Latest Posts
AI vs Manual: Kubernetes Troubleshooting Showdown 2025
It’s 3 AM. Your phone buzzes. Production is down. A Pod won’t start. You run kubectl events, wade through hundreds of normal events to find the one warning that matters, describe the Pod, check the ReplicaSet, trace back to the Deployment, realize a PersistentVolumeClaim is missing, write the YAML, apply it, validate the fix. Thirty minutes later, you’re back in bed, wondering if there’s a better way.
There is. What if AI could detect the issue, analyze the root cause, suggest a fix, and validate that it worked? What if all four phases happened automatically, or at least with your approval, while you stayed in bed?
I’m going to show you exactly how to do this with Kubernetes. First, we’ll walk through the manual troubleshooting process so you understand what we’re automating. Then I’ll show you an AI-powered solution using Claude Code and the Model Context Protocol that handles detection, analysis, remediation, and validation. Finally, we’ll look under the hood at how the system actually works.
AI Agent Architecture Explained: LLMs, Context & Tool Execution
You type “Create a PostgreSQL database in AWS” into Claude Code or Cursor, hit enter, and boom - it just works. Database created, configured, running. Like magic.
But it’s not magic. Behind that simple request is an intricate dance between you, an orchestrator called an agent, and a massive language model. Most people think the AI is doing everything. They’re wrong. The AI can’t touch your files, can’t run commands, can’t do anything on its own.
So how the hell does it work? How does your intent turn into actual results? That’s what we’re going to break down. The real architecture. The three key players. And why understanding this matters if you’re using these tools every day.
Best AI Models for DevOps & SRE: Real-World Agent Testing
You’re a software engineer. Maybe you’re doing DevOps, SRE, platform engineering, or infrastructure work. You’re using large language models, or at least you should be. But which ones? How do you know which model to pick?
I was in the same situation. I made choices based on gut feelings, benchmark scores that didn’t mean anything in production, and marketing claims. I thought I should change that.
So I ran ten models from Google, Anthropic, OpenAI, xAI, DeepSeek, and Mistral through real agent workflows. Kubernetes operations. Cluster analysis. Policy generation. Systematic troubleshooting. Production scenarios with actual timeout constraints. And the results were shocking compared to what benchmarks and marketing promised.
Seventy percent of models couldn’t finish their work in reasonable time. A model that costs 120 dollars per million output tokens failed more evaluations than it passed. Premium “reasoning” models timed out on tasks that cheaper models handled easily. Models everyone’s talking about couldn’t deliver reliable results. And the cheapest model? It delivered better value than options costing twenty times more.
By the end of this article, you’ll know exactly which models actually work for engineering and operations tasks, which ones are unreliable, which ones burn your money without delivering results, and which ones can’t do what they’re supposed to do.
Self-Healing Kubernetes: When to Use AI vs Traditional Automation
It’s 2 AM Saturday. You’re sleeping when your phone rings. You wake up ready to answer with “go to hell you prick,” but then you see it’s PagerDuty. Pods are crashing in production. You drag yourself to your laptop, still half-asleep, and start digging. Events. Logs. Metrics. Configurations. Forty minutes later, you’ve found it: an out-of-memory condition. The fix takes thirty seconds. A simple memory limit adjustment. Something that could have been detected and fixed automatically while you slept.
This doesn’t have to be your reality. Here’s what you’ll learn: how to build automation that watches Kubernetes events, analyzes problems, and remediates issues before they ruin your weekend. We’ll cover when traditional automation works, when AI adds value, and how to progressively mature your incident response from manual firefighting to intelligent self-healing systems.
The foundation is understanding events themselves.
MCP Server Deployment Guide: From Local To Production
Everyone’s using MCP servers these days. They’re connecting AI agents to databases, Kubernetes clusters, GitHub repos, cloud resources, you name it. MCP is becoming the standard way to give AI tools access to external systems. But here’s the question: how should you actually run these MCP servers?
The documentation typically shows you one way: running it locally with NPX. But is that secure? Is it scalable? Can your team share it? What about production? There are actually multiple deployment options, each with different trade-offs.
So here’s what we’re going to do: I’ll show you four different ways to deploy MCP servers, from the dead simple to the enterprise-ready. We’ll look at local execution with NPX, Docker containers, Kubernetes deployments, and operator-managed resources. Plus, I’ll cover a few notable cloud platforms like Fly.io, Cloudflare Workers, and AWS Lambda. For each approach, I’ll show you exactly how it works, what problems it solves, and what new problems it creates.
Why Your Infrastructure AI Sucks (And How to Fix It)
Here’s the harsh reality: your AI agent is completely useless for infrastructure management, and you probably don’t even realize it yet.
You’ve probably tried throwing ChatGPT or Claude at your DevOps problems, thinking AI will magically solve your infrastructure challenges. Maybe you got some generic responses that looked helpful on the surface. But when you actually tried to implement those suggestions, you discovered the painful truth - the AI has no clue about your environment, your standards, or your constraints.
Most organizations are making the same critical mistake: they’re treating AI like a search engine instead of building it into their platform properly. They ask vague questions, get generic answers, and wonder why their “AI transformation” isn’t working.