Shamsher Khan

In November 2025, I published a Bash script that analyzed Kubernetes clusters in about 60 seconds. It generated HTML reports, surfaced crash loops, orphaned resources, and other operational issues that were easy to overlook. The most interesting part wasn't the script — it was what happened after people started running it. Many told me they found problems they hadn't known existed. Looking back, the bash script wasn't really solving debugging. It was solving prioritization.

I built a Kubernetes operational triage dashboard after a bash script showed me engineers weren't missing data - they were missing prioritization.

Our first version was wrong 57% of the time. Not because the AI model couldn't identify Docker container failure scenarios—it usually could. The failures occurred at the decision boundary: determining when an automated action was appropriate, when escalation was required, and when no action should be taken. Over several weeks, we built and evaluated an AI-assisted remediation system on Docker MCP Gateway across four container failure scenarios, improving decision correctness from 43% to 100%.