Snowflake Intelligence + Anomalo: Bringing Data Quality Intelligence Directly Into Snowflake

Enterprises have been promised for years that AI would make analytics effortless: “Ask your data anything.” But AI can only be as good as the data behind it. Without trustworthy, contextual, high-quality data, even the most advanced AI agents struggle to provide reliable answers.

Snowflake Intelligence represents a major step forward by embedding agentic AI directly into the governed Snowflake environment. It gives users a powerful new way to interact with data without writing SQL, building dashboards, or relying on engineering teams for every question.

To answer questions accurately, Snowflake Intelligence uses Cortex agents. These agents need context that goes beyond schema and semantics. They need an understanding of how the data behaves over time and whether it can be trusted. That’s where Anomalo comes in.

With Anomalo integrated into Snowflake Intelligence, Snowflake customers can now tap into behavioral data quality insights directly inside Snowflake, giving AI agents the context they need to produce grounded, trustworthy answers.

Why Snowflake Intelligence Needs Data Quality Context

Snowflake Intelligence agents are designed to interpret intent, reason over governed data, and return answers directly inside Snowflake. This solves a long-standing problem in analytics: access.

But there is a second problem every data team recognizes immediately: understanding the data itself.

When a business user asks, “Why is revenue down today?”, an AI agent needs more than access to a table or metric. It needs to know:

• Is the data complete?
• Has volume changed unexpectedly?
• Did the schema shift?
• Is this within normal seasonal behavior?

Without this context, even well-intentioned AI agents risk:

• Misdiagnosing the cause of metric changes
• Generating misleading explanations
• Producing SQL that appears correct but is based on faulty data

By providing behavioral data quality signals to Snowflake Intelligence, Anomalo fills this gap, enabling the creation of data quality-aware agents. These signals are derived from continuously modeling how data changes over time.

What Snowflake Intelligence Gains From Anomalo

With Anomalo’s data quality signals available, Snowflake Intelligence agents can:

• Detect whether unexpected results are driven by data quality issues
• Explain why an anomaly occurred, not just that it occurred
• Incorporate quality scores and anomaly metadata into responses
• Provide higher-confidence answers to business users

For example, a Snowflake Intelligence agent can now answer: “Is this dashboard drop real, or is there a data issue upstream?”

And respond with:

• A definitive explanation
• Root-cause signals from Anomalo
• Historical behavioral context
• Impacted data

All without leaving the Snowflake environment. This context is possible because Anomalo models behavioral data quality patterns across platforms and exposes them through enterprise-secure APIs.

High-Level Technical Architecture

Below is a simplified view of how Anomalo and Snowflake Intelligence work together:

Components

Anomalo

• Executes data quality checks
• Models historical behavior, trends, seasonality, drift, and anomalies
• Exposes insights via REST APIs consumable by Snowflake Intelligence

Snowflake Intelligence Agent

• Interprets user intent
• Determines when quality context is needed
• Calls Anomalo’s REST endpoints as custom Snowflake tools
• Responds to users with grounded, contextual reasoning

Example Flow

1. Abnormal data arrives (e.g., 47% drop in volume).
2. Anomalo detects the anomaly and records metadata.
3. A user asks Snowflake Intelligence, “Why is revenue down on the dashboard today?”
4. The agent consults its orchestration instructions and calls Anomalo’s APIs.
5. Anomalo returns behavioral context, including the anomaly and affected data.
6. Snowflake Intelligence synthesizes the insight and returns a clear, trustworthy explanation to the business user.

This is the next generation of enterprise analytics, where AI agents are informed by the behavior of the data itself, similar to the intelligence that powers Anomalo’s AIDA platform.

How Snowflake Intelligence Calls Anomalo’s REST APIs

The workflow for building Snowflake Intelligence agents that leverage data quality insights from Anomalo is a simple one. Stored procedures are created in Snowflake to call the Anomalo APIs, which are then used as custom tools within the Snowflake Intelligence agent builder.

Typical Anomalo Endpoints Used by Snowflake Intelligence

Having access to Anomalo’s APIs provides Snowflake Intelligence agents with valuable insights into the quality of data assets inside and outside of Snowflake. Insights include:

• Which tables are being monitored for quality
• Current and historical quality assessments
• SQL to isolate anomalous rows of data
• Root cause analysis to accelerate remediation

Creating Custom Tools Inside Snowflake Intelligence

For each Anomalo endpoint:

1. Create a Snowflake stored procedure that wraps the API call.
2. Register the procedure as a custom tool inside Snowflake Intelligence.
3. Add tool instructions (provided by Anomalo) that tell the agent how to use the endpoint.
4. Add orchestration instructions that guide the agent across endpoints holistically. This allows the agent to decide, for example, when to check quality scores vs. when to fetch recent anomalies.

Leveraging the output from these custom tools, the Snowflake Intelligence agent’s reasoning chain will be able to generate data quality insights, provide explanations, and pinpoint anomalous records. This comprehensive information empowers both technical and non-technical users to detect and resolve data quality issues earlier in the data pipeline, thus minimizing downstream impacts.

Key Customer Use Cases

Can I trust this data right now?

Snowflake Intelligence agents can retrieve Anomalo’s quality scores and recent anomalies directly inside Snowflake. This gives teams a shared, real-time view of data trust without leaving the Snowflake environment.

Why did this KPI change?

When metrics shift unexpectedly, Snowflake Intelligence evaluates Anomalo’s behavioral signals to determine whether the change is caused by ingestion issues, schema drift, malformed records, or expected seasonality. Clear, actionable explanations boost trust levels for your data consumers and enable self-service so you can scale business value.

Can I trust what the AI is telling me?

By incorporating data quality context into agent reasoning, Snowflake Intelligence produces safer SQL, more accurate insights, and recommendations grounded in how the data actually behaves over time. This boosts confidence in your Snowflake Intelligence agents, deepens their depth of analysis, and understanding that goes beyond a point-in-time snapshot of your data.

Reliable, Governed AI Powered by Data Quality

Snowflake Intelligence represents the future of enterprise analytics: AI agents that can understand user intent, synthesize context, and deliver answers in a governed environment.

But for these agents to be truly intelligent, they need to understand the behavior and trustworthiness of the data beneath them.

That’s what Anomalo delivers with:

• No data movement required
• Behavioral modeling that gives agents real context
• REST API integration that is simple, secure, and Snowflake-native

Together, Snowflake + Anomalo make the AI-powered enterprise real and reliable.

See for Yourself

Want to see how cross-platform data quality can power Snowflake Intelligence agents? Watch this Snowflake Intelligence + Anomalo demo video or request a personalized demo with our joint Snowflake + Anomalo technical team.