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2025 Data + AI Governance Partner of the Year

Agent Trust for Databricks

Monte Carlo natively monitors, troubleshoots, and improves your Databricks agents and the data feeding them, so you can deploy trusted AI in production.

Trusted by 400+ enterprises

T. Rowe Price PepsiCo Cisco Comcast Nasdaq Disney Gap Highmark Target Salesforce
Partnership

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 Databricks 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

Agent Bricks Observability

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

AI-ready

Business Intelligence Integration

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

Databricks BI

Industry Competency Badges

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

Multi-industry
Capabilities

Agent and data observability across your Databricks stack

Monte Carlo closes the full trust loop: raw data in Delta Lake, the context your agents retrieve, and the outputs they produce. So when an agent gives a bad answer, you know exactly what caused it.

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 Databricks 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
Context, performance, and behavior monitoring for Agent Bricks and Databricks AI agents.
Context quality monitoring
Monitor the Delta tables your Databricks agents retrieve from. Catch stale, incomplete, or anomalous context before it reaches the agent's 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.
Agent Bricks integration
Native integration with Databricks Agent Bricks — 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 Databricks AI and 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.

Four things go wrong with your agents: the context they retrieve, what they cost, how they behave, and what they produce. Only Monte Carlo catches all four.

  • 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 Agent Bricks, Genie, and anything else you run on Databricks
  • Lets you choose: retain full human oversight, enable fully autonomous operations, or anywhere in-between
See agent observability
The four stages Monte Carlo monitors across the Databricks agent loop: context, performance, behavior, and output — ending in a validated agent output.

Ready to trust the agents running on Databricks?

Join 300+ Databricks customers using Monte Carlo to eliminate data downtime and build reliable AI.

Get a demo

G2 names Monte Carlo as #1 leader for the 13th consecutive quarter

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