Google AI releases 5 new AI agents/platforms for developers

Google Cloud recently launched five dedicated AI agents designed to simplify developer workflows – reducing manual work, speeding up analytics and reducing barriers to advanced data and code automation. From data pipeline orchestration to enterprise-level GitHub management, each agent solves a unique developer challenge. This is a detailed introduction to the work of these agents, the foundations of technology, and how they adapt to the modern cloud-native and DevOps ecosystem.

1. Big Query Data Agent

BigQuery data proxy brings natural language automation into data pipeline creation and management in Google’s BigQuery platform. This agent is aimed at data engineers and analysts who want to focus on insights rather than boilerplate data pipelines.

Key Features:

• Automatic data ingestion: Build and manage data pipelines from sources like Google Cloud Storage with simple tips, reducing the need for custom ETL scripts.
• Zero code data quality: Keep data quality and consistency with AI-driven inspections and transformations without manual encoding.
• AI-assisted data preparation: Automate data cleaning, metadata generation and schema evolution, supporting structured and unstructured data.
• Dialogue interface: Developers can describe pipeline logic in natural language, and agents can generate and optimize necessary SQL or DataFrames.

Technology Foundation:

Built on Gemini, the agent utilizes LLM-driven intent recognition and code generation, and is tightly integrated with BigQuery’s knowledge engine to allow metadata to attract data discovery and pedigree.

2. Laptop Agent (Company Laptop)

Laptop agents (available as enterprise laptops) are available and enhance BigQuery laptops with end-to-end AI-powered analytics and model building.

Key Features:

• EDA and functional engineering: Run exploratory data analysis (EDA) and functional engineering with conversation prompts and automate tedious data science workflows.
• Seamless ML prediction: Generate predictions and models directly in your notebook, minimizing boilerplate code and manual tweaks.
• Well-planned knowledge base: Organize and synthesize research, documents, and datasets into interactive notebooks that teams can reuse.
• Content synthesis: The summary finds that generating FAQs can even produce an asynchronously consumed audio summary.

Technology Foundation:

NoteBookLM Enterprise is different from regular NotebookLM products, which integrates it into BigQuery notebooks, uses timely-based controls and strictly controls enterprise security and collaboration.

3. Finder Code Assistant

Looker Code Assistant embeds Generative AI directly into Looger’s data exploration and BI platform, allowing non-technical users to access analytics without sacrificing power.

Key Features:

• Natural Language Query: Users ask questions in simple English and receive visualizations, Python code or interactive charts as output.
• Custom visualization and lookml: Generate LookML and JSON formatting options from the prompts, speeding up dashboard development.
• Positive insights: Explain the analytical method and ask follow-up questions to enhance trust and accessibility.
• Data context awareness: Use Looker’s semantic layer to ensure that queries are related to business definitions.

Technology Foundation:

The Assistant, supported by Gemini and Looker’s Explore API, converts natural language into optimized Looker queries, SQL and Visual Code, bridges the gap between business users and analytics teams.

4. Database Migration Agent

Database migration agents (DMS with Gemini assisted) simplify and speed up the transition from traditional databases (e.g., MySQL, Oracle, SQL Server) to modern scalable Google cloud databases (e.g., Spanner, Cloud SQL, and AlloyDB).

Key Features:

• AI-driven mode and code conversion: Review and convert stored programs, features, and patterns to cloud-native formats, reducing manual effort and migration risks.
• Minimum downtime: During the migration process, the leverage continuously replicates the downtime near zero.
• Interpretable migration: Provides side-by-side comparisons of legacy and object code, and provides detailed explanations to developers.
• Serverless operation: Fully managed by Google Cloud and does not require infrastructure.

Technology Foundation:

The agent uses Gemini to understand and translate database logic, verify migration results, and guide users through each step of the process.

5. GitHub proxy (Gemini Cli GitHub action)

Gemini Cli GitHub Action is an open source autonomous AI proxy that enhances GitHub workflow by automating regular repository management tasks.

Key Features:

• Question classification: Automatically tag, prioritize, and route GitHub issues based on content and project context.
• Pull request comment: Review code changes, propose improvements and provide instant feedback to reduce the burden of manual code review.
• Cooperation on demand: Developers can delegate tasks by tagging agents in the issue or PRS (e.g., “Write a test for this error”).
• Customizable workflow: Ships with default workflows, but fully open source, meet team-specific needs.

Technology Foundation:

The proxy is built on the Gemini CLI, responding to GitHub events is out of sync, uses the project context to perform accurate operations, and is directly integrated into the GitHub operation pipeline.

Summary: Google’s new AI agent targets developers

Summary

These agents represent a major leap forward Automatic developer tools– AI is responsible for repetitive, prone tasks, allowing developers to focus on innovation and business logic. They lower the technology layer for analytics, migrations, and collaboration, while maintaining (even increasing) caps (even increasing) caps through cloud-scale data and code.

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