November 4, 2025

Better Together: Genesis and Snowflake Cortex Agents API Integration

From gold data to governed AI agents that answer business questions in hours

Most enterprises still face a massive data backlog.

The integration of Genesis's Cortex Agent APIs with Snowflake Intelligence (SI) dramatically accelerates the path to conversational analytics. Genesis first delivers verified, governance-ready gold data inside Snowflake. Snowflake Intelligence's Semantic Views then leverage this gold data, automatically adding rich, business-contextual metadata.

The Cortex Agent APIs act as the essential bridge, allowing Genesis-created agents to interpret natural language queries (conversational analytics) by intelligently reading both the gold data and the semantic metadata. This means a user's question is not just matched to a column name, but to its true business meaning and relationships, unlocking a wider, more contextual range of analytical possibilities directly within the Snowflake ecosystem. This partnership enables automated data discovery, mapping, and transformation to converge seamlessly with next-generation conversational AI.

Genesis as Data Pipeline and

Cortex Agent Builder

From Raw Data to Intelligent Agents — Fully Automated

Genesis data engineering agents already build the pipelines that take raw bronze data to gold business-ready data. Now, through the Snowflake Cortex Agent REST API, Genesis extends that automation into the creation and supervision of intelligent Cortex agents.

How It Works

Data Foundation: Genesis automates data discovery, mapping, validation, and transformation to deliver verified gold data.

Agent Creation: Once gold data is available, Genesis uses the Cortex Agent REST API to automatically create, test, and maintain analysis agents — customizing prompts, generating semantic models, seeding search indices, and assigning appropriate tools.

Continuous Testing and Monitoring: Genesis agents test, debug, and QA Cortex agents automatically, ensuring reliability and compliance within Snowflake.

Iterative Improvement:

When agents succeed, Genesis learns and reinforces what works.
When agents fail, Genesis updates the semantic model or extends pipelines from bronze to gold.

The result: Genesis becomes the Cortex Agent Builder and Supervisor — automating what once required entire engineering and analytics teams. Pipelines and agents evolve together, forming a self-improving system.

Why Genesis and Snowflake Are a Natural Pair

Run Where Your Data Lives: Genesis operates as a Snowflake Native App via Snowpark Container Services. No replication, no external processing — your data, security, and governance stay intact.
Pipeline-to-Agent Flow: Genesis automates the entire journey from raw data to live agents ready for business use.
Self-Improving System: Every interaction refines the models, pipelines, and agents over time.

The Joint Impact

Speed: Insight cycles compress from months to hours.
Trust: Every answer is grounded in verified gold data.
Efficiency: Automation frees engineers for higher-value work.
Scalability: Pipelines and agents continuously improve as usage grows.

Getting Started: Your First Use Case in Under a Day

Action

Objective

Step 1. Deploy Genesis within your Snowflake account.

Run pipelines and agent tooling inside your trusted environment.

Step 2. Build your gold tables.

Let Genesis automate data mapping, testing, and transformation to deliver verified, audit-ready gold data.

Step 3. Generate a semantic model.

Use Genesis Cortex Agent Tools to create and validate a preliminary business model structure.

Step 4. Seed search and document indices.

Connect policies, contracts, or text-based references through Cortex Search, adding unstructured data context to the agent.

Step 5. Create your first Cortex agent.

Use Genesis to automate the prerequisites for creating a Cortex agent, focusing on connecting the agent to the gold data and semantic layer.

Step 6. Enable Conversational Analytics with Snowflake Intelligence Semantic Views to Improve Accuracy and Maximize Time to Value.

Leverage Genesis to create, test and maintain Semantic Views to power the Snowflake Intelligence conversational interface. This eliminates query tuning and complex data interpretation overhead, dramatically improving the Time to Value (TTV). This integration enables richer, context-aware queries on both the data and its metadata.

Step 7. Activate feedback and iteration.

Monitor agent performance, measure query accuracy against the semantic views, and update models or pipelines as needs evolve.

Genesis and Snowflake make AI-driven, governed, and conversational analytics practical for every enterprise. When pipelines and agents evolve together, teams move from data backlog to trusted insight — with confidence and speed.