- Google Cloud adds six new AI agents for data scientists, engineers and more
- Advanced analytics will become more accessible with natural-language AI
- A solid data foundation is just as important, but Google can help you migrate
Google Cloud has launched six new AI agent tools to assist data engineers, data scientists, developers and business users realize even more productivity benefits.
Outlining a, “new era where specialized AI agents work autonomously and cooperatively to unlock insights at a scale and speed,” Data Cloud Managing Director Yasmeen Ahmad explained the benefits of a “single, unified, AI-native cloud” over siloed tools when it comes to using AI.
Besides new, specialized AI agents, Google Cloud is also launching a series of APIs, tools, and protocols as well as updates to unify data.
Google Cloud launches even more AI agents
The first agent, destined for data engineers, is designed to automate complex data pipelines by allowing engineers to describe tasks and then autonomously building and executing workflows. A separate Spanner Migration Agent will simplify migrating from legacy databases like MySQL to Spanner, eliminating hours of tedious administrative work.
Data scientists will benefit from an agent that automatically performs exploratory data analysis, data cleaning, feature engineering and ML predictions, offering step-by-step reasoning and collaborative feedback, while business users and analysts will get to use two separate agents designed to answer questions about data and interpret code with visualisations and explanations, meaning that non-technical users can perform advanced analytics.
Finally, Gemini CLI GitHub Actions will automate pull requests, tests, reviews and implementation for developers.
“The true potential of the agentic shift is realized when developers not only use existing agents, but also extend and connect them to their own intelligent systems, creating a broader network,” Ahmad explained.
With its new agents, Google Cloud hopes to lower the barrier of entry into advanced data analytics, “eras[ing] the line between operational and analytical worlds.”
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