Tag: n8n

🚀 It started with a quiet notification at 3:14 AM—not an outage, but a billing alert. Our Kubernetes cluster in `us-east-1` had silently doubled its node count over the weekend, yet the application throughput hadn’t budged. The standard Horizontal Pod Autoscaler (HPA) was doing its job technically, but it was acting reactively to fragmented resource requests, spinning up expensive `m5.4xlarge` nodes for pods that requested 4GB of RAM but used 200MB. By the time the DevOps team logged in on Monday, we had burned through $4,000 in unnecessary compute. The traditional solution would be to tweak `requests` and `limits` manually or install a commercial tool like Karpenter or Cast AI. But today, we can build something far more adaptable: an autonomous rightsizing engine that doesn’t just react to metrics but plans capacity changes using the new reasoning capabilities of Kiro-cli 1.23.0 and the orchestration power of n8n 2.0.

🧠 The Orchestrator: n8n 2.0 and the AI Agent Node

The backbone of this autonomous system is the newly released n8n 2.0. While previous versions of n8n were excellent for linear automation, the 2.0 release introduces the AI Agent Tool Node, which fundamentally shifts how we handle logic. Instead of building rigid `If-Then` branches for every possible cluster state, we can now define a high-level objective—”Maintain cluster utilization above 80% without violating PDBs”—and let the agent decide the implementation details.

In our rightsizing architecture, n8n acts as the central nervous system. It ingests metrics from Prometheus via webhook and, crucially, connects to our ticketing system (JIRA) to understand context. A CPU spike during a known load test requires a different response than a spike during a quiet Sunday. The n8n 2.0 agent uses the LangChain integration to “think” before acting. It doesn’t just fire off a script; it first checks if a freeze period is active in Google Calendar (using the new native integrations) or if a deployment is currently rolling out.

Here is how we configure the primary Orchestrator Agent in n8n. Note the use of the `decision_maker` tool which wraps our policy logic:

# n8n AI Agent Definition (Simplified YAML representation)
agent:
  name: "ClusterCapacityManager"
  model: "claude-3-5-sonnet"
  temperature: 0.1
  system_prompt: |
    You are a Senior SRE responsible for cluster cost optimization.
    Do not disrupt production workloads.
    If utilization drops below 60% on any node pool, initiate a drain plan.
    Check the #ops-announcements Slack channel for maintenance windows first.
  tools:
    - name: "get_prometheus_metrics"
      description: "Fetches avg_cpu_usage and memory_pressure over 1h"
    - name: "check_freeze_window"
      description: "Returns true if we are in a deployment freeze"
    - name: "trigger_kiro_plan"
      description: "Delegates complex CLI tasks to Kiro-cli"

🤖 The Architect: Kiro-cli 1.23.0 and the Plan Agent

Once n8n identifies a candidate for rightsizing—say, a node pool that is heavily fragmented—it hands the tactical execution over to Kiro-cli. This is where the release of version 1.23.0 becomes critical. We utilize the new Plan Agent (accessible via `kiro-cli chat –agent plan` or Shift+Tab in the terminal), which is capable of breaking down a high-level directive into a multi-step execution strategy.

Standard scripts fail at rightsizing because they lack situational awareness. A script might try to drain a node that contains a single replica of a critical service with a strict Pod Disruption Budget (PDB), causing the drain to hang indefinitely. The Kiro Plan Agent, however, operates differently. It first queries the cluster state, identifies the PDBs, and then formulates a plan to cordon the node, scale up a replacement node in a cheaper pool, wait for readiness, and then evict the pods sequentially.

Crucially, we leverage the new MCP (Model Context Protocol) Registry support in Kiro 1.23.0. This allows Kiro to pull context from disparate sources without us needing to write custom API wrappers. We register a local MCP server that interfaces with our cloud billing API (AWS Cost Explorer or GCP Billing). This enables Kiro to “see” the dollar cost of the current nodes versus the target nodes.

# Kiro-cli MCP Configuration (~/.kiro/settings/mcp.json)
{
  "mcpServers": {
    "k8s-cost-estimator": {
      "command": "uvx",
      "args": ["mcp-server-cost-estimator", "--region", "us-east-1"],
      "env": {
        "AWS_PROFILE": "production-read-only"
      }
    },
    "argocd-inspector": {
      "command": "docker",
      "args": ["run", "-i", "--rm", "argocd-mcp:latest"]
    }
  }
}

With this configuration, the Kiro agent can reason: “Moving these 5 pods to spot instances will save $0.45/hour, but the `argocd-inspector` warns that these are stateful workloads. Aborting plan.” This level of autonomous “Safety II” thinking—where the tool focuses on what could go wrong—is what separates modern AI automation from brittle bash scripts.

⚙️ The Hands: Headless Claude and Multi-Session Support

While Kiro plans the strategy, the actual manipulation of manifests and GitOps repositories is handled by Claude Code in headless mode. The newest features in Claude Code allow for “Computer Use” capabilities, but for a DevOps pipeline, we prefer the headless CLI approach (`claude -p`). This allows us to pipe the output of Kiro’s plan directly into a Claude instance that has write access to our infrastructure repository.

We use Kiro’s Multi-session support to keep the context isolated. One session handles the “Safety Check” (scanning logs for errors), while a parallel session handles the “GitOps Commit”. If the Safety Session detects a regression in the canary deployment, it signals n8n to halt the Commit Session. This mimics the separation of duties between a QA engineer and a Release engineer.

In this workflow, Claude Code doesn’t just edit a YAML file; it refactors it. If we are moving from a standard Deployment to a KEDA-based ScaledObject, Claude understands the schema differences. It can verify that the new configuration matches the CRD (Custom Resource Definition) versions present in the cluster.

# Example Prompt for Headless Claude
claude -p "
Review the node_pool.yaml in the current directory.
Refactor the instance type from 'm5.2xlarge' to 't3.xlarge'.
Ensure that the 'taints' and 'tolerations' are preserved.
Run 'kubectl dry-run' to validate the manifest against the current cluster context.
If successful, commit the change to a new branch named 'optimization/node-pool-01'.
"

💡 The Future: Google Workspace Studio and Lovable

While the combination of n8n, Kiro, and Claude Code offers a powerful toolkit for engineering teams today, we must look at what is coming next. The release of Google Workspace Studio (Dec 2025) presents a threat—or an opportunity—to this bespoke approach. Workspace Studio allows non-technical users to build AI agents using natural language that live directly inside the Google ecosystem.

Imagine a Finance Director who doesn’t know what a Kubernetes pod is, but knows that the cloud bill is too high. Using Workspace Studio, they could create an agent simply by typing: “Monitor the monthly GCP invoice in Drive. If it exceeds $10,000, ask the Engineering Lead in Chat for a cost-saving plan.” This democratizes the trigger for automation, moving it out of Prometheus and into the business layer. Similarly, tools like Lovable are pushing the concept of “vibe coding,” where the entire dashboard for managing these operations is generated on the fly. Instead of maintaining a complex n8n dashboard, a DevOps engineer might simply prompt Lovable: “Build me a React admin panel that shows Kiro’s active plans and allows me to approve them with one click.” This suggests a future where the “glue” code we write in n8n is eventually replaced by transient, AI-generated applications tailored to the specific problem at hand.

💻 Conclusion

The convergence of n8n 2.0’s agentic nodes, Kiro-cli’s planning capabilities, and Claude Code’s headless execution creates a closed-loop system for Kubernetes operations that was previously impossible. We are moving away from static automation—scripts that break when the environment changes—toward predictive orchestration. By implementing the “Plan Agent” pattern, we ensure that our automated systems don’t just execute commands, but actually reason about the consequences of those commands against cost and stability constraints. For the DevOps engineer, the goal is no longer to write the script that drains the node, but to architect the agent that decides when and how to drain it safely.

🚀 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: true

Once 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: Never

This 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.

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.