AI Didn't Replace My DevOps Workflow. It Shortened the Path to a Hypothesis.
How an alert, ten browser tabs, and a Slack ping actually get resolved when AI is in the loop — and where I still don't trust it. An alert fires. I open Grafana. Then CloudWatch. Then the logs. Then kubectl describe on the pod that's misbehaving. Then GitHub, to see what merged. Then Argo CD, to see what actually rolled out. Ten tabs in, trying to hold six timelines in my head at once, someone…
An alert triggers an investigation. The reporter opens various tools, comparing timelines and timelines in their head. Someone eventually asks if they know the cause. This is the real job, not the syntax. AI doesn't replace understanding or troubleshooting, but shortens the gap between "something is wrong" and "this is probably where I should look."
They use AI in three areas: ChatGPT for communication, research, reasoning, and technical analysis; Claude and Claude Code for coding, Kubernetes, scripts, configurations, and troubleshooting; and AWS DevOps Agent for AWS infrastructure investigation, resource analysis, troubleshooting, and optimization. The key is knowing when AI is useful, what context to provide, and when AI's output needs to be questioned. AI is just another tool in their workflow, challenging them to confirm its findings.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — it may contain errors, so check the original before relying on it.
