How Cortex Code and DataOps Automation Work Together to Deliver Trusted Data

As AI becomes more embedded in today’s data platforms, one pattern is becoming clear: the best outcomes come from specialized AI agents working together, each focused on a clearly defined responsibility.

Snowflake Cortex Code (CoCo) and DataOps.live Metis are both agentic AI systems built for different stages of the data lifecycle and different problems:

• Cortex Code helps write Snowflake code.

• Metis ensures that code delivers trusted data products in production.

That separation of responsibilities is what makes an agentic AI work in the real world. Specialized agents work together, each in its zone of strength.

When AI agents work together, results improve

The most effective AI systems are not single, all-purpose agents. They are collections of specialized agents.

Cortex Code and Metis reflect this design principle.

Cortex Code from Snowflake operates inside the Snowflake AI data cloud platform. It assists developers with SQL and Snowpark development, helping ensure Snowflake features are used correctly and efficiently. In Snowflake terms, this is development, moving from idea to working code faster, with less friction.

Metis from DataOps.live operates at the delivery layer. It focuses on what happens after code is written, automating the processes required to properly deliver data products into production. This includes testing, promotion workflows, governance rules, observability, AI ready scoring, and auditability.

Each agent has a distinct responsibility:

• CoCo - How should this code be written to work correctly in Snowflake?
• Metis - How should that code be tested, approved, promoted, and operated so the resulting data can be trusted in production?

With CoCo and Metis working together, development speed and operational discipline are achieved. Code can be written faster without increasing risk, and data products can be delivered into production with confidence.

Writing code is not the same as delivering trusted data

Looking at how Snowflake is used in enterprise organizations highlights a long-standing reality. Most organizations do not struggle with Snowflake syntax or feature usage. Those are development challenges, and Cortex Code is designed to help address them.

Where organizations continue to struggle is reliable data product delivery in production.

That struggle shows up in questions like:

• Who approved this change?
• How was it tested before reaching production?
• Is this allowed in a regulated domain?
• Does this meet AI Readiness?
• What happens when multiple teams deploy at the same time?
• How do we explain this change to an auditor months later?

These are not code writing problems. They are data delivery and operational problems, and they exist outside the Snowflake platform by design.

Metis automates the last mile to production

Even with agentic code generation, DataOps automation is needed for having trusted data in production.

Built into the DataOps.live Automation Platform, Metis automates the delivery layer, managing CI/CD workflows, promotion rules, testing expectations, governance controls, ownership, and observability. It ensures that Snowflake code can move through environments safely, consistently, and with the traceability required at enterprise scale.

Rather than focusing on how code is written, Metis focuses on how data solutions are operated over time. It provides the automation required to make production delivery repeatable, auditable, and reliable.

Seeing agentic collaboration in action

This separation of responsibilities between CoCo and Metis is especially clear when you see how the work is performed across the complete data-solution delivery lifecycle.

The table above illustrates how responsibilities shift as AI-based automation is introduced.

For most organizations, troubleshooting and error analysis through testing, monitoring, and promotion rely entirely on the user. With Snowflake’s Cortex Code, the technical burden of code analysis and implementation is reduced, but delivery activities such as sandbox creation, pipeline execution, monitoring, and promotion are still entirely manual and user-driven.

When Cortex Code is working with Metis, the workflow automation is complete. Cortex Code writes and fixes Snowflake code, while Metis oversees the DataOps automation.

To see all of this in action, watch the attached video demonstration:

Why DataOps automation matters even more in the AI era

As organizations adopt AI-driven analytics and applications, the cost of unreliable data increases dramatically. AI systems amplify existing data issues rather than hiding them.

Snowflake, including Cortex Code, helps teams write better code faster. Snowflake does not provide DataOps automation for delivering trusted data in production.

That automation becomes critical as AI adoption accelerates. Without it, even well-written code can result in data that is inconsistent, poorly governed, or difficult to audit, undermining trust in downstream AI systems.

A complementary, Snowflake-first model

Nothing fundamental has changed.

Snowflake continues to provide a powerful, extensible data platform, now enhanced with AI to assist with Snowflake code development. DataOps.live extends that platform by automating how data products are delivered, governed, and operated in production.

Cortex Code accelerates Snowflake development at the point of creation. Metis governs how that code is promoted, operated, and trusted in production at enterprise scale.

Together, they support the full lifecycle of modern data products, from development to reliable production delivery.

The DataOps.live Automation Platform is free to try, and you get 500 free minutes every month across test, dev, and prod. See how easy it is to operationalize your Snowflake data for trusted AI — Sign up today.