A practical guide to the best tools for both using AI agents to manage Kubernetes (AIOps) and running AI agent workloads on Kubernetes infrastructure in 2026.
This article is part of our guide to Kubernetes for AI.
Kubernetes was designed for stateless workloads. AI agents are highly stateful, requiring persistent memory, GPU scheduling (including fractional Multi-Instance GPU allocation), long-running processes, and unpredictable compute bursts.
Integrating AI agents with Kubernetes covers two distinct engineering use cases:
These tools treat your cluster as the input and use AI to reduce the operational burden on your team.
Qovery's built-in AI layer sits above the infrastructure and handles environment provisioning, deployment troubleshooting, and configuration suggestions through natural language. Unlike standalone AIOps tools, it acts on your infrastructure directly — not just alerts you. Particularly useful for platform engineers managing multiple environments across clusters. Qovery also exposes an AI agent skill so external agents (Claude, Cursor, etc.) can deploy and manage apps through a single prompt with guardrails to keep compliance and security on point.
The most widely adopted AI assistant for Kubernetes operations. Integrates directly into Slack and Teams, monitors cluster events in real time, and lets engineers trigger kubectl commands through chat. Best for teams who want to bring Kubernetes context into their existing communication workflow without a new dashboard.
Autonomous resource optimisation platform. Continuously adjusts pod and node allocation based on live workload behaviour without requiring manual intervention. Strong fit for cost-conscious teams running high-variance workloads.
k8sgpt is an open-source CLI tool (now a CNCF Sandbox project) that scans your cluster, runs diagnostics, and returns plain-English explanations of what's broken and how to fix it. It works with multiple LLM backends - OpenAI, Azure OpenAI, local models via Ollama, and others - so no data has to leave your infrastructure if you don't want it to. You run k8sgpt analyze --explain and it surfaces failing pods, misconfigured services, and CrashLoopBackOff causes in readable language. It's the best entry point for teams exploring AI-assisted Kubernetes troubleshooting without committing to a SaaS platform.
kubectl-ai is a Google-backed open-source tool that adds a natural-language interface on top of kubectl. Instead of remembering exact syntax, you describe what you want ("scale the frontend deployment to 5 replicas in staging") and it generates and runs the corresponding kubectl command. It's ideal for engineers who live in the terminal and want to lower the cognitive overhead of Kubernetes operations. Like k8sgpt, it supports multiple model backends including local models, keeping you in control of where prompts are sent.
These tools solve the infrastructure challenge of running agent workloads — which behave differently from standard web services.
Qovery abstracts Kubernetes complexity for teams deploying AI agent services. You define your app, Qovery handles the Kubernetes manifests, autoscaling, environment promotion (dev → staging → prod), and ephemeral environments for every PR. For AI workloads specifically, Qovery supports GPU node scheduling, long-running process configurations, and multi-service architectures common in agent pipelines (orchestrator + retrieval + inference). Try free →
Ray's Kubernetes operator for distributed AI and ML workloads. Best for teams running large-scale inference or training jobs that need fine-grained resource control. Requires meaningful Kubernetes expertise to operate.
The standard GitOps stack for teams who want version-controlled, declarative deployments of agent services. Mature, widely supported, but requires your team to own the full Kubernetes configuration layer. Qovery can sit on top of this stack and provide the developer-facing abstraction.
For a deeper look at where this is all heading - a single control plane that lets AI agents operate infrastructure, not just run code - see what an agentic infrastructure platform is.
For AI-assisted operations (AIOps), the leading tools are k8sgpt and kubectl-ai (open-source CLIs), Botkube (chat-based ops), and Sedai (autonomous optimization). For running AI agent workloads on Kubernetes, KubeRay handles distributed ML, and platforms like Qovery abstract the cluster so teams can deploy agent services without managing manifests.
k8sgpt is an open-source, CNCF Sandbox CLI tool that uses LLMs to scan a Kubernetes cluster, diagnose problems, and explain them in plain English. It supports multiple model backends (OpenAI, Azure, local models via Ollama) so you can keep cluster data inside your infrastructure. It's commonly used to quickly triage failing pods and misconfigurations.
kubectl-ai is a Google-backed open-source tool that adds a natural-language layer on top of kubectl. You describe the operation you want in plain English and it generates and executes the matching kubectl command, lowering the syntax overhead of day-to-day Kubernetes work.
Yes, but agent workloads are stateful and bursty - they need persistent memory, GPU scheduling, and long-running processes, which differ from the stateless services Kubernetes was designed for. Tools like KubeRay and platforms like Qovery handle GPU node scheduling, autoscaling, and multi-service agent architectures so these workloads run reliably.
Melanie leads content at Qovery. She covers platform engineering trends, Kubernetes operations, FinOps, and the tools that help engineering teams ship faster.
Qovery ensures every agent action is scoped, audited, and policy-checked. Start deploying in under 10 minutes.