🚀 Self-Healing Kubernetes: Orchestrating GPU Slicing with n8n 2.0 and Kiro-cli Agents

It was 5:45 PM on a Friday—classic deployment time for disaster. Our new multi-modal inference service had just gone live, and within minutes, the alerting channel lit up like a Christmas tree. The error? OOMKilled. The pod was thrashing, consuming every bit of VRAM on the A100 node, starving the critical payment processing service sharing that same GPU. In the old days, this would mean paging the on-call engineer (me) to manually cordon the node, kill the rogue pod, and painstakingly adjust resource limits while sweating over kubectl. But this time, I just watched. A notification popped up in Slack: “Anomaly detected: VRAM exhaustion on Node gpu-01. Auto-remediation initiated.” Moments later: “Plan Agent analysis complete. GPU MIG profile adjusted. Pod restarted with new slices. Service healthy.” The system fixed itself. This isn’t science fiction; it’s the reality of modern Kubernetes AI deployment using the latest breed of agentic automation.

We are witnessing a shift that creators like NetworkChuck have been warning us about: the rise of the “AI IT Department.” But unlike the fear-mongering about robots taking jobs, the reality is far more pragmatic and exciting. It involves using tools like n8n, Claude Code, and Kiro-cli to build autonomous SRE agents that handle the heavy lifting of network operations and capacity planning. In this post, we will explore how to build a self-healing Kubernetes cluster that leverages **NVIDIA MIG** for dynamic **GPU slicing**, orchestrated by the brand new n8n 2.0 and the latest Kiro-cli agents.

🤖 The Agentic Layer: Kiro-cli 1.23.0 and Claude Code

The brain of our operation isn’t a static script; it’s an intelligent agent capable of reasoning. While we have had CLI tools for a while, the release of Kiro-cli version 1.23.0 brings features that are critical for autonomous operations: Subagents, the Plan Agent, and the MCP (Model Context Protocol) Registry. These aren’t just buzzwords; they represent a fundamental change in how we execute terminal commands programmatically.

In our self-healing scenario, we use Kiro-cli as the execution engine running directly on a secure management pod. When triggered, we don’t just ask it to “restart the pod.” We invoke the new Plan Agent. This specialized agent first analyzes the situation—running `kubectl describe`, checking `nvidia-smi` logs, and reviewing recent commits—to formulate a remediation plan. It might decide that a restart is insufficient and that the GPU partition size needs to be increased. Only once the plan is formulated does it delegate execution to a Subagent. This separation of planning and action prevents the “bull in a china shop” problem common with earlier AI automations.

Furthermore, Kiro-cli’s integration with the MCP Registry allows it to securely access context from our documentation and architecture diagrams, ensuring it understands why the cluster is configured a certain way. This mirrors the “headless” capabilities recently introduced in Claude Code, where agents can operate without a UI, integrating seamlessly into CI/CD pipelines. As detailed in this comparison of 2011 Watson AI vs modern ChatGPT 5.0, the leap in reasoning capabilities allows these agents to handle complex logic that rigid scripts simply cannot.

# Example Kiro-cli Plan Agent Invocation via n8n Script Node
# Triggers a planning session for OOM remediation

#!/bin/bash
kiro-cli plan \
  --context "Pod ai-inference-v2 failed with OOM on node gpu-01" \
  --tools "kubectl, nvidia-smi, logs" \
  --goal "Restore service health and prevent recurrence" \
  --output-json /tmp/remediation_plan.json

⚙️ Orchestration Evolution: n8n 2.0

If Kiro-cli is the hands, n8n is the nervous system. The recently released n8n 2.0 is a massive step forward for enterprise-grade automation. For DevOps engineers, the most critical update is the “Secure by Default” philosophy. In previous versions, running arbitrary code or shell commands (like triggering our Kiro agent) could be risky if the main n8n process was compromised. n8n 2.0 introduces Task Runners which are enabled by default. These isolate code execution environments, ensuring that our heavy-duty automation scripts run separately from the main workflow engine.

Another pain point addressed in 2.0 is the separation of “Save” and “Publish.” When building complex auto-healing flows, you don’t want your half-finished logic effectively live just because you hit save. This allows us to iterate on our “AI SRE” workflows safely. We can model the logic: Receive Prometheus Alert -> Verify with Kiro Plan Agent -> Request Human Approval (optional) -> Execute Remediation. This flow replaces legacy PagerDuty-to-human loops. As Nick Puru has highlighted in his coverage of AI automation tools, platforms like n8n are rapidly evolving from simple integration glue to robust backend orchestrators that can effectively replace junior operations roles.

While tools like Lovable are making waves for their ability to generate frontends and simple backends via “vibe coding,” for deep infrastructure work, the determinism and control of n8n 2.0 remain superior. We need to know exactly which `kubectl` command is being fired, and n8n’s visual audit trail combined with Kiro’s session logs provides that transparency.

🐳 Infrastructure: GPU Slicing and NVIDIA MIG

Now, let’s talk about the resource we are managing. In the age of Large Language Models (LLMs), the GPU is the most expensive resource in the cluster. Allocating a whole A100 to a small inference model is wasteful. This is where GPU slicing comes in. We have two main approaches: Time-Slicing and **NVIDIA MIG** (Multi-Instance GPU).

Time-slicing is software-based; it interleaves workloads on the GPU cores. It’s flexible but lacks memory isolation—one OOMing pod can crash others. **NVIDIA MIG**, on the other hand, partitions the GPU hardware itself into isolated instances with dedicated memory and compute. For our self-healing cluster, MIG is the preferred choice because it provides fault isolation. If our inference pod crashes a MIG slice, it doesn’t affect the training job on the adjacent slice.

The challenge with MIG is that reconfiguring partitions (e.g., changing from seven 5GB slices to three 20GB slices) is non-trivial and often requires draining the node. However, with our AI agent, we can automate this capacity planning. The agent can detect that a deployment requires a larger slice, cordon the node, re-apply the MIG profile, and uncordon it—all without human intervention. This dynamic adjustment is crucial when comparing KServe vs Seldon for model serving; KServe’s serverless nature pairs beautifully with dynamic MIG partitioning to scale to zero or scale up based on demand.

# NVIDIA MIG Partition Configuration in Kubernetes
apiVersion: v1
kind: ConfigMap
metadata:
  name: mig-parted-config
  namespace: gpu-operator
data:
  config.yaml: |
    version: v1
    mig-configs:
      all-1g.5gb:
        - devices: all
          mig-enabled: true
          mig-devices:
            "1g.5gb": 7
      mixed-strategy:
        - devices: [0]
          mig-enabled: true
          mig-devices:
            "3g.20gb": 2
            "1g.5gb": 1

🧠 Practical Implementation: The Auto-Healing Loop

Let’s construct the full loop. We start with a KServe InferenceService deploying a Llama-3 model. It is configured with a resource request that maps to a specific MIG profile.

1. **Monitoring**: Prometheus monitors `container_memory_usage_bytes` and `DCGM_FI_DEV_GPU_UTIL`. An alert fires if memory usage exceeds 90% of the allocated slice.
2. **Trigger**: The alert webhook hits an n8n 2.0 webhook node.
3. **Analysis**: n8n passes the alert payload to a “Code” node running a Kiro-cli wrapper. The Kiro Plan Agent investigates. It sees that the incoming request batch size has increased, requiring more VRAM.
4. **Decision**: The agent checks the node capacity. It sees available space to reconfigure the MIG geometry from `1g.5gb` to `2g.10gb` on a spare GPU.
5. **Execution**: Kiro spawns a Subagent to apply the new `ConfigMap` (like the example above) and trigger the NVIDIA operator to re-partition. It then patches the KServe deployment to request the new resource type.
6. **Verification**: The agent waits for the pod to reach `Ready` state and posts a summary to the SRE Slack channel.

This automated capacity planning ensures security scanning is not an afterthought. The agent can also run tools like Trivy or Falco during the analysis phase to ensure the OOM wasn’t caused by a cryptomining exploit. This holistic view is what makes the “AI Agent” approach superior to simple scripts.

# KServe InferenceService requesting a specific MIG slice
apiVersion: "serving.kserve.io/v1beta1"
kind: "InferenceService"
metadata:
  name: "llama-3-inference"
  namespace: "ai-ops"
spec:
  predictor:
    model:
      modelFormat:
        name: pytorch
      storageUri: "s3://models/llama-3-quantized"
      resources:
        limits:
          nvidia.com/mig-2g.10gb: 1
        requests:
          nvidia.com/mig-2g.10gb: 1

💻 Conclusion

The convergence of **Kubernetes AI deployment** tools is creating a new paradigm for operations. We are moving away from static dashboards and manual runbooks toward dynamic, agent-driven infrastructure. The combination of n8n 2.0’s secure orchestration, Kiro-cli’s reasoned planning agents, and the hardware isolation of **NVIDIA MIG** allows us to build systems that don’t just alert us to problems but actively solve them.

While some may fear that tools like Claude Code and autonomous agents will replace network engineers, the reality is that they elevate the role. Instead of fixing OOM errors at 3 AM, engineers can focus on architecture, model optimization, and governance. The “AI IT Department” isn’t a replacement; it’s the ultimate force multiplier. As you explore these tools, remember to focus on security and observability—allowing an agent to rewrite your infrastructure requires trust, but with the robust logging of n8n and the governance of the MCP registry, that trust can be verified.