Monte Carlo’s Data + AI Observability Platform gives your team full visibility into the health of your AI systems — from data input to agent output — natively on Snowflake’s AI Data Cloud.
Validated at every level of the Snowflake ecosystem
The highest tiers of technical validation and partner recognition from Snowflake — so your team can buy and deploy with confidence.
Elite Technology Partner
The highest tier in the Snowflake Partner Network, recognizing technical depth and joint customer success.
Snowflake Partner Network
Snowflake Ready Technology
Officially validated for performance, security, and reliability on the AI Data Cloud.
Technical Validation
Snowflake Intelligence Partner
The #1 observability platform for Snowflake AI agents — natively integrated with Snowflake Intelligence, Cortex, and CoCo.
Snowflake Intelligence
Available on Snowflake Marketplace
Procure directly through Snowflake Marketplace — counts toward committed spend and simplifies procurement.
Marketplace
Cortex Agent Observability Partner
First Data + AI Observability platform with native monitoring for Snowflake Cortex AI agents.
AI-Ready
Industry Competency Badges
Recognized across Financial Services, Healthcare, Retail, Media, and Technology for verified customer success.
Multi-Industry
Capabilities
Observability at every layer of your stack
Monte Carlo covers the full journey — from raw Snowflake data through to what your AI agents produce.
Data layer
Data observability
Automated monitoring across every Snowflake table, schema, and pipeline.
Automated anomaly detection
ML-powered monitors learn your data patterns and flag deviations in volume, freshness, schema, and distributions — no thresholds needed.
End-to-end lineage
Column-level lineage from ingestion through dbt to every downstream BI tool and AI model — zero instrumentation required.
Custom SQL monitors
Define business logic rules on any Snowflake table. Runs on your compute — no data leaves your environment.
Impact analysis
When a table breaks, instantly see every downstream consumer — dashboards, AI models, Cortex agents — before you remediate.
Agent layer
AI agent reliability
Input validation and context reliability for Snowflake Cortex agents.
Pre-flight data validation
Monitor the Snowflake tables your Cortex agents retrieve from. Catch stale or anomalous data before it reaches the agent context window.
Context reliability scoring
Score the quality and freshness of every data input to your agents — always know if an agent is reasoning on data you can trust.
Cortex & CoCo integration
Native integration with Snowflake Cortex Agents and Cortex Code — no additional instrumentation for full pipeline visibility.
Snowflake Intelligence monitoring
As a Snowflake Intelligence partner, Monte Carlo surfaces data health signals directly within the Intelligence layer.
Output layer
AI output observability
Monitor what agents produce and trace failures back to root cause.
Agent output monitoring
Track what your Cortex agents produce over time — detecting drift, degradation, or unexpected behavior before it reaches customers.
Root cause tracing
When an agent misbehaves, trace the failure back through the full stack to the specific Snowflake table or pipeline that caused it.
Incident routing
Route AI-related incidents to the right owner instantly — with Slack, PagerDuty, and Jira integrations and automatic blast radius scoping.
SLA & reliability tracking
Set reliability targets for your AI systems. Track data SLAs, agent uptime, and input quality trends over time.
Agent Observability
If the data is wrong, the agent is wrong.
Monte Carlo is the first observability platform built to monitor the full Cortex agent loop — from data input through agent output — so your team catches failures before customers do.
Discover all Cortex Agents in a single click — no instrumentation
Monitor data quality before agents consume it
Trace every agent decision back to its Snowflake source
Works natively with Snowflake Intelligence, Cortex, and CoCo
TL;DR What is AI observability? AI observability is the practice of monitoring AI applications end-to-end — from source data to model output — to detect and resolve the silent, probabilistic failures that traditional monitoring tools miss. Why it matters AI can fail silently — wrong outputs with no error signal Data issues upstream often look … Continued
Hallucination—when an AI confidently generates false or nonsensical outputs—is the most notorious failure mode for AI applications. But is it really the one you need to worry about? A lot of noise has been made about hallucinations in recent months. As more companies introduce AI into production, the issue of hallucinations is perennially in the … Continued
Building Reliable Foundations for Data + AI Systems It’s no big revelation that data teams are being challenged to do more with AI. But while deploying an AI prototype has never been easier, operating those applications safely in production is harder than ever. Achieving high-quality data remains one of the most critical components for trustworthy … Continued
Data observability improves data quality with features like data monitoring, lineage, automated root cause analysis, and data health insights to detect, resolve, and prevent data anomalies.
The data estate is evolving, and data quality management needs to evolve right along with it. Here are three common approaches and where the field is heading in the AI era.
As we approach the final quarter of 2025, it’s time to step back and examine the trends shaping the face of data and AI for 2026. While the headlines might focus on the latest model releases or benchmark wars, it’s clear to anyone actually using these technologies that the latest headlines are far from the … Continued
