With their extended partnership, data + AI observability leader and the Data AI Cloud bring reliability to structured and unstructured data pipelines in Snowflake Cortex AI. –

Announced today, Monte Carlo and Snowflake are delivering end-to-end observability across both structured and unstructured data pipelines powering agentic AI applications in Cortex AI, the AI Data Cloud’s AI development suite. 

This joint solution will make it easier and faster for data organizations to build, train, and deploy reliable LLMs in Snowflake and drive value with their AI investments. 

Ensuring trust in an agentic future

Organizations of all sizes are realizing the impact of AI for the enterprise. Revolutionary features like Snowflake’s Cortex AI have democratized access to powerful AI for everyone with the data to activate it. 

But what happens when your AI solution produces bad responses?

Maybe your chatbot will advise you to put glue on pizza or sell your customer a $1 Chevy Tahoe, or even worse, risk your company’s reputation or bottomline. Unreliable data and AI solutions costs organizations millions per year, wasting valuable time and resources, and instead of driving value, data teams are left scrambling to understand what went wrong – and how to fix it. 

That’s why Monte Carlo is excited to extend our partnership with Snowflake to offer end-to-end observability across structured and unstructured data pipelines powering Cortex AI applications.

Read on for more details and find out how we’re thinking about unstructured data observability for AI. 

Why observability for unstructured data?

Success for production-ready agentic AI depends on the unstructured data that’s used to train, fine-tune, and augment it. 

To achieve agentic reliability, teams will not be successful by observing model outputs in a vacuum. AI failures often begin with data issues and cascade through silent model drift, systems buckling at scale, and unforeseen code changes. That’s why data + AI observability must go beyond monitoring inputs or outputs: it needs to cover the entire stack.

For data + AI observability, that means integrations across the core system components. In other words, the four ways data + AI products break: in data, system, code, and models. Detecting, triaging and resolving issues will require visibility into structured/unstructured data, orchestration/agent systems, prompts, contexts and model responses. 

With Monte Carlo and Snowflake, joint customers like Drata will have end-to-end visibility into the entire data + AI system, including unstructured data pipelines. 

Committed to the future of trustworthy data + AI

Much like the observability of traditional data pipelines, observability can only truly be effective when it’s administered end-to-end. AI failures often begin with data issues, but that failure cascades into silent model drift, buckling systems, and unforeseen code changes. 

At Monte Carlo, we’re committed to defining the future of reliable AI for enterprise data teams. That means extending coverage to every layer and integration that could impact the reliability of your Cortex AI applications—and that begins today with the structured and unstructured data that powers it. 
Interested in learning more? Reach out to our team to schedule a demo.