Streamlining your Kubernetes operations with automated AI troubleshooting agents

Hey everyone,

Like many teams running Kubernetes, we noticed our platform/DevOps engineers spending hours every week triaging routine pod errors for developers—things like ImagePullBackOff, bad probes, or OOM crashes that developers didn't have the context to diagnose themselves.

To fix this, I built a full-stack Kubernetes AI Troubleshooting Agent that acts as an automated first responder.

Architecture & Tech Stack:

* Frontend: Next.js / React (modular cluster explorer & live investigation timeline)

* Backend: Python + FastAPI

* Cluster Diagnostics: Automated collectors inspecting pod states, events, streaming logs, deployment specs, and DNS/service endpoints via kubectl

* AI / Reasoning: OpenRouter-backed LLMs prompted with SRE-level diagnostic instructions

* Database & Auth: Insforge backend handling auth, RBAC-style cluster permissions, and investigation histories

* Local Dev: kind clusters running in Docker

How it works:

* A developer opens the dashboard and selects their authorized cluster/namespace.

* When an issue occurs, clicking "Investigate" kicks off an automated diagnostic run:

* Evaluates pod health & restarts

* Scrapes recent container logs & termination reasons

* Inspects Kubernetes events for scheduling/resource bottlenecks

* Verifies service endpoints and network policies

* The raw diagnostic payload is passed to the LLM engine.

* The agent returns:

* Root Cause Summary: Clear, plain-English explanation of why the failure occurred.

* Remediation Command: Concrete kubectl command or YAML patch.

* Confidence Score: e.g., 85% confidence if alternative configuration choices exist.

* Prevention Advice: Best-practice changes (e.g., CI linting, memory limit tuning).

Tested Failures:

Tested against invalid tags (ImagePullBackOff), misconfigured crash-loops, and CoreDNS readiness check issues. In all cases, it accurately flagged the error and saved ~15-20 minutes of manual log hunting per incident.

I'd love to get your thoughts: Have you implemented AI-assisted debugging in your clusters? What safety guardrails do you require before letting agents suggest or execute cluster mutations?