wenjin272 commented on issue #894: URL: https://github.com/apache/flink-agents/issues/894#issuecomment-5077556987
@weiqingy I’m wondering whether we should complement the reviewer-side guidance with some developer-side guidance for AI-assisted PRs. For a non-trivial PR whose implementation is largely AI-assisted, the author could provide a concrete **Implementation Description**, either in the PR description or as a linked document. It should go beyond a high-level design and cover the runtime flow, behavioral contracts, key decisions, failure behavior, compatibility impact, and how tests map to those contracts. This could support a two-stage review process: 1. Human reviewers first review the architecture, design decisions, and behavioral contracts. 2. A coding agent then verifies that the implementation and tests match the accepted description. One possible way to introduce this would be to add a short **How to Contribute** section to `AGENTS.md`, link to something like `contribution-guides/ai-assisted-pr.md`, and add an **Implementation Description** section to the PR template. The goal would be to keep design and architectural decisions under human review, while letting coding agents handle more of the implementation-level verification. Do you think this direction would be useful and feasible for the project? I think we could first try a lightweight experiment before promoting this as a community-wide practice. For PRs opened by the two of us, especially non-trivial or heavily AI-assisted ones, we could voluntarily include a concrete **Implementation Description** in the PR body or a linked document. It would describe the runtime flow, behavioral contracts, key decisions, failure behavior, compatibility impact, and the mapping between those contracts and tests. Reviewers could first review this description at the design and architecture level, then use a coding agent to verify that the implementation and tests match it. This may help keep architectural decisions under human review while delegating more implementation-level verification to AI. After trying this on a few PRs, we could evaluate whether it actually reduces review effort, what level of detail is useful, and how much authoring overhead it introduces. If the experiment works well, we could then consider documenting it in `AGENTS.md`, adding a contribution guide, or updating the PR template. Would you be interested in trying this? -- This is an automated message from the Apache Git Service. To respond to the message, please log on to GitHub and use the URL above to go to the specific comment. To unsubscribe, e-mail: [email protected] For queries about this service, please contact Infrastructure at: [email protected]
