데이터 에이전트 키트를 활용한 에이전트 기반 데이터 과학

Join our product leaders to architect your agentic future Imagine your compliance team notices that payments are taking longer than expected to clear. They suspect fraud, and you’re the data scientist helping figure out which transactions to investigate first. Assembling that shortlist requires ingesting and cleaning raw transaction logs, training a model on past fraud, scoring new records, and scheduling the pipeline to run daily. Here’s how you can build that workflow end to end with an agent, using the Data Agent Kit inside the Antigravity IDE.

Data Agent Kit helps you work with Google Cloud data through a plugin for coding agents or an extension for Visual Studio Code-based IDEs . It brings two significant additions to the workflow: Help for your agent: MCP tools connect your agent to data services. Skills are Markdown files that give it instructions and best practices for working with your data stack Tools in your IDE: work with Google’s Agentic Data Cloud , from querying databases to deploying an Airflow pipeline, without leaving your editor Let’s start with the raw transaction logs in Google Cloud Storage .

You can ask the agent to create a notebook that loads them into BigQuery using Spark : The agent generates a Jupyter notebook that loads the JSON logs into a BigQuery table named raw_transactions . You can open the notebook, review the code, and run it using a preconfigured Spark Serverless runtime from the IDE. After the notebook runs, you can open the raw_transactions table with the Data Agent Kit. The extension allows you to browse the table and inspect its metadata right from the IDE. The records are now in BigQuery, but they aren’t ready for model training yet.

First, we’ll handle duplicate and incomplete records, then join the remaining records with info about who sent and received each payment. Let’s put the cleanup logic into a dbt project so we can run it on each new batch of data. We’ll also test that each transaction appears only once. Prompt the agent: The agent generates a dbt project with SQL models for cleaning and enriching the data. Take a look at the generated code to check how it handles duplicates and joins reference tables. Then run dbt build in the terminal.