OCP/Telco Definition of Done
https://docs.google.com/document/d/1TP2Av7zHXz4_fmeX4q9HB0m9cqSZ4F6Jd4AiVoaF_2s/edit#heading=h.gaa58bzbvwde
Epic Template descriptions and documentation.
https://docs.google.com/document/d/14CUCEg6hQ_jpsFzJtWo29GfFVWmun2Uivrxq3_Fkgdg/edit
ACM-wide Product Requirements (Top-level Epics)
https://docs.google.com/document/d/1uIp6nS2QZ766UFuZBaC9USs8dW_I5wVdtYF9sUObYKg/edit

*<--- Cut-n-Paste the entire contents of this description into your new
Epic --->*

Epic Goal

Submariner team has been recently working on leveraging AI to help with different deay to day activities and workflows. It started primarily as an exercise to understand agentic AI and tools like Cursor, Claude etc. with a goal to see how it could help team improve their efficiency.

The main conclusion reached was that main points where AI can help the team considerably are:

1. Troubleshooting Submariner issues faced by QE, other teams and customers/support.
2. Deploying and testing dev changes and latest upstream and downstream releases.
3. Automating releases and Konflux so anyone can do it with minimal effort
4. Managing CVEs
5. Slack bot to smartly auto reply to queries from other teams and support.

There are already K8s MCP server with work being done on extended versions with OCP and OVNK MCP server. But Submariner has some unique MultiCluster requirements not met by them as well as needs support for subctl cli.

Key decision points are:

1. Troubleshoot on live clusters in realtime vs from gathered data.
2. Address privacy concerns regarding exposing customer data to third party models and tools. This requires training and using an in-house or RH/IBM approved model or anonymizing data before using external models like Sonnet, Gemini etc.
3. Tools to be used internally by team vs sharing with support and other teams.

Why is this important?

Submariner is a small team with imminent move to OVNK Networking to work on more end user MultiClusterNetworking use cases. But team still has to support existing users with ACM. Most of the user cases and queries have a very typical and rather fixed workflow which can be easily automated using AI, saving considerable time for the team and allowing them to focus on new features.

Scenarios

...

Acceptance Criteria

...

Dependencies (internal and external)

1. ...

Previous Work (Optional):

1. ...

Open questions:

1. …

Done Checklist

• CI - CI is running, tests are automated and merged.
• Release Enablement <link to Feature Enablement Presentation>
• DEV - Upstream code and tests merged: <link to meaningful PR or GitHub
  Issue>
• DEV - Upstream documentation merged: <link to meaningful PR or GitHub
  Issue>
• DEV - Downstream build attached to advisory: <link to errata>
• QE - Test plans in Polarion: <link or reference to Polarion>
• QE - Automated tests merged: <link or reference to automated tests>
• DOC - Doc issue opened with a completed template. Separate doc issue
  opened for any deprecation, removal, or any current known
  issue/troubleshooting removal from the doc, if applicable.
• Considerations were made for Extended Update Support (EUS)