Data + AI Observability For Databricks

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.

Databricks Partner Connect

Available directly in Databricks Partner Connect — integrate Monte Carlo into your lakehouse in a few clicks with no manual configuration required.

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.

Mosaic AI & AgentBricks Observability

Native observability for Databricks Mosaic AI agents and AgentBricks — monitor AI agent inputs, behavior, and outputs end to end.

AI/BI Integration

Monitor the quality of data underpinning Databricks AI/BI insights with AI-powered anomaly detection and automated root cause analysis.

Industry Competency Badges

Recognized across Financial Services, Healthcare, Retail, Media, and Technology for verified customer success.

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.

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

First 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.

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

Customer Stories

Reliability Designed for the Enterprise

Our customers scale trust, reduce risk, and deliver better business outcomes. See how you can too.

How Axios Is Delivering Reliable AI with Agent Observability

The Challenge:

Axios needed to monitor across their data + AI lifecycle including agent context, performance, behavior, and outputs.

The Solution:

Leveraged Agent Observability for full agent visibility integrated into a robust incident management workflow.

How JetBlue Improved Internal “Data NPS” By 16 Points YoY

The Challenge:

When a data migration improved data usage, increased access brought increased scrutiny. And the trustworthiness of the data took center stage.

The Solution:

Operationalizing data + AI observability and leveraging Monte Carlo's in-app features to measure the outcomes.

Nasdaq's Journey to Reliability with Monte Carlo

The Challenge:

Nasdaq generates 6,000 reports per day across 35 services and 2,200 users. The question is—how do you make that much data reliable?

The Solution:

The team deployed Monte Carlo to monitor its entire data lake via a multi-step deployment.