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Kaif Shakeel
Kaif Shakeel

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🧠 Meet Kagent: AI Agents That Run Inside Your Kubernetes Cluster

#devops #infrastructureascode #ai #productivity

We’ve been automating deployments, monitoring systems, and scaling infrastructure for years. But here’s a question:

Why are we still troubleshooting and fixing things manually?

That’s where Kagent comes in — an open-source framework to deploy smart, LLM-powered AI agents inside your Kubernetes cluster.

Let’s break it down 👇

😵 Why Do We Need Something Like Kagent?

If you've managed a Kubernetes environment, you’ve probably:

  • Spent hours tracing failed network hops
  • Dug through endless logs to debug an error
  • Tried (and failed) to make Prometheus alerts “smart”
  • Wrestled with ArgoCD when a rollout broke something

The problem? Too much tribal knowledge, too many manual steps, and not enough automation for troubleshooting and ops intelligence.

🤖 What Exactly Is Kagent?

Kagent is a framework that brings autonomous AI agents to Kubernetes.

These aren’t just scripts or bots. These are LLM-powered agents that:

  • Read your prompt (e.g. “why is this service slow?”)
  • Plan their steps
  • Use tools like kubectl, Prometheus, or ArgoCD
  • Act, analyze results, and keep refining their approach

All from inside your cluster. All Kubernetes-native.

🔍 What Can Kagent Actually Do?

Here are some real things you can do with Kagent:

  • Diagnose why a service can’t connect to another
  • Query Prometheus to understand app performance
  • Debug traffic issues in Istio gateways
  • Run safe, progressive rollouts using Argo
  • Build your own custom AI agents to solve your platform pain points

It’s like giving your platform superpowers 🦸

✅ Why You Might Love It

  • Built for Kubernetes: Agents, tools, and logic run as native CRDs
  • Declarative agents: Define behavior in YAML, manage like any other K8s object
  • Extensible: Comes with tools for kubectl, Prometheus, Istio, Argo — but you can plug in more
  • Multi-agent teamwork: Agents can delegate tasks to other agents (like an AI SRE team)
  • UI + CLI: Interact through terminal or a slick web UI

⚠️ What to Watch Out For

  • Still early-stage — some features are WIP (like telemetry and testability)
  • Needs a reliable LLM backend (OpenAI, Claude, etc.)
  • Not 100% bulletproof — AI might hallucinate, and prompt design matters
  • No mature debugging or evaluation tools yet (coming soon)

💡 What Could Make It Even Better?

Kagent has a solid roadmap. Some exciting ideas include:

  • Tracing and observability baked in (via OpenTelemetry)
  • Better test frameworks to verify agents before production
  • Graph-based workflows instead of just prompt-response
  • Multi-LLM support (Ollama, Claude, Mistral, etc.)
  • Easy sharing of reusable agent templates for the community

🛠️ Hands-On: Your First Kagent in One Shot

Let’s get a working Kagent agent up and running in your cluster from start to finish.

🔧 Step 1: Install Kagent on Your Cluster 

helm repo add kagent https://kagent.dev/helm
helm install kagent kagent/kagent
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🧠 Step 2: Define an AI Agent in YAML

Create a file named agent.yaml

apiVersion: kagent.dev/v1alpha1
kind: Agent
metadata:
  name: diagnose-network
spec:
  systemPrompt: "You are a Kubernetes troubleshooter."
  tools:
    - name: kubectl
  model:
    provider: openai
    model: gpt-4
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This agent will use GPT-4 to analyze networking issues inside your cluster using kubectl.

🚀 Step 3: Apply the Agent to Your Cluster 

kubectl apply -f agent.yaml
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💬 Step 4: Quick Start It with a Natural Language Prompt 

kagent >> run chat [agent-name] [session-name] [initial-task]"
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The agent will:

  • Parse the prompt
  • Plan troubleshooting steps
  • Execute kubectl commands
  • Analyze the results
  • Return a detailed, intelligent response

🔍 Step 5: Check the Agent’s Execution Logs

You can inspect what the agent did by running:

kubectl get agentruns
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Then get logs from a specific run:

kubectl logs agentrun/<run-name>
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Or open the web UI if you're using it.

🚀 Final Thoughts

Kagent brings intelligent agents to where the real action is — your Kubernetes cluster.
Instead of just automating infra setup, it’s automating the ops smarts that usually live in your brain, Notion docs, or Slack threads.
If you’re a DevOps engineer, platform nerd, or AI enthusiast, now’s the time to explore what agentic operations can do for you.

👨‍💻 Try it: https://kagent.dev
📦 GitHub: https://github.com/kagent-dev/kagent

Let me know if you build something cool with it — or if you want a hand writing your first agent!
Want more DevOps + AI breakdowns like this? Follow me here 👇
💬 Comments welcome!

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