Several weeks ago, we released a series of changes that tightened the thresholds when monitoring the volume (row count) of a table.
In some cases, these made thresholds more sensitive or noisy than desirable, putting some customers at risk of alert fatigue. Today, we released adjustments to the model to reduce much of that noise. In many cases, this will cause volume thresholds to moderately widen / loosen.
Slack users can now initiate Monte Carlo's Troubleshooting Agent directly from Slack alerts. Upon completion, a troubleshooting summary is provided along with a link to open the findings in Monte Carlo for further investigation!
• One-click troubleshooting: "Troubleshoot alert" button on all Monte Carlo Slack alerts • Thread-based interactions: Agent responses stay organized in the alert thread • Real-time status updates: Clear feedback when troubleshooting starts and completes • Seamless handoff: Summary results in Slack with "Open in Monte Carlo" link for detailed findings
Collaboration just got easier! You can now tag teammates directly in alert comments using @username to bring them into the conversation.
When you mention someone, they’ll receive an email notification with a link to the alert - making it simple to jump in, review context, and take action.
Good news! Navigation in Monte Carlo just got easier. You will have access to all your tabs on a left-side bar in the new experience. Watch a video walk-through
How it works, top to bottom:
Agent instructions enables you to provide custom context about your data platform that troubleshooting agent can leverage during alert analysis.
Navigate to Settings → AI Agents to add custom context about your data platform.
Use-cases might include:
• Platform-specific rules: Define naming conventions, data sources, and architectural patterns • Custom context: Share information about your ETL tools, schema organization, and data lineage • Shared workspace instructions: Context is shared across your entire Monte Carlo account, not just individual users
Examples:
• “All tables that start with ‘sfdc_’ come from Salesforce” • “We use dbt core to run all tables in the ‘prod’ schema but the dbt integration is not yet connected to Monte Carlo” • “Our staging tables are refreshed every 4 hours during business hours only”
You can now manually trigger metric monitors either from the UI (via the Run button) or through the API. This gives teams flexibility to run monitors exactly when their data pipelines finish loading, avoiding wasted compute from partial runs and ensuring results reflect the latest data.
You can now schedule monitors to run immediately after your ETL jobs complete, reducing time-to-detection (TTD) from hours to minutes. Whether you use Airflow, dbt, or other supported orchestration tools, Monte Carlo can trigger monitors as soon as a job finishes successfully.
Snowflake users may now raise monitor query timeouts up to 1 hour directly from the UI.
Support for BigQuery, Redshift, and Databricks is in progress.
Note: raising the timeout increases usage of your Snowflake warehouse. Use longer timeouts only when needed to avoid unnecessary spend.
Comparison monitor is now available for Salesforce CRM. This allows users to ensure integrity of data flowing from CRM to warehouse. Docs.
You can now connect Monte Carlo to Datadog Incident Management. Create or link incidents directly from alerts, sync status and ownership automatically, and add Datadog as a notification channel.
