The Data Engineering Agent in BigQuery is an intelligent assistant powered by Gemini that moves beyond simple code completion to provide end-to-end task automation. It's grounded in your specific data environment—it uses metadata from Dataplex to understand your schemas, lineage, and business definitions. It can autonomously handle the entire data lifecycle: from discovering relevant datasets and generating complex SQL or PySpark transformations to orchestrating those jobs via Dataform or Cloud Composer. By automating the "toil" of data engineering—such as fixing broken pipelines, documenting legacy code, or migrating queries from older data warehouses—it transforms the role of the data engineer from a manual coder into an architect who supervises AI-driven workflows.

The Data Science Agent in BigQuery accelerates data science development with agentic capabilities that facilitate data exploration, transformation, and ML modeling.

With a simple prompt, the agent generates a detailed plan covering all aspects of data science modeling from data loading, exploration, cleaning, visualization, feature engineering, data splitting, model training/optimization and evaluation. If the agent makes an error, it can autocorrect and generate new code to rectify it. You maintain full control, with the ability to approve each step and make manual edits if desired.

The agent also has full contextual awareness of your notebook, understanding existing code, outputs, and variables to provide tailored code for each step of the plan, allowing you to make iterative changes to your existing code.

Conversational Analytics in BigQuery is a sophisticated AI-powered reasoning engine that eliminates the "analytics bottleneck" by empowering data teams to bridge the gap between business questions and trusted answers through intuitive natural language interaction.

Powered by the latest Gemini models, this agent moves beyond simple translation by grounding its logic in your organization's existing data assets—specifically leveraging metadata, table and column descriptions, business glossaries, and user defined functions—to ensure every generated result aligns perfectly with your internal business definitions. Moving beyond simple reporting, the agent uses BigQuery AI to project future outcomes and interprets unstructured data like images within object tables to turn hidden information into actionable intelligence.

Conversational Analytics in Looker simplifies Business Intelligence by enabling business users to find answers using natural language. This reduces the burden on data analysts and facilitates faster, more confident decision-making. Business users can ask direct questions about product performance or traffic trends without needing to understand complex field names.

Beyond simple querying, it provides a comprehensive lifecycle management framework, incorporating enterprise-grade security and user management directly into the consumption layer. The universal semantic layer of Looker ensures that metrics like revenue and churn remain consistent across the company by creating a central hub for data context, definitions, and relationships.

The Conversational Analytics API lets developers embed natural-language query functionality in custom applications, internal tools, or workflows, all backed by trusted data access and scalable, reliable data modeling. It’s the same API that powers the out-of-the-box conversational experiences in Looker and BigQuery.

The Conversational Analytics API lets you build custom data experiences that provide data, chart, and text answers while leveraging Looker's trusted semantic model for accuracy or providing critical business and data context to agents in BigQuery. You can embed this functionality to create intuitive data experiences, enable complex analysis via natural language, and even orchestrate conversational analytics agents as ‘tools’ for an orchestrator agent using Agent Development Kit.

Google Cloud's agent development tools reduce the need for developers to build custom database connectors through ADK and MCP integration methods.

The MCP server for BigQuery allows an AI agent and MCP clients to interpret schemas and execute queries against BigQuery data while reducing the security or governance risks or latency associated with moving data into context windows.

For more flexibility and control, use the MCP Toolbox–an open-source server that centralizes the hosting and management of toolsets, decoupling agentic applications from direct database interaction. It’s also available with a variety of IDEs and developer tools including Gemini CLI and Antigravity, allowing you to securely connect your AI agents to services like AlloyDB, BigQuery, Spanner, Looker and more.

Additionally, the BigQuery ADK integration toolset includes ready-to-use functions that enable agents to autonomously: explore data, understand schemas, run queries and forecasts, and get insights using natural language.