Hi Everyone, Thanks to you all, our project is off to a such a great start. It's been great to see so much enthusiasm and positive energy all around. I think we have an opportunity to build something really special here. To keep the momentum going, I am sharing an early draft of the roadmap for the project to seek feedback on the priorities.

Roadmap

kubectl-ai is an AI-powered assistant for kubernetes and associated cloud infrastructure. It should be be able to:

• Answer questions related to kubernetes concepts
• Answer questions related to resources in a kubernetes cluster and associated cloud.
• Perform operations that involves creating/updating/deleting one or more resources in a cluster and associated cloud
• Troubleshoot a mis-configuration or unexpected behavior in a kubernetes cluster

P0: Bugs

Anything that prevents using kubectl-ai should be addressed first. Unblocking usage is the key to making something people want. Issues that are identified as bugs will be labelled with the label bug.

P0: Tools Extensibility (bring your own tools: BYOT)

kubectl-ai uses built-in tools such as kubectl and bash. One of the big advantages of using kubernetes is the rich ecosystem of tools and we have observed users use multiple tools (OSS and in-house tools) to accomplish k8s related tasks. We are also observing that MCP (model context protocol) has become a standard way to discover and call external tools.

Example CUJs:

• Respond to a github issue containing kubernetes related troubleshooting query (requires kubectl and github mcp server or gh custom tool)
• Install a helm chart and verify the resources (requires helm and kubectl)
• Estimate the current costs of all the nodes in a cluster (requires using kubectl and (gcloud/aws/az).
• Create a gke cluster and deploy nginx app in web namespace (terraform, kubectl)

kubectl-ai should:

• Support plugging in additional tools
• Support integration with MCP servers.
• Rich evals to evaluate performance of using additional tools as well tools exposed by MCP servers.

P0: K8s-bench

An agent is as good as the evals. Our current evals are saturated especially with Gemini 2.5 pro and we need evals that represent user realistic scenarios and also represents complexity of today’s cloud infrastructure.

K8s-bench should:

• Have well defined guidelines and instructions for adding evals.
• Periodically publish evals for top models (starting with Gemini at least).
• 10 evals per category of CUJ. Categories are – troubleshooting, creating/updating/deleting resources, operations involving kubectl commands and other tools, fixing mis-configuration, and answering questions related to resources.
• Sufficient hard evals to ensure that the saturation is at 60-70% range for the top-most model.
• Score results on the basis of other parameters such as input/output tokens, costs, thinking budgets and latency.

P1: Code Quality and Contributor experience

kubectl-ai is in its early days and we expect it to evolve rapidly especially given the state of the AI industry. We want to ensure that it is easy to contribute to the project. We need to ensure that kubectl-ai is not bloated with unnecessary features and should not be afraid to say NO.

Project should ensure:

• Faster dev loops
• Contributors should be able to develop locally without any external dependencies.
• Healthy test coverage (unit, integration and e2e). We need to strike a healthy balance here and prioritize test coverage for the core pieces.
• Less toil. We should automate as much as possible. We should also explore leveraging coding agents for development.
• Clear and concise contributing guide.

P2: Experiments/POCs

We want to encourage experiments and POC in the projects. We think it is important to foster creativity and have fun. POCs are critical to understanding the complexity of a problem and shape of the solution. We prefer POCs over design docs.

POC backlog:

• Remote kubectl-ai agent
• MCP-Server for kubernetes
• GUI (HTML) based interface for the agent
• Terminal UI (using bubbletea or rtview)
• Session management
• Secure tool use: - [ ] isolated exec (pods, containers, drop permissions, ...) - [ ] block specific commands for tools - [ ] experiment without shell command, which is too broad

P2: Misc Enhancements

• list/load specific prompts/TSGs that would help diagnose (some overlap with MCP)

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