Google Unveils Advanced Agentic AI Tools for Data Scientists and Engineers
Google introduced a suite of new agentic AI capabilities aimed specifically at data engineers and data scientists during the Google Cloud Next Tokyo 2025 conference.
The new offerings currently in preview include:
- Data Engineering Agent in BigQuery: Enables data engineers to simplify and automate complex data pipelines using natural language prompts. It supports workflows from data ingestion (e.g., Google Cloud Storage) to transformation and data quality maintenance.
- Data Science Agent in BigQuery Notebooks: Powered by Google's Gemini large language model, this agent assists with exploratory data analysis, data cleaning, machine learning predictions, and feature engineering. It can plan, execute code, reason about results, and present findings while collaborating interactively with data scientists.
- Conversational Analytics Agent and Code Interpreter: Designed for business users and analysts, the Code Interpreter translates complex natural language queries into Python code, facilitating easier data interaction.
- Migration Agent for Spanner: An AI-driven service that simplifies database migrations from systems like MySQL to Google Spanner.
- Conversational Analytics API and Gemini CLI GitHub Actions: These new tools enable integration of Looker's natural language processing and code interpreter features into custom applications and streamline AI coding workflows.
Google's focus on agentic AI reflects growing enterprise interest in autonomous AI agents, especially for complex data workflows. Analysts note that while agentic AI has traditionally targeted customer service and developer use cases, the shift toward data engineering and science represents tackling more intricate enterprise challenges.
Chirag Dekate, Gartner analyst, emphasized that enterprises aim to "reimagine, deconstruct, and reconstruct" their operations in an AI-native context. Agentic AI tools are key to increasing productivity by automating and augmenting data workflows.
Bradley Shimmin from Futurum Group highlighted that data professionals previously spent significant time searching for data rather than building infrastructure or applications. Agentic AI tools like Google's promise to transform this dynamic.
Google also expanded its collaboration with Wells Fargo to deploy AI agents across banking departments, including corporate, investment banking, and customer service.
Recent Gemini platform advancements include:
- Veo 3 and Veo 3 Fast: Video generation models now generally available on Vertex AI GenAI platform, with upcoming image-to-video capabilities.
- Gemini 2.5 Deep Think: Expanded availability for Google AI Ultra subscribers.
These developments position Google as a key player in enabling enterprises to adopt agentic AI for data-centric workflows, improving agility and precision.
Esther Shittu is an Informa TechTarget news writer and podcast host covering AI software and systems.
Source: Originally published at TechTarget on August 5, 2025.
Frequently Asked Questions (FAQ)
Google's Agentic AI Tools for Data Professionals
Q: What are agentic AI tools?
A: Agentic AI tools are advanced artificial intelligence systems designed to perform tasks autonomously, often with the ability to reason, plan, and interact with their environment to achieve specific goals. In the context of data science and engineering, they aim to automate complex workflows.
Q: What is the primary benefit of Google's new agentic AI tools for data scientists and engineers?
A: The primary benefit is the automation and simplification of complex data-related tasks, freeing up data professionals to focus on higher-level analysis and innovation. These tools promise to significantly increase productivity and efficiency in data pipelines and analysis.
Q: How does the Data Engineering Agent in BigQuery work?
A: It allows data engineers to interact with BigQuery using natural language prompts to build and manage data pipelines, from ingestion to transformation and quality checks.
Q: What kind of tasks can the Data Science Agent in BigQuery Notebooks assist with?
A: This agent, powered by Google's Gemini model, can help with exploratory data analysis, data cleaning, machine learning model predictions, and feature engineering. It can also plan and execute code, interpret results, and present findings interactively.
Q: Who are the Conversational Analytics Agent and Code Interpreter designed for?
A: These tools are intended for business users and analysts who may not have extensive coding knowledge. The Code Interpreter can translate natural language queries into Python code, making data interaction more accessible.
Q: How does Google's expansion into agentic AI for data science reflect broader industry trends?
A: It signifies a growing enterprise demand for autonomous AI agents that can tackle complex business challenges, moving beyond traditional customer service or simple developer use cases into more intricate data workflows.
Q: What impact do these tools have on the daily work of data professionals?
A: As highlighted by industry analysts, these tools aim to reduce the time data professionals spend on data discovery and routine tasks, allowing them to dedicate more effort to building infrastructure and developing innovative applications.
Q: What are some recent advancements in Google's Gemini platform mentioned in the article?
A: Recent advancements include the general availability of Veo 3 and Veo 3 Fast for video generation on Vertex AI GenAI platform, and expanded availability of Gemini 2.5 Deep Think for Ultra subscribers.
Crypto Market AI's Take
Google's introduction of advanced agentic AI tools for data scientists and engineers marks a significant step in making complex data workflows more accessible and efficient. This aligns with the broader trend of AI adoption across industries, including finance. At Crypto Market AI, we are keenly observing how such sophisticated AI capabilities are being integrated into financial analysis and trading. Our own platform leverages advanced AI and machine learning models to provide comprehensive market intelligence and trading insights. For instance, our focus on AI-driven crypto trading bots aims to offer similar automation and efficiency gains in the cryptocurrency market. The development of tools that can interpret data, generate insights, and even execute tasks based on natural language prompts is a testament to the growing power of AI in transforming professional workflows, a principle we deeply resonate with here at Crypto Market AI.
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- AI-Driven Crypto Trading Tools Reshape Market Strategies in 2025
