At Google Cloud Next 25, the tech giant unveiled new generative AI-powered agents aimed at accelerating data management and analytics workflows for data engineers, scientists, analysts, and business users. The new tools, including the Data Engineering Agent for BigQuery, Data Science Agent for Colab, and the Looker Conversational Analytics Agent, aim to simplify and speed up tasks like data preparation, model development, and query analysis.
These agents, now generally available, integrate AI to assist with tasks like anomaly detection, automated metadata generation, and intelligent model selection. Built on the BigQuery Knowledge Engine, which uses Google’s Gemini large language model, the agents provide enhanced semantic search and contextual outputs that significantly improve efficiency and accuracy.
These enhancements are tailored for specific roles—data engineers, data scientists, and business analysts—allowing for a more personalized user experience. According to analysts, such role-specific agents help address distinct challenges faced by each group, fostering better productivity and data insights.
Despite the potential, experts caution that training and intuitive user interfaces will be crucial to ensuring the agents are accessible and effective. Google plans to expand the scope of agentic AI capabilities, with more features targeting unstructured data and autonomous data management expected in the coming years.
In addition, Google introduced other new tools like BigQuery’s multimodal tables and AI query engine, as well as the integration of Apache Iceberg for data lakehouses, aiming to enhance real-time data pipelines and simplify data operationalization. However, analysts urge caution, noting that some of the capabilities are still in preview, and the full impact of these tools will depend on real-time performance.