Google Colab has been a staple of the ML community for years: free GPUs, zero setup, shareable notebooks. But the tool you open today looks fundamentally different from the one you used a year ago. The new Colab is no longer just a hosted Jupyter environment. It's been rebuilt from the ground up as an AI-first coding partner, with Gemini embedded at every layer of the workflow.
From notebook to agent
The centerpiece of the new Colab is what Google calls the Data Science Agent (DSA). This is not a chatbot that suggests the next line of code. It's an agentic system -- meaning it can plan, act, and reason across multiple steps without you holding its hand through each one.
The DSA triggers autonomous analytical workflows: the agent creates a plan, executes code, reasons about the results, and presents its findings, all while allowing you to provide feedback and stay in control. In practice, this means you can drop a raw CSV into a notebook, type "clean this dataset, engineer features, and train a classifier," and watch Colab do it.
From taking a raw dataset and asking the agent to autonomously perform cleaning and preparation, to generating feature analysis, training models, and evaluating the results -- this turns hours of work into a guided, conversational experience.
The agentic loop the DSA runs through looks like this:
- Plan: Outlines the steps it will take to accomplish your goal
- Execute: Writes and runs the necessary Python code across multiple cells
- Reason: Analyzes the output to inform its next steps
- Present: Summarizes findings and surfaces them back to you
- Iterate: Accepts your feedback mid-execution to refine or reroute the analysis
The features under the hood
Colab AI operates across your entire notebook, providing more accurate responses, taking actions across multiple cells, and engaging in meaningful ways to help you build faster. Beyond the DSA, the new Colab ships a set of tightly integrated AI features:
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