Worked on the aws/sagemaker-distribution repository to deliver a feature that improves deployment reliability for SageMaker Workflows. Developed a conditional installation process for the sm-spark-cli tool, ensuring it is only deployed when the relevant blueprint status is detected. This approach involved updating the post-startup script to verify the blueprint status before proceeding, which prevents unnecessary tool execution and streamlines workflow operations. Enhanced the workflow client to accurately identify project blueprint status, integrating AWS DataZone and Boto3 for automation and control. Utilized Python, Bash, and shell scripting to implement these changes, focusing on robust conditional logic and efficient deployment practices.