Developed and deployed an automated AI-powered issue triage workflow for the kubeflow/pipelines repository, focusing on streamlining the process of assessing new issues. Leveraging GitHub Actions, Bash scripting, and the GitHub Models CLI, the workflow analyzes incoming issues for quality, scope, and complexity, then posts AI-generated assessments directly as comments. This integration reduces manual triage effort and improves the consistency of issue labeling, enabling faster prioritization and more efficient resource allocation. The work centered on a single integration commit, demonstrating end-to-end automation for triage workflows and freeing engineering teams to focus on higher-value development tasks.