Monte Carlo extends observability coverage from Delta Lake tables and Lakeflow, Databricks’ unified data engineering solution, to agents built on Agent Bricks — giving enterprises a continuous, unified view across their entire Databricks environment

Monte Carlo, the agent trust platform for enterprise AI, announced its support for Agent Bricks, Databricks’ platform to build, deploy and govern AI agents on enterprise data. With this integration, Monte Carlo extends its observability capabilities to enterprises building and operating agents on the Databricks Data Intelligence Platform — completing a continuous, unified view across the full Databricks stack.

Integration with Agent Bricks: Observability Across the Full Databricks Stack

Enterprises running on Databricks rely on Monte Carlo to monitor the health of the data underlying their analytics and AI. Monte Carlo monitors Delta Lake tables for freshness, schema drift, and volume anomalies; tracks health and lineage across Lakeflow, Databricks’ unified data engineering solution; and surfaces data quality issues before they reach downstream consumers. With the Agent Bricks integration, Monte Carlo extends that coverage into the agent layer.

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Monte Carlo now provides observability built on Databricks across three interconnected layers:

• Delta Lake & Data Tables: Continuous monitoring for data freshness, schema drift, volume anomalies, and quality degradation across the Delta tables that serve as the foundation for all downstream analytics and AI.
• Lakeflow: Health monitoring, anomaly detection, and end-to-end lineage across the data engineering workflows that ingest, transform, and orchestrate data across the Databricks environment.
• Agent Bricks: Observability across the tool calls, retrieval steps, model interactions, orchestration workflows, and data inputs that compose agents built on Agent Bricks — enabling teams to trace failures, validate data reliability, and identify the root cause of agent issues across the full stack.

As agents move into production, the reliability of agent behavior depends directly on the reliability of the data and infrastructure beneath them. Monte Carlo gives enterprises a continuous audit trail from raw data in Delta Lake to actions taken by deployed agents — making it possible to distinguish a data failure from a model failure from a pipeline failure, and to resolve issues before they affect end users or business outcomes.

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“Deploying agents in production means managing an entirely new layer of infrastructure — and most enterprises have no visibility into it,” said Barr Moses, co-founder and CEO of Monte Carlo. “Our integration with Agent Bricks changes that. Databricks customers now have a single, cohesive view of everything their agents run on and everything their agents do — from the data in Delta Lake to the decisions agents make in production. That’s what it takes to operate AI you can actually trust.”

For enterprises like Nasdaq, that trust starts with the data layer. “Even if you have access to all of the information in your entire data ecosystem, if you can’t trust the data, then it’s no good. For us, that’s where Monte Carlo comes in,” said Michael Weiss, AVP of Product Management Nasdaq.