I have been using Claude Code as my primary coding environment for infrastructure work for six months. This is not a getting-started tutorial — there are plenty of those. This is a practical guide to the workflow patterns that actually work for Terraform and Python DevOps tooling, and an honest account of where the friction still lives.
The diagram below shows the session loop I have settled on for Terraform work:
Most infrastructure automation is written as linear scripts: do this, then that, handle this error, exit. That model works fine for well-defined, predictable tasks. But when the task requires judgement — “figure out why this deployment is failing and fix it” — a linear script hits a wall.
LangChain agents offer a different model: a reasoning loop that can call tools, observe results, and decide what to do next. This tutorial builds a read-only Kubernetes diagnostic agent backed by Claude.
Runbooks rot. Every team knows it. The procedure that was accurate eighteen months ago now references a service that was renamed, a tool that was replaced, and a Slack channel that no longer exists.
A Retrieval-Augmented Generation (RAG) pipeline does not fix the rot, but it makes the good parts of your knowledge base dramatically more accessible — and it surfaces the rot so you can fix it.
This tutorial builds a complete RAG pipeline using LangChain, ChromaDB, and Claude that lets your on-call engineers ask questions in plain English and get cited answers from your internal runbooks.
Post-incident reports (PIRs) are one of those tasks that everybody agrees are valuable and nobody enjoys writing. After a long incident, the last thing an on-call engineer wants to do is reconstruct a timeline from Slack threads, runbooks, and half-remembered decisions at 2 AM.
This tutorial walks through the exact Python pipeline I built at DHL IT Services that reduced PIR drafting from 2–3 hours to a 15-minute review cycle.
When I first pitched GitHub Copilot to my team at DHL IT Services, the reaction was polite scepticism. We were a DevOps team, not software developers — what would an AI code assistant do for people who mostly write Terraform, Bash, and YAML all day? Turns out: quite a lot.
This article is a practical guide to adopting Copilot in a DevOps context — what to set up, what to expect, and where the boundaries are.