This article was co-authored with Shankar Ganeshan.
When you’re setting up a Kubernetes cluster, DevOps and Platform engineers feel like they’re navigating a maze. Take a simple service deployment: you might have to check the deployment (kubectl get deployment), the service (kubectl get service), inspect events (kubectl describe pod), the logs (kubectl logs pod-name), and even Ingress rules (kubectl get ingress). Each step requires context, copy-pasting names and ids from one place into another. A single typo can leave you scratching your head. Worse, incomplete information can lead you down the wrong path and waste hours. The complexity of multi-step diagnostics, especially when things go wrong, can be overwhelming.

​
Why Kubernetes troubleshooting still hurts and why we built k8x.

Even veteran SREs know the familiar pain cycle:
SymptomUsual manual steps
Pods in CrashLoopBackOff• kubectl get pods -A → copy failing names
• kubectl logs … (repeat per pod & container)
• grep for hints, cross-check ConfigMaps
503s on an Ingress• Check Service endpoints → kube-proxy rules → NetworkPolicy
• Compare readiness probes & resource pressure
”myservice works on most nodes”• Inspect taints/labels, daemonsets, CNI logs
• Describe nodes for kernel versions & allocatable
Each scenario is multi-step, context-heavy, and needs you to track context across multiple kubernetes commands. You need to keep a note of resource ids to use them in other resource descriptions and logs to know what’s going on.

​
The agentic leap: from suggestion to orchestrated review

Recent agents like GitHub Copilot Chat (in VS Code w/ terminal access), Claude Code (terminal-native edits) and Goose showed a new pattern: the LLM drives an interactive loop—executes safe commands autonomously, then narrates the findings. General-purpose LLM helpers (ChatGPT, Claude, Copilot Chat) can go beyond suggesting commands - they can copy-paste, re-run, and stitch results together. k8x applies this agentic idea to Kubernetes:
  • Natural-language prompts → e.g. “Find pods that aren’t ready and tell me why.”
  • The agent plans a sequence: kubectl get …, kubectl describe …, maybe kubectl top ….
  • It executes those read-only commands, parses output, and reasons about root causes.
  • Results appear as an explanation first, with raw command logs one keystroke away.
Unlike code-centric tools, k8x is infra-native. It understands resource kinds, status fields, events, and failure taxonomies (image pull, scheduling, OOMKilled).

​
Design choices that matter to operators

k8x works in your console with the your current kubectl configuration, to perform autonomous, multi-step workflows to detect and troubleshoot kubernetes issues with your credentials.
PrincipleExperienceWhy it builds trust
Read-only by default (v0.1)Zero risk of deletions; mirrors commands you could typeSafely trial AI before granting mutate rights ([GitHub][3])
Plain kubectl under the hoodFamiliar audit trail; works anywhere your kubeconfig doesNo proprietary sidecars or admission webhooks
Multi-LLM back-endSelect OpenAI, Claude, or Gemini at k8x configureAvoid vendor lock-in; keep traffic in-house
Command history & undok8x history list shows past sessionsAuditors see exactly what ran; SREs replay in staging
There’s more to come, including write permissions, parallelism, etc. Conrtributions are welcome.

​
A day in the life with k8x

# 1️⃣ Something is off
default$ k8x -c "my checkout service is returning 502s"

# 2️⃣ Agent plan (condensed)
• Check Ingress status
• Verify Service endpoints
• Scan pod readiness & logs
• Examine recent HPA events

# 3️⃣ Summary
❌ 2/5 endpoints unhealthy
↳ Pods stuck in Init:CrashLoopBackOff (db-migrations)
↳ Migration container fails on `ALTER TABLE …` (lock timeout)
Suggested fix: run `kubectl exec` into db-migration-pod or scale replicas to 0/1 to release lock
In ~30 seconds, you get an actionable story instead of fifteen manual commands.

​
How a multi-step review actually works

  1. Intent parsing - Translates English prompts into an internal diagnostic goal.
  2. Planning - LLM selects a safe chain of read-only kubectl queries.
  3. Adaptive execution - After each command, it decides if deeper queries are needed.
  4. Reasoning & templated explanations - Maps results to known issue patterns for a deterministic, auditable summary.
Only redacted command output reaches the LLM—no full application logs—another trust measure.

​
Where k8x stands in the AI-ops landscape

ToolDomainAutonomyLocal-first?Write access
GitHub Copilot ChatCode / CISuggests fixes, runs queries in UINoOptional PR commits
Claude CodeCode & CLI automationPlans & edits filesYesYes (file edits)
GooseMulti-agent dev tasksRuns terminal commandsYesYes
k8x (v0.1)Kubernetes operationsPlans & executes kubectl readsYesNo (read-only)
k8x fills the gap for platform and DevOps engineers looking for Copilot-level assistance after deployment, not just in CI/CD.

​
Getting started in 60 seconds

brew tap aihero/k8x
brew install k8x         # installs v0.1.1
k8x configure            # choose LLM & set API key
k8x -c "Are all pods running?"
You’ll never look at a 3-screen tmux layout the same way again.

​
Open Source

k8x is Apache 2.0-licensed and available on GitHub. We’re looking for contributors to help build out the next features, including:
  • v0.2 - Declarative fixes
    • Generate a patch plan (kubectl diff) and let humans --approve.
  • Support ArgoCD and other k8s tools
    • Integrate with ArgoCD for GitOps workflows.
    • Use kubectl apply to update resources based on agent suggestions.
  • Terraform & cloud-CLI mode
    • Run terraform plan or aws eks update-kubeconfig as sub-steps.
  • Cluster runbooks as code
    • Store successful sessions as YAML recipes to auto-trigger on alerts.

​
Final thoughts

Generative-AI agents are moving from IDEs into production infrastructure. By combining LLM planning, policy-guarded execution, and domain-specific reasoning, k8x transforms Kubernetes troubleshooting from a scavenger hunt into a guided review. Start with read-only diagnostics today; when you’re ready, the agent will apply fixes—one audited pull request at a time.

​
Get Connected, Share, and Other Socials

Share on LinkedIn

Share on X/Twitter

Have thoughts? I'd love to chat!

More about Rahul and Elevate.do

Follow Rahul on LinkedIn

Follow Rahul on Twitter/X