AI First Colab Notebooks in BigQuery and Vertex AI

At Google I/O 2025, we announced a new, reimagined AI-first Colab with agentic capabilities, making it a true coding partner that understands your current code, actions, intentions, and goals. Today, we are excited to bring these capabilities to Google Cloud BigQuery and Vertex AI via the Colab Enterprise notebook. Designed to simplify and transform data science and analytics workflows for organizations, the new capabilities in the Colab Enterprise notebook can:

• Automate end-to-end data science workflows through the built-in Data Science Agent (DSA), which creates multi-step plans, generating and executing code, reasons about the results, and presents its findings.
• Generate, explain and transform code, as well as explain errors and fix them automatically. It can also provide code assistance while you type.
• Create visualizations from simple prompts.

Let’s take a closer look.

Simplify workflows with Data Science Agent

Data science can be complex, iterative, and time-consuming. You must first translate your business problem into a machine learning task, identify and clean raw data, transform it, train a model, evaluate it and then repeat the loop to optimize it. This requires skill and time. The Data Science Agent (DSA) in Colab accelerates data science development with agentic capabilities that facilitate data exploration, transformation and ML modeling.

You start with a simple prompt such as “Train a model to predict ‘income bracket’ from table bigquery-public-data.ml_datasets.census_adult_income“ in the notebook chat. The Data Science Agent then generates a detailed plan covering all aspects of data science modeling from data loading, exploration, cleaning, visualization, feature engineering, data splitting, model training/optimization and evaluation.

You can accept, cancel, or modify this plan. The generated code is executed on the Colab runtime. If the agent makes an error it can autocorrect and generate new code rectifying it. You maintain full control, approving each step and can make manual edits if desired. This iterative approach ensures transparency and trust.

The agent also has full contextual awareness of your notebook, understanding existing code, outputs, and variables to provide tailored code for each step of the plan, allowing you to also make iterative changes to your existing code.