Payment delays prompted a compliance team to suspect fraud, requiring a data scientist to identify transactions for investigation. This process involves ingesting and cleaning raw transaction logs, training a fraud detection model, scoring new transactions, and automating the workflow. The Data Agent Kit within the Antigravity IDE simplifies this end-to-end process.The kit offers MCP tools and Markdown skills to connect agents to Google Cloud data services and provides guidance for data stack interactions. It also enables users to manage Google's Agentic Data Cloud directly from their IDE. Initially, transaction logs are loaded into BigQuery using Spark via an agent-generated notebook.Following data loading, a dbt project is created for cleaning and enriching the BigQuery data, including handling duplicates and joining with reference information. This dbt project also includes a uniqueness test for transactions. Next, a training notebook is generated to build a Random Forest fraud detection model using historical data with known fraud labels.The trained model is then used in an inference notebook to score unlabeled transactions. Transactions with a fraud probability exceeding a specified threshold, like 50%, are sent to a Spanner table for review. This process creates a shortlist of transactions for the compliance team to investigate.Finally, Managed Service for Apache Airflow is utilized to automate the entire workflow, ensuring it runs daily. The agent generates an Airflow DAG definition and deployment configuration, orchestrating the data loading, transformation, and scoring steps. This automated pipeline continuously populates the review queue with potentially fraudulent transactions.