🚀 It was 3:00 AM when the pager screamed. Our primary inference cluster had stalled. A new Llama 3 deployment had effectively seized every available GPU cycle, causing a cascade of Out of Memory (OOM) errors across the critical production namespace. The on-call engineer—me—stared at the terminal, watching pods crash loop while the queuing system overflowed. In the past, this meant manually cordoning nodes, calculating VRAM fragmentation, and reshuffling workloads like a frantic game of Tetris. But this time, I sat back. I watched as an autonomous workflow triggered by the alert didn’t just restart the pods, but actually analyzed the VRAM usage patterns, determined that the NVIDIA A100s were inefficiently partitioned, and dynamically re-sliced the GPUs using Multi-Instance GPU (MIG) profiles. By 3:05 AM, the cluster was healthy, the model was serving traffic, and I hadn’t typed a single command. This wasn’t magic; it was the new reality of **Kubernetes AI deployment** managed by agentic automation.
🤖 The New Agentic Stack: n8n 2.0, Kiro-cli 1.23.0, and Headless Claude
The convergence of workflow automation and autonomous agents is reshaping how we approach DevOps. We are moving beyond simple scripts that react to webhooks and toward intelligent systems that can plan and execute complex remediation strategies. At the heart of this shift is the release of n8n 2.0. NetworkChuck has famously demonstrated how n8n can function as an “IT Department” employee named Terry, capable of monitoring homelabs and executing SSH commands. However, for enterprise Kubernetes operations, we need to take this concept further. n8n 2.0 introduces “Task Runners,” which isolate code execution for enhanced security and stability—a critical feature when your automation has write access to your production cluster.
While n8n acts as the central nervous system, listening for alerts from Prometheus or Kube-State-Metrics, the actual “brain” requires more cognitive reasoning. This is where Kiro-cli version 1.23.0 shines. The latest release introduces a dedicated “Plan Agent” and support for “Subagents.” In our architecture, n8n doesn’t just run a script; it initializes a Kiro-cli session that delegates the problem to specialized subagents. One subagent might specialize in log analysis using KServe vs Seldon debugging protocols, while another specializes in resource negotiation. Furthermore, the new MCP (Model Context Protocol) registry in Kiro-cli allows these agents to securely query your internal documentation or architecture diagrams to understand why the cluster is configured a certain way before making changes.
Completing this triad is Claude Code, specifically its new headless capabilities. By running `claude -p` in a headless environment, we can embed Anthropic’s reasoning directly into our CI/CD pipelines or remediation jobs. Unlike standard CLI tools, Claude Code can act as an operator, navigating the Kubernetes API with the nuance of a human engineer. It can verify that a **GPU slicing** operation won’t destabilize neighboring tenants—a level of context awareness that static bash scripts simply cannot match. This mirrors the evolution of AI capabilities, much like the Comparison of 2011 Watson AI vs modern ChatGPT 5.0 capabilities, where we have moved from rigid rule-based systems to fluid, reasoning engines.
🧠 Implementing Intelligent GPU Slicing with NVIDIA MIG
Let’s dive into the specific example of solving the OOM error through dynamic **NVIDIA MIG** partitioning. In a traditional setup, if a model requires 12GB of VRAM and acts as a greedy process, it might lock an entire 80GB A100 card, wasting massive amounts of capacity. To fix this automatically, we need an agent that can recognize the waste and command the infrastructure to slice the GPU into smaller, isolated instances.
The workflow begins in n8n 2.0. We use a webhook node to catch the alert payload from Alertmanager. This payload contains the pod name, the namespace, and the specific exit code (OOMKilled). n8n then formats this context and triggers a Kubernetes Job via the K8s API node. This job spins up a container with Kiro-cli installed. The magic happens when Kiro-cli utilizes its Plan Agent. It doesn’t just try to restart the pod; it drafts a remediation plan. It might decide: “Current profile 1g.10gb is insufficient. Promoting to 2g.20gb slice required. Migrating adjacent workloads to Node B to free up capacity.”
Here is how you might configure the Kiro-cli agent within your orchestration container. This configuration leverages the new subagent capability to separate the planning phase from the execution phase, ensuring that a “check” step occurs before any destructive command.
# kiro-agent-config.yaml
version: '1.23.0'
agent:
name: "GPU-Remediation-Specialist"
role: "SRE"
capabilities:
- "kubectl"
- "nvidia-smi"
subagents:
- name: "CapacityPlanner"
model: "claude-3-5-sonnet"
goal: "Analyze GPU fragmentation and propose MIG profile updates."
- name: "Executioner"
model: "claude-3-haiku"
goal: "Safe execution of kubectl patch commands."
mcp_registry:
- "k8s-documentation-internal"
- "prometheus-metrics-adapter"
safety_policy:
require_approval: false
dry_run_first: trueOnce the plan is generated, the Executioner subagent interacts with the cluster. It leverages the headless mode of Claude Code to construct the complex JSON patches required to update the node’s MIG configuration. This is far more reliable than regex-based bash scripts because the agent understands the schema validation of Kubernetes manifests.
⚙️ The Technical Workflow: From Alert to Resolution
Nick Puru of AI Automation often discusses the transition from simple chatbots to full “AI Systems.” This workflow exemplifies that shift. We are not just chatting with an AI; we are building a system where the AI interacts with the metal. The n8n 2.0 workflow acts as the orchestrator, managing the state and history of the incident. It uses the new “Multi-session support” in Kiro-cli to maintain context. If the remediation fails, Kiro remembers the previous attempt and tries a different strategy (e.g., scaling up the node pool instead of slicing) without needing to re-analyze the logs from scratch.
Below is an example of the Kubernetes Job manifest that n8n would deploy to trigger this agentic intervention. Notice how we mount the secure credentials and define the specific scope for the agent. We utilize the NVIDIA MIG capability to ensure our AI workloads are isolated.
apiVersion: batch/v1
kind: Job
metadata:
name: auto-healer-gpu-001
namespace: ops-automation
spec:
template:
spec:
serviceAccountName: ai-remediation-sa
containers:
- name: kiro-agent
image: kiro-cli:1.23.0
command: ["kiro", "run", "--headless", "--plan", "fix-oom-strategy"]
env:
- name: TARGET_POD
value: "llama3-inference-worker-x92z"
- name: CLAUDE_API_KEY
valueFrom:
secretKeyRef:
name: ai-secrets
key: anthropic-key
volumeMounts:
- name: agent-config
mountPath: /etc/kiro
volumes:
- name: agent-config
configMap:
name: kiro-remediation-config
restartPolicy: NeverThis job runs the `kiro run` command, which utilizes the agent configuration we defined earlier. The `–plan` flag activates the new Plan Agent, ensuring the AI thinks before it acts. This is a crucial practical deployment strategy: always decouple the reasoning (Plan Agent) from the doing (Execution Agent) to prevent runaway automation.
For the actual resource slicing, the agent might apply a patch to the node triggers a reconfiguration of the Time-Slicing or MIG profiles. It is vital to understand the underlying infrastructure, such as KServe for deploying ML and AI models on Kubernetes, because the agent needs to know if it should drain the node before applying changes. If you are running KServe, the inference service abstraction adds another layer of complexity that the agent must navigate.
🔨 Beyond n8n: The Rise of Lovable and Next-Gen Interfaces
While n8n 2.0 is a powerhouse for backend logic, the interface for interacting with these agents is also evolving. Nick Puru highlights tools like “Lovable” as the next step after n8n for certain use cases. Lovable allows you to generate full-stack applications from prompts. In our context, we can use Lovable to build the dashboard that DevOps engineers use to oversee these autonomous agents. Instead of staring at kubectl logs, an engineer could use a Lovable-generated React app that visualizes the “thought process” of the Kiro-cli agent in real-time.
Imagine a control plane where you see the “Plan” generated by Kiro’s Plan Agent displayed in a clean UI, with “Approve” and “Reject” buttons. This “Human-in-the-Loop” design is essential for building trust in AI automation. The backend logic is still handled by the robust n8n 2.0 orchestration, but the frontend interaction is streamlined by tools like Lovable. This hybrid approach—n8n for the plumbing, Kiro/Claude for the intelligence, and Lovable for the interface—represents the future of internal developer platforms (IDPs).
Furthermore, as we look at replacing manual network operations, the headless nature of Claude Code allows us to integrate these checks into pull requests. If a data scientist submits a PR changing a model’s batch size, a headless agent can spin up an ephemeral environment, test the memory pressure, and comment on the PR with a recommendation to adjust the GPU slicing profile, all before a human reviewer even sees the code.
💻 Conclusion
The era of manually troubleshooting Kubernetes clusters is coming to an end. By combining the structured orchestration of n8n 2.0 with the reasoning capabilities of Kiro-cli 1.23.0 and Claude Code, we can build systems that not only heal themselves but optimize their own capacity. The move to **NVIDIA MIG** and dynamic **GPU slicing** driven by agents allows organizations to squeeze every ounce of value from their expensive hardware. While tools like Lovable promise to simplify the interface, the core value lies in the intelligent workflows we design today. As DevOps engineers, our role is shifting from fixing the machine to designing the machine that fixes itself. We are no longer just operators; we are the architects of autonomous infrastructure.