The release of Apache Airflow 3.1 builds upon the significant developments introduced in Airflow 3.0 by enhancing support for AI workflows, introducing a new plugins interface, and improving the user interface (UI). In response to the growing use of Airflow for machine learning operations (MLOps) and generative AI applications, version 3.1 introduces features like human-in-the-loop (HITL), which allows users to interact with workflows in real time. The update also supports synchronous DAG execution, enabling external services to wait for workflow completion, which is beneficial for applications requiring immediate results. The plugins interface now supports React views, enhancing customization possibilities within the Airflow UI. Additionally, Airflow 3.1 introduces the ability to favorite DAGs and switch the UI language, aiming to improve user experience and accessibility globally. These changes, alongside other updates like support for Python 3.13 and a new trigger rule, reflect the continuous evolution of Airflow to adapt to modern data orchestration needs.