Google Launches Advanced Agentic AI Tools for Data Scientists and Engineers

Google introduced a suite of new agentic AI capabilities aimed at data engineers and data scientists during the Google Cloud Next Tokyo 2025 conference. These tools are designed to simplify complex data workflows and enhance productivity by leveraging generative AI.

New AI Agents and Tools

• Data Engineering Agent in BigQuery: This agent helps data engineers simplify and automate complex data pipelines. Users can use natural language prompts to streamline workflows such as ingesting data from Google Cloud Storage, transforming data, and maintaining data quality.
• Data Science Agent in BigQuery Notebooks: Powered by Google's Gemini large language model, this agent supports 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: The Code Interpreter translates complex natural language queries into Python code, making it accessible for business users and analysts.
• Spanner Migration Agent: An AI-powered service that assists data engineers in migrating databases from systems like MySQL to Google Spanner.
• Gemini Data Agents APIs: These APIs orchestrate different agents and enable integration with systems. The first API, Conversational Analytics API, integrates Looker's natural language processing and code interpreter capabilities into applications.
• Gemini CLI GitHub Actions: A free AI coding agent designed to assist developers.

Industry Context and Expert Insights

Google's focus on agentic AI aligns with growing enterprise interest in autonomous AI agents, which are expected to transform how businesses operate by reimagining workflows in an AI-native context. Chirag Dekate, Gartner analyst, emphasized that enterprises aim to deconstruct and rebuild their operations around AI and agent technologies. He noted that the new data engineering and science agents mark a shift toward tackling more complex enterprise problems. Bradley Shimmin from Futurum Group highlighted the productivity gains these tools offer, pointing out that data scientists and engineers previously spent excessive time searching for data rather than building applications and workflows. Shimmin also stressed the importance of trust in AI models, noting that while AI agents are improving in understanding data and generating structured outputs, users must remain aware of the semistructured nature of business data and processes.

Collaborations and Recent Developments

Google expanded its partnership with Wells Fargo to deploy AI agents across various departments, including corporate and investment banking and customer service. Additionally, Google recently made advancements in its Gemini AI platform:
• 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 to Google AI Ultra subscribers.

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Esther Shittu is a news writer and podcast host covering AI software and systems for Informa TechTarget.

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Source: Originally published at TechTarget on August 5, 2025.

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Frequently Asked Questions (FAQ)

Google's New AI Agents for Data Professionals

Q: What are Google's new agentic AI tools designed for? A: These tools are designed to simplify complex data workflows and enhance productivity for data engineers and data scientists by leveraging generative AI. Q: How does the Data Engineering Agent in BigQuery help data engineers? A: It helps simplify and automate complex data pipelines by allowing users to use natural language prompts for tasks like data ingestion, transformation, and quality maintenance. Q: What capabilities does the Data Science Agent in BigQuery Notebooks offer? A: Powered by Google's Gemini large language model, it supports exploratory data analysis, data cleaning, machine learning predictions, and feature engineering. It can also plan, execute code, and reason about results interactively. Q: Who can benefit from the Conversational Analytics Agent and Code Interpreter? A: Business users and analysts can benefit from it, as the Code Interpreter translates complex natural language queries into Python code. Q: What is the purpose of the Spanner Migration Agent? A: It's an AI-powered service designed to assist data engineers in migrating databases, for example, from MySQL to Google Spanner. Q: What are Gemini Data Agents APIs used for? A: These APIs orchestrate different agents and enable integration with various systems, including integrating Looker's natural language processing and code interpreter capabilities into applications via the Conversational Analytics API. Q: What is the Gemini CLI GitHub Actions tool? A: It's a free AI coding agent intended to assist developers. Q: What is the industry trend related to Google's new agentic AI tools? A: Google's focus aligns with the growing enterprise interest in autonomous AI agents, which are expected to transform business operations by creating AI-native workflows. Q: What are the key benefits for data scientists and engineers mentioned by experts? A: Experts like Bradley Shimmin highlight productivity gains, noting that these tools can reduce the time data professionals spend searching for data, allowing them to focus more on building applications and workflows. Q: What is an important consideration when using AI agents for data analysis, according to experts? A: Experts emphasize the importance of trust in AI models and users should remain aware of the semistructured nature of business data and processes, even as AI agents improve in understanding and generating outputs.

Crypto Market AI's Take

Google's advancements in agentic AI tools for data professionals reflect a broader trend where AI is increasingly being integrated into specialized workflows to boost efficiency and unlock new analytical capabilities. This mirrors the developments in the financial sector, where AI is revolutionizing market analysis and trading. For instance, at Crypto-Market.AI, we leverage AI-powered agents and machine learning models to provide sophisticated market intelligence, automated trading bots, and personalized financial insights. These advancements aim to make complex financial data more accessible and actionable, similar to how Google's new tools empower data scientists. We believe that the future of data analysis and financial markets lies in the intelligent application of AI, driving innovation and enhancing user productivity across various industries.

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