🤖 The Hands: Claude Code Headless & Kiro Subagents

While n8n provides the workflow logic, the actual “intelligence” required to diagnose a complex cluster failure often requires an LLM capable of reasoning and tool use. This is where the new headless capabilities of Claude Code and the subagent architecture of Kiro-cli 1.23.0 come into play. Claude Code’s new headless mode (invoked via the -p flag) allows it to be embedded directly into CI/CD pipelines or Kubernetes Jobs without an interactive UI. An n8n workflow can trigger a Kubernetes Job running Claude Code to perform a root cause analysis on the crashing pod logs, utilizing the Model Context Protocol (MCP) to securely access cluster state.

Simultaneously, Kiro-cli version 1.23.0 has introduced the concept of “subagents” and a “Plan agent.” In our OOM scenario, n8n could trigger a Kiro Plan agent to devise a remediation strategy. The agent might determine that the GPU resources are fragmented and decide to implement GPU slicing using NVIDIA MIG (Multi-Instance GPU) technology. Instead of a human manually calculating the geometry, the agent generates the patch command. This dynamic resource allocation is essential for modern AI workloads where static partitioning leads to waste. By leveraging RAG applications that enhance LLMs with retrieval from knowledge bases, these agents can even reference internal runbooks to ensure their proposed changes comply with company policy before execution.

# Example K8s Job triggering a Claude Code Headless Agent
apiVersion: batch/v1
kind: Job
metadata:
  name: ai-root-cause-analysis
spec:
  template:
    spec:
      containers:
      - name: claude-agent
        image: anthropic/claude-code:latest
        command: ["/bin/sh", "-c"]
        args:
          - |
            claude -p "Analyze the logs in /var/log/pods for OOM errors. 
            If found, suggest a kubectl patch for NVIDIA MIG config." \
            --allowedTools "kubectl,grep,cat"
        volumeMounts:
        - name: pod-logs
          mountPath: /var/log/pods
      restartPolicy: Never

☁️ The Next Wave: Sim AI, Lovable, and Model Serving

As we look at the evolving landscape, the question arises: what comes next after n8n? Content creators like Nick Puru have been investigating “n8n killers” such as Sim AI and Lovable. While Lovable focuses heavily on the “vibe coding” experience—generating full-stack applications from prompts—Sim AI presents a compelling open-source alternative for AI-native workflows. For a DevOps engineer, the choice often comes down to stability versus innovation. While Sim AI offers rapid, local-first agent building which appeals to privacy-conscious teams, n8n’s maturity in handling webhooks and integrations makes it stickier for critical infrastructure operations. However, the integration of these tools relies heavily on the underlying model serving infrastructure.

When deploying the models that power these agents, or the models the agents are managing, the debate often settles on KServe vs Seldon. KServe (formerly KFServing) has gained traction for its serverless traits and native integration with Knative, allowing for scale-to-zero capabilities that save costs on expensive GPU nodes. Seldon Core, conversely, offers robust enterprise features and complex inference graphs. For a self-healing cluster, an agent might interact with KServe for deploying ML and AI models on Kubernetes to dynamically adjust the `minReplicas` based on real-time inference load, effectively closing the loop between monitoring and action. The future likely holds a hybrid approach: n8n orchestrating high-level logic, while specialized tools like Kiro and Sim AI handle the granular, intelligent sub-tasks.

# Dynamic MIG Reconfiguration Patch generated by Agent
# Applied via kubectl patch to the Node or GPU Operator Policy
apiVersion: v1
kind: ConfigMap
metadata:
  name: custom-mig-config
data:
  config.yaml: |
    version: v1
    mig-configs:
      mixed-strategy:
        - devices: all
          mig-enabled: true
          mig-devices:
            "1g.10gb": 2
            "2g.20gb": 1

💻 Conclusion

The convergence of robust orchestration tools like n8n 2.0 with agentic capabilities from Claude Code and Kiro-cli is transforming Kubernetes operations from reactive firefighting to proactive, autonomous management. By leveraging task runners for secure execution and headless agents for intelligent analysis, DevOps teams can build systems that not only detect failures like GPU OOM errors but actively repair them through advanced techniques like MIG reconfiguration. While new contenders like Sim AI and Lovable challenge the status quo, the immediate value lies in integrating these intelligent agents into established workflows, utilizing robust serving layers like KServe to power the very intelligence that keeps the lights on.