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 Databricks’s Data Intelligence Platform.
Validated at every level of the Databricks ecosystem
The highest tiers of technical validation and partner recognition from Databricks — so your team can buy and deploy with confidence.
2025 Data Governance Partner of the Year
Officially awarded by Databricks for innovation, joint customer success, and leadership in data + AI observability across the Data Intelligence Platform.
Award Winner
Databricks Partner Connect
Available directly in Databricks Partner Connect — integrate Monte Carlo into your lakehouse in a few clicks with no manual configuration required.
Partner Connect
Unity Catalog Native Integration
First end-to-end observability platform to integrate with Delta Lake and Unity Catalog across all endpoints — down to the BI layer.
Native
Mosaic AI & AgentBricks Observability
Native observability for Databricks Mosaic AI agents and AgentBricks — monitor AI agent inputs, behavior, and outputs end to end.
AI-Ready
AI/BI Integration
Monitor the quality of data underpinning Databricks AI/BI insights with AI-powered anomaly detection and automated root cause analysis.
AI/BI
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 lakehouse
Monte Carlo covers the full journey — from raw data in Delta Lake through Unity Catalog to what your Mosaic AI agents produce.
Data layer
Lakehouse observability
Automated monitoring across Delta Lake, Unity Catalog, and all Databricks pipelines.
Automated anomaly detection
ML-powered monitors learn your data patterns across Delta tables and flag deviations in volume, freshness, schema, and distributions automatically.
End-to-end lineage
Column-level lineage from ingestion through Databricks Workflows to every downstream BI tool, AI model, and Mosaic AI agent — zero instrumentation needed.
Unity Catalog Metrics monitoring
Monitor the integrity of Unity Catalog Metrics definitions, ensuring key business KPIs remain accurate and consistent across domains and dashboards.
Databricks Workflows integration
Correlate data anomalies directly to the specific Databricks Workflow or task that caused the issue — enabling faster, full-lifecycle incident resolution.
Agent layer
AI agent reliability
Input validation and context reliability for Mosaic AI and AgentBricks agents.
Pre-flight data validation
Monitor the Delta tables your Mosaic AI agents retrieve from. Catch stale, incomplete, or anomalous data before it reaches the agent context window.
RAG pipeline observability
Monitor unstructured data powering LLMs and RAG pipelines in Databricks — detect anomalies in documents, chat logs, and embeddings before they degrade agent quality.
AgentBricks integration
Native integration with Databricks AgentBricks — monitor agent inputs, behavior, and outputs without custom instrumentation or code changes.
Unstructured data monitoring
AFirst platform to monitor both structured and unstructured data in Databricks — detect sentiment shifts, missing text, and format anomalies in AI-feeding datasets.
Output layer
AI output observability
Monitor what agents produce and trace failures back to root cause in your lakehouse.
Agent output monitoring
Track what your Mosaic AI and AI/BI agents produce over time — detecting drift, degradation, or unexpected behavior before it reaches customers.
Root cause tracing
When an agent misbehaves, Monte Carlo traces the failure through the full lakehouse stack — from agent output to the specific Delta table or pipeline that caused it.
Incident routing
Route AI-related incidents to the right owner instantly via Slack, Teams, PagerDuty, and Jira — with automatic blast radius scoping across all consumers.
SLA & reliability tracking
Set reliability targets for your AI systems. Track data SLAs, agent uptime, and input quality trends to demonstrate AI readiness to leadership.
Agent Observability
If the data is wrong, the agent is wrong.
Monte Carlo is the first observability platform built to monitor the full Mosaic AI and AgentBricks agent loop — from data input through agent output — so your team catches failures before customers do.
Monitor Delta Lake data quality before agents consume it
Trace every agent decision back to its lakehouse source
Observe RAG pipelines and unstructured data inputs end to end
Works natively with Mosaic AI, AgentBricks, and Databricks AI/BI
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
