheduled.
• Check host device attachment: Describe the running pod using kubectl describe pod -l app=agent-inference-worker -n agent-platform. Check the events section and DRA driver logs to verify that the CoHDI control plane physically attached the PCIe device to the target node’s bus.
• Test dynamic detachment: Scale the deployment to zero replicas using kubectl scale deployment agent-inference-worker --replicas=0 -n agent-platform. Confirm via kubectl get resourceclaims -n agent-platform that the claim is released, and verify through the fabric controller logs that the PCIe device was detached from the host and returned to the unallocated hardware pool.

When NOT to Do This

While composable disaggregated infrastructure and Kubernetes DRA unlock massive operational efficiency for large agent fleets, this approach is not appropriate for every environment:

• Small or Homogeneous Clusters: If your workloads run on uniform hardware with predictable CPU and memory footprints, the operational burden of disaggregated fabric switches and specialized DRA drivers far outweighs the savings.
• Ultra-Low Latency Inter-GPU Communication: Connecting hardware over composable fabric switches introduces slight fabric latency compared to local high-bandwidth host interconnects. Workloads requiring extreme peer-to-peer memory bandwidth across GPUs should remain on tightly coupled, dedicated GPU worker nodes.
• Teams Without Deep Hardware Infrastructure Support: Managing CXL or PCIe fabric controllers requires hardware-level observability and specialized platform engineering expertise. If your team struggles with standard Kubernetes cluster lifecycles, adding hardware disaggregation introduces high operational risk.

Recommendation

Software-level sandboxing techniques like GKE Agent Sandbox provide immediate density wins for CPU-bound agent tasks. But relying solely on container snapshotting while running fixed worker node pools leaves physical hardware trapped behind static node boundaries.

The future of high-density agent platforms requires decoupling at both layers: software-level snapshotting and warm pools for fast startup, combined with Kubernetes DRA and CoHDI for hardware-level disaggregation. Platform teams building for long-term LLM workload scaling should begin evaluating DRA-compatible control planes today.

As agent fleets grow in complexity and hardware diversity, will your cluster architecture be constrained by static physical worker nodes, or will your platform control plane compose hardware dynamically on demand?

References

• [Reduce your agent’s costs by 75% with GKE Agent Sandbox

  Google Cloud Blog](https://cloud.google.com/blog/products/containers-kubernetes/reduce-your-agents-costs-with-gke-agent-sandbox)

• [AT&T and Microsoft scale trillion-token workloads with Microsoft Foundry and AMD

  Microsoft Azure Blog](https://azure.microsoft.com/en-us/blog/att-and-microsoft-scale-trillion-token-workloads-with-microsoft-foundry-and-amd)

• Welcome CoHDI to the CNCF: Evolving Kubernetes into composable disaggregated infrastructures