The emergence of AI agents is transforming how applications and analytical systems operate. Traditional AI platforms often struggle to access the context within enterprise databases, leading to inaccuracies and security vulnerabilities. Google addresses these challenges with its Agentic Data Cloud, an AI-native system designed to integrate operational and analytical systems seamlessly.
Recently, Google unveiled a suite of new data agents and tools aimed at enhancing the development of AI workflows. These innovations cater to various roles, including business analysts, data scientists, and developers, facilitating greater automation and intelligence in their daily tasks.
Conversational Analytics
To support the development of agents using natural language, Google has expanded its Conversational Analytics capabilities across the Data Cloud:
- BigQuery: Now in preview, this feature integrates an AI reasoning engine into BigQuery Studio, allowing users to leverage business context for more accurate insights and automate workflows.
- Lakehouse: Users can query distributed data lakes across multiple cloud platforms using natural language, enhancing data accessibility without data transfer.
- AlloyDB, Spanner, and Cloud SQL: These databases now support conversational AI, enabling users to interact with their data in real time.
- Looker Embedded Conversational Analytics: This feature allows embedding agents into custom applications, simplifying the integration of conversational AI into workflows.
New Data Agents
To promote proactive intelligence in data management, Google has introduced several new data agents:
- Data Engineering Agent: Automates the creation and maintenance of data pipelines, converting natural language into optimized SQL or Python code.
- Data Science Agent: Assists data scientists in model development by suggesting features and generating code.
- Database Observability Agent: Monitors database performance and provides recommendations for optimization.
- Database Onboarding Agent: Guides users in selecting and provisioning the right Google Cloud database based on their requirements.
- Looker Dashboard Agent: Enables natural language queries within dashboards, providing context-aware answers and summaries.
- Data Insights Agent: Offers unified insights by querying both structured and unstructured data sources.
- Deep Research Agent: Constructs comprehensive research plans by synthesizing information from various sources.
Development Tools
Google has also rolled out tools to aid developers in building AI applications:
- Data Agent Kit: A suite of skills and tools for data practitioners to manage data at scale.
- Managed MCP Servers: These servers simplify the connection between AI models and data, ensuring security and scalability.
- QueryData: Translates natural language into database queries with high accuracy.
- Universal Commerce Protocol Analytics: Enables real-time event streaming into BigQuery for enhanced observability in commerce.
These advancements provide organizations with the tools needed to enhance their data management strategies and leverage AI effectively. For further details on accessing these new agents and tools, documentation is available through the Google Cloud console.
