Improve the review quality and efficiency SDK Release PR

## Summary:
As SDK generation and release workflows increasingly rely on AI and self-service, the review process has become a key bottleneck.
While SDK generation is becoming faster and more scalable, human review remains time-consuming and inconsistent, impacting release velocity and quality.

## Challenges:
As we move from a Swagger-based workflow to self-service SDK releases, the review burden has increased significantly.
In the Swagger-based approach, SDK PRs were created and updated centrally by our team, which meant:

- Changes are created by CodeGen and all changes are prodictable.
- Review scope was well understood and reviewers could focus on a small, known set of diffs

In self-service mode, however:

- SDK PRs are created and updated by bots and service teams could commit change easily.
- Changes vary widely across specs, configurations, codegen outputs, and manual edits
- Reviewers must check many more areas to ensure correctness and quality which means manual review effort increases a lot which slowing down approvals and releases

## Goal

The goal of this work is to ensure fast, scalable, and high-quality SDK PR reviews in a self-service model.

To address this, we need to improve the review experience by:
- Integrating AI into the review flow to summarize changes, detect risks, review breaking changes in apiview etc.
- Structuring review process for consistency and scalability

Improving this step is critical to support the growing volume of SDK PRs and maintain high-quality, developer-friendly releases.