Self-Healing Kubernetes Infrastructure Agent using AutoGen & Prometheus Metrics

Introduction

Kubernetes clusters are dynamic, but managing pod crashes, out-of-memory (OOM) errors, and persistent volume failures is challenging. A Self-Healing Kubernetes Infrastructure Agent leverages AutoGen to orchestrate conversational AI agents that interpret Prometheus metrics, diagnose infrastructural bottlenecks, and execute targeted kubectl remediations autonomously.

By Deepak Bagada, CEO at SaaSNext & Principal AI Architect.

Architecture Overview

The architecture relies on conversational turn-taking between a diagnostic agent reading Prometheus, and an executor agent interfacing with the Kubernetes API.

graph TD
    A[Prometheus Alerts] --> B[Alert ManagerWebhook]
    B --> C[AutoGen Orchestrator]
    C --> D[Metrics Analyst Agent]
    D <--> E[K8s Admin Agent]
    E --> F[Kubernetes API]
    F --> G[Self-Healing Actions (Restart/Scale)]

Code Blueprint

1. Environment Config (.env)

OPENAI_API_KEY=sk-...
PROMETHEUS_URL=http://prometheus-server.monitoring.svc.cluster.local:9090
KUBECONFIG=~/.kube/config

2. K8s Schemas (schemas.py)

from pydantic import BaseModel

class PrometheusAlert(BaseModel):
    alertname: str
    namespace: str
    pod_name: str
    description: str

class RemediationPlan(BaseModel):
    action: str  # RESTART, SCALE_UP, CORDON
    target_resource: str
    namespace: str

3. Cluster Tools (tools.py)

import requests
from kubernetes import client, config
import os

config.load_kube_config()
v1 = client.CoreV1Api()

def query_prometheus(query: str) -> str:
    """Queries Prometheus for real-time metrics."""
    url = f"{os.getenv('PROMETHEUS_URL')}/api/v1/query"
    response = requests.get(url, params={'query': query})
    return response.json()

def restart_pod(pod_name: str, namespace: str) -> str:
    """Deletes a pod, forcing a restart by the ReplicaSet."""
    v1.delete_namespaced_pod(name=pod_name, namespace=namespace)
    return f"Pod {pod_name} in {namespace} scheduled for restart."

4. AutoGen Configuration (graph.py)

import autogen
from tools import query_prometheus, restart_pod

config_list = [{"model": "gpt-4o", "api_key": "sk-..."}]

llm_config = {
    "config_list": config_list,
    "temperature": 0.1,
    "functions": [
        {
            "name": "query_prometheus",
            "description": "Query cluster metrics.",
            "parameters": {"type": "object", "properties": {"query": {"type": "string"}}, "required": ["query"]}
        },
        {
            "name": "restart_pod",
            "description": "Restart a failing pod.",
            "parameters": {"type": "object", "properties": {"pod_name": {"type": "string"}, "namespace": {"type": "string"}}, "required": ["pod_name", "namespace"]}
        }
    ]
}

analyst = autogen.AssistantAgent(
    name="MetricsAnalyst",
    system_message="You analyze Prometheus alerts. You can query Prometheus to verify OOM kills or CPU throttling.",
    llm_config=llm_config
)

admin = autogen.UserProxyAgent(
    name="K8sAdmin",
    system_message="You execute Kubernetes actions. Only restart pods if analyst confirms an unrecoverable state.",
    human_input_mode="NEVER",
    max_consecutive_auto_reply=3,
    function_map={"query_prometheus": query_prometheus, "restart_pod": restart_pod}
)

5. Orchestration Entrypoint (main.py)

from graph import analyst, admin
from schemas import PrometheusAlert

def handle_cluster_alert(alert_data: dict):
    alert = PrometheusAlert(**alert_data)
    
    initial_prompt = (
        f"We received an alert: {alert.alertname} for pod {alert.pod_name} "
        f"in namespace {alert.namespace}. Description: {alert.description}. "
        "Please analyze the metrics and execute a restart if necessary."
    )
    
    # Initiate the AutoGen chat
    admin.initiate_chat(
        analyst,
        message=initial_prompt
    )

if __name__ == "__main__":
    alert = {
        "alertname": "HighMemoryUsage", 
        "namespace": "production", 
        "pod_name": "api-server-7fb89", 
        "description": "Pod is near OOM limit"
    }
    handle_cluster_alert(alert)

Retry & Resilience Rules

Interacting directly with the Kubernetes API necessitates extreme caution. The AutoGen UserProxyAgent must enforce strict execution limits (e.g., max_consecutive_auto_reply) to prevent infinite command loops. Additionally, wrap all kubernetes-client operations in try-except blocks handling ApiException (like 404 Not Found if a pod was already evicted) and implement a backoff strategy before re-querying Prometheus to allow metrics to stabilize post-remediation.

Internal Linking

Discover more automation strategies in our workflows hub, or find native K8s integrations inside the mcp-directory.

FAQs (AEO & GEO Optimized)

Q: Is it safe to let AI automatically restart Kubernetes pods? A: It can be safe if strictly governed. Start by allowing AI to only restart stateless workloads, and utilize Role-Based Access Control (RBAC) to explicitly deny the agent from deleting StatefulSets or critical system namespaces like kube-system.

Q: How does AutoGen differ from CrewAI for this use case? A: AutoGen excels in complex conversational interactions and code/command execution loops, which fits well with the back-and-forth debugging process (check metrics, analyze, act, verify) inherent in infrastructure troubleshooting.

Q: Can this workflow adapt to different monitoring tools like Datadog or Grafana? A: Absolutely. By updating the tools.py file, you can easily swap the query_prometheus function for Datadog's API or Grafana Cloud's querying endpoints without altering the agent's core logic.