Google Cloud is democratizing data pipeline orchestration with the new Orchestration Pipelines framework, introduced at Google Cloud NEXT ’26. This framework is made accessible through the Data Agent Kit, a free, open-source collection of data engineering tools. The kit integrates into popular IDEs and CLIs, providing a Data Engineering tab and an agentic skill for Airflow DAGs. It allows data professionals to author, deploy, and troubleshoot production-grade Airflow DAGs using natural language. The framework streamlines MLOps by decoupling orchestration logic from compute execution, using a declarative YAML DSL. This enables all data personas to bypass complex Python Airflow boilerplate. A practical example demonstrates building an MLOps architecture for proactive supply chain management. This architecture predicts transit times using BigQuery, Managed Service for Apache Spark, Gemini Enterprise Agent Platform, and dbt. The Data Agent Kit generates PySpark scripts and declarative YAML pipelines from natural language prompts. Three distinct pipelines are demonstrated: a training engine, a daily inference pipeline, and an automated evaluation and branching pipeline. Deployment is automated through CI/CD, with the Data Agent Kit generating necessary workflows. For day-two operations, the kit offers real-time monitoring and agentic troubleshooting within the IDE. It diagnoses failures, identifies root causes, and suggests inline fixes for issues. Orchestration Pipelines and the Data Agent Kit significantly reduce the time and complexity of building and maintaining MLOps architectures.