How to compose kprompt with kagent without collapsing the layers: kagent hosts Agents-as-CRDs via MCPServer / RemoteMCPServer; kprompt ships read/plan-only MCP tools that return a typed PlanResult and never auto-apply. Validated against kagent quickstart + first MCP tool docs.
Day-2 OpenTelemetry with kprompt: bind Jaeger or Tempo, walk a span tree, narrate bottleneck waits, enrich service graphs with call edges when traces exist, and degrade honestly when OTel is missing. Not a Jaeger UI and not auto-remediation.
Day-2 Argo CD with kprompt: detect Application CRDs, show gitops sync status, investigate drift vs Git, approve-gated sync/promote/rollback through the real Application API, and optional --gitops PR mode instead of live reconcile. Flux is the peer engine on the same contract. Not an Argo UI and not silent sync.
kagent (CNCF Sandbox / Solo.io) is a Kubernetes-native agent runtime — Agents as CRDs, MCP, A2A, mesh. kprompt is an AI Runtime for cluster ops: PlanResult → approve, plus Observe notify. Is kprompt a kagent alternative? Only for the ops job — decision guide.
Use kprompt against GKE the same way you use kubectl — get-credentials, aliases, plan-before-apply — plus Gemini BYOK. Optional Observe agent on the cluster; no Marketplace SaaS, no kubeconfig upload.
Use kprompt against Amazon EKS the same way you use kubectl — update-kubeconfig, aliases, plan-before-apply — plus Ollama or cloud BYOK. Optional Observe agent on the cluster; no Marketplace SaaS, no kubeconfig upload. Native Bedrock preset still deferred.
Looking for a kagent alternative? Map by job: kprompt for plan-before-apply day-2 + Observe notify, kubectl-ai for NL kubectl REPL, K8sGPT for diagnosis, ARK for agent apps as CRDs — plus when to keep CNCF kagent.
agentgateway (Linux Foundation) is an AI-native proxy for LLM, MCP, and A2A traffic on Gateway API. kprompt is an AI Runtime for cluster ops: PlanResult → approve, plus Observe notify. Is kprompt an agentgateway alternative? Only for the ops job — decision guide.
Looking for an agentgateway alternative? Map by job: kprompt for plan-before-apply day-2 + Observe notify, other AI/MCP gateways for LLM traffic policy, kagent for Agents-as-CRDs — plus when to keep Linux Foundation agentgateway.
Use kprompt against Azure Kubernetes Service the same way you use kubectl — az aks get-credentials, aliases, plan-before-apply — plus Azure OpenAI BYOK or Ollama. Optional Observe agent on the cluster; no Marketplace SaaS, no kubeconfig upload.
Three Kubernetes AI layers that share vocabulary and confuse buyers: AI Runtime (kprompt PlanResult ops), AI Gateway (agentgateway LLM/MCP/A2A data plane), Agent Platform (kagent Agents-as-CRDs). One hub, honest jobs, deep links.
Day-2 Prometheus with kprompt: bind an existing Prom URL, read-only performance explain (CPU, memory, p95, HPA), optimize idle/rightsizing, and fail clear when metrics are missing. Not a PromQL IDE and not auto-remediation.
A tagged hub of ~100 real Kubernetes ops prompts—Operations, Debug, GitOps, Observability, Security, and Multi-cluster—each with a kubectl or tooling note, linked to our cheat sheet, error playbook, and edge-case guide.
kprompt mcp serve exposes read and plan tools to Cursor, Claude Desktop, and other IDE assistants over stdio. Mutations return a PlanResult and never auto-apply. IDE interop, not an agent platform.
Starting from zero with kind is easy. The real challenge is attaching kprompt to a cluster you already run: bind existing Prometheus, read-first insight, optional MCP — install last.
A real on-call walkthrough: checkout pods stuck in CrashLoopBackOff, how we read the previous logs and events with kprompt, and why the fix still waited for human approve.
Why unsupervised auto-remediation is not the destination. Observe by default, Autopilot propose-only, reality anchors, and investigate → plan → approve → verify as the load-bearing loop — not a fleet of agents that apply because the model sounded sure.
Read-only service topology as typed nodes/edges — Ingress, PVC, Secret/ConfigMap names, reverse impact — not a Secret CMDB or chat “who depends on what” folklore. Honest degraded when OTel/mesh are missing.
Namespace dependency facts that bias Observe without becoming root-cause proof. Local or in-cluster stores — never cloud dumps as fake authority — with AG-034 confidence caps.
Incident chronology as typed EvidenceRef[], not chat folklore. Events, rollout revisions, and HPA in one Investigation artifact — with honest degraded[] when Prom/OTel/mesh are missing.
Chat is a line; ops is a gated graph. Fan-out where edges are real, independent verify (not same-session soft-agree), PlanResult → approve → verify — without becoming a free-form agent fleet.
kubectl vs K9s (and k9s vs kubectl): kubectl is the scriptable API client; K9s is a live terminal UI over the same API. When to use each in incidents, CI, and day-2 ops — plus where AI CLIs fit.
A plain-language map of Kubernetes AI: analyzers like K8sGPT, intent CLIs like kubectl-ai and kprompt, in-cluster agents, and what belongs in CI vs on your laptop — for searches like kubernetes ai, k8s ai, and k8s ai tools.
Practical shortlist of AI tools for Kubernetes troubleshooting: K8sGPT for scans, intent CLIs for explain/plan, kubectl for evidence — with an honest mutation checklist.
What “chat with your Kubernetes cluster” means in practice: local NL CLIs, hosted consoles, and analyzers — and how to keep credentials and apply behind a human gate.
How to use AI on Kubernetes Pods safely: explain CrashLoopBackOff and ImagePullBackOff with kubectl, K8sGPT, or an intent CLI — without piping model output straight into apply.
People searching Kubegpt usually mean K8sGPT — the analyzer-first Kubernetes AI tool. How it differs from kubectl-ai and plan-before-apply CLIs, and when each job fits.
McKinsey’s ARK is an Agentic Runtime for Kubernetes — CRDs to run agent apps on the cluster. kprompt is an AI Runtime that reasons about the cluster under plan → approve. Same word “runtime,” different jobs. Decision guide.
Why kprompt’s category is an AI Runtime for Kubernetes: observe, reason, plan, approve, execute, learn — not a ChatGPT wrapper, chatbot, or silent auto-healer. Honest shipped vs building.
For Kubernetes AI, clever prompts lose to curated context: live cluster facts, tool detection, local history, and a typed PlanResult. Why the next leap is what you feed the model — not how you word the sentence.
AI SRE across kubeconfig contexts: single-context default, explicit read fan-out, per-context mutate approval, aliases, and why we refuse silent fleet --approve or uploading cluster credentials.
CrashLoopBackOff is a symptom, not a cause. How the restart backoff works, what exit codes tell you, the kubectl ladder for finding the real failure, and how to apply a bounded fix you actually reviewed.
ImagePullBackOff means the container never started. How ErrImagePull differs from CrashLoopBackOff, what Events tell you, common causes (bad tag, private registry, rate limit), and how to fix it without guessing.
Policy is code, not LLM vibes. How kprompt’s safety engine hard-denies wipe-class intents, scores risk, forces approval, and why fail-closed is the load-bearing wall of AI SRE.
PlanResult is the IR of AI SRE: one typed document for humans and CI. Why JSON, what applied means vs verify, how blastRadius attaches, what never gets stored, and how investigate/why must extend the same artifact.
Kubernetes deserves a compiler, not a chatbot. How kprompt turns natural language into Intent → Actions → PlanResult, why Go owns planning and safety, and why the IR must stay reviewable for AI SRE.
AI kubectl is not enough. Production needs investigate, why, blast radius, and verify — still behind an approval boundary. Why the AI SRE category exists, what failed in classic AIOps, and what we ship first.
A long-form series on building an AI SRE under an approval boundary — intent compiler, PlanResult, safety, multi-context, investigation, and why we refuse unsupervised auto-remediation. Episode index and honesty rules.
Natural language → plan → approve is the wedge. The differentiator is thinking about the cluster — investigate, why, timeline, blast radius, verify — still under the same approval contract. Honest shipped vs building vs exploring.
Edge-case prompt scenarios for Kubernetes AI CLIs — wipe jokes, unscoped deletes, ambiguous resource names, missing tools, secrets reads, scale-to-zero, and --approve traps — with what kprompt does today.
A playbook of real incident prompts for Kubernetes — what to type when pods crash, images fail to pull, memory kills, wipe-class mistakes, RBAC denials, and latency spikes — with kprompt examples that stay plan-before-apply.
A practical guide to OOMKilled in Kubernetes — exit 137, Last State, memory requests vs limits, kubectl checks, and how kprompt explain can suggest a reviewable memory patch.
Why natural-language Kubernetes tools need plan-before-apply, risk scoring, and hard denies — with real kprompt examples for scale, rollback, and blocked wipe prompts.
A practical guide to Kubernetes troubleshooting — CrashLoopBackOff, deployments not ready, image pull errors, and rollbacks — using kubectl workflows and natural-language explains with kprompt.