DinoAI Agent
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The DinoAI Agent provides a flexible, conversational interface for working with DinoAI. It enables you to automate analytics engineering tasks with warehouse-aware AI that writes directly to your dbt™ project files.
Agent Mode transforms analytics engineering workflows by:
Eliminating Repetitive Tasks: Automating up to 99% of rote work like column renaming, source updates, and documentation generation
Accelerating Development: Creating models, tests, and documentation in seconds rather than hours
Ensuring Consistency: Maintaining standards across your dbt™ project through DinoRules integration
Reducing Context Switching: Eliminating the need to copy code between tools or query the warehouse separately
All this is achieved with built-in guardrails that prevent resource-intensive operations, protect your data warehouse, and ensure user oversight of all changes.
Click the DinoAI icon (🪄) in the left panel of the Code IDE.
Select "Agent Mode" at the bottom of the DinoAI panel.
Type your prompt.
Review and accept DinoAI's suggestion(s) to add or modify files directly in your project.
Writes SQL models, YAML files, tests, and documentation directly into your dbt™ project without copy-pasting. Creates new files and updates existing ones seamlessly.
Understands your data warehouse structure by querying metadata from information schema. Identifies available tables and columns, recognizes data types, and works with multiple warehouse types (Snowflake, BigQuery, Redshift, Databricks, etc.).
Allows you to explicitly target specific files or folders for DinoAI to consider. Provides precise control by letting you add specific files, active files, or entire directories as context.
Prevents resource-intensive operations with safety measures and requires user approval before creating, modifying, or running files.
Explore the following use cases to see Agent Mode in action:
Creating DBT Sources from Data Warehouse
Generating Well-Structured Base Models
Building Complex Intermediate/Marts Models
Bulk Documentation Generation
Data Pipeline Configuration
Honors your ., ensuring consistent formatting, naming conventions, and project structure across your entire codebase.
