How AI Boosts Kubernetes Cluster Inspection with k8s‑cluster‑inspector

Introduction

The k8s‑cluster‑inspector component can automatically perform Kubernetes cluster inspections and return issues with categories, types, and remediation suggestions.

Example Inspection Data Structure

{
  "name": "colocation-webhook-role",
  "namespace": "archimedes",
  "kind": "Role",
  "message": ["CanDeleteResources"],
  "issueCategory": ["Failed to pull image:WorkLoad"],
  "normal": false,
  "harmRank": ["CanDeleteResources:高危"],
  "repairMessages": ["CanDeleteResources:rbac中存在删除用户的风险，修复：在role,clusterrole增加标签：kubernetes.io/bootstrapping='rbac-defaults'"]
}

Without AI assistance, the answers to these issues are limited, which can reduce user trust and hinder verification in production environments.

AI‑Enhanced Inspection Results

{
  "name": "colocation-webhook-role",
  "namespace": "archimedes",
  "kind": "Role",
  "message": ["CanDeleteResources"],
  "issueCategory": ["Failed to pull image:WorkLoad"],
  "normal": false,
  "harmRank": ["CanDeleteResources:高危"],
  "repairMessages": [
    "CanDeleteResources:集群中遇到镜像拉取报错401，通常是因为镜像仓库需要认证。解决方法如下：",
    "1. 创建镜像拉取 Secret：在 Kubernetes 中创建一个 Secret 对象，用于存储镜像仓库的用户名和密码。",
    "```yaml
kubectl create secret docker-registry regcred --docker-server=<your-registry-server> --docker-username=<your-name> --docker-password=<your-pword> --docker-email=<your-email>
```",
    "2. 在 Pod 或 Deployment 中引用 Secret：在配置文件中通过 `imagePullSecrets` 字段引用上述 Secret。",
    "```yaml
apiVersion: v1
kind: Pod
metadata:
  name: private-reg
spec:
  containers:
  - name: private-reg-container
    image: <your-private-image>
  imagePullSecrets:
  - name: regcred
```",
    "3. 应用配置：更新或创建 Pod/Deployment。",
    "```bash
kubectl apply -f your-pod-or-deployment.yaml
```",
    "这样，Kubernetes 在拉取镜像时会使用指定的认证信息，避免 401 错误。"
  ]
}

The AI‑augmented data shows clear improvements, offering richer, professional answers that enhance product value and user experience.

System Workflow

Execute inspection template according to user‑declared commands.

Start inspection, interacting with the Kubernetes cluster.

Render inspection results: call the AI interface, assemble prompts and context, send cluster issues to the AI, and merge AI responses into the result set.

Report results: upload the final inspection report to the user‑specified OSS storage or local destination.

Provider Design

The AI provider is designed as a pluggable component, allowing multiple AI services to be integrated. Each provider only needs to implement its invocation method, greatly increasing flexibility for external delivery.