An MCP server that lets AI agents operate a multi-cluster Kubernetes fleet through an Open Cluster Management hub, with policy, approval, and audit between the model and your clusters.
AgentOps for Kubernetes fleets, done safely.
An MCP server that lets AI agents operate a multi-cluster Kubernetes fleet through an Open Cluster Management hub, with policy, approval, and audit between the model and your clusters.
The agent never holds a kubeconfig. Every write is policy-checked, human-approved, and traced.
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The whole safe-remediation loop: investigate with free reads, propose a change, get rejected by the guardrails and correct it, wait for a human-signed token, apply, verify, and report from the audit log.
Your team runs many Kubernetes clusters. Sooner or later somebody asks the question: can an AI agent take the 2 a.m. page?
The quickest way to find out is to hand a model kubectl with cluster-admin and watch. In production that experiment ends badly, for three separate reasons:
- The model is non-deterministic. The same alert can produce a careful diagnosis one run and a kubectl delete the next. - The credentials are real. There is no dry run between the model's decision and your production cluster. - There is no record. When something breaks, you cannot reconstruct what the agent did, in what order, or on whose authority.
This project starts from a different observation: fleets already have a control point that humans trust every day, the multi-cluster hub. Open Cluster Management (a CNCF project) gives every fleet an inventory (ManagedCluster), a scheduler (Placement), and
From the project README.
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