Releasesby GlyphConnect your own MCP servers to Monte Carlo's agents
Monte Carlo now allows connecting custom MCP servers to its AI agent, Bayes, to pull in external context from tools like GitHub, Linear, or Notion.
Also shipped Oct 29, 2026Monte Carlo in October 2026 →
Gemini-powered Explore summaries in Looker are generally available
The ability to generate summaries of Looker Explores using Gemini assistance is now generally available.
Dashboard relationships are now generally available
Dashboard relationships, which enable multi-fact data models in AI/BI dashboards, are now generally available.
Looker bullet chart visualization is generally available
The bullet chart visualization type is now generally available in Looker, allowing users to evaluate metric performance against targets and thresholds.
Xero connector is available in Beta
The managed Xero connector is now available in beta for Databricks Lakeflow Connect, supporting incremental ingestion of accounting data.
Looker table row grouping is generally available
Table row grouping and associated menu options are now generally available in Looker, allowing hierarchical display of table data.
Looker Visualization Assistant is generally available
The Looker Visualization Assistant is now generally available.
More from Monte CarloEvery Monte Carlo release →
Match dbt Cloud runs by warehouse account, host or database
Monte Carlo now allows dbt Cloud integrations to match runs based on warehouse account, host, or database fields. This improves lineage and alert accuracy for environments where multiple connections share the same warehouse.
Monte Carlo's Terraform provider is now on the OpenTofu registry
Monte Carlo has published its Terraform provider and agent modules to the OpenTofu registry, enabling users to manage Monte Carlo infrastructure as code using OpenTofu.
Your agents’ end-user ratings, tied to the trace behind each answer
Monte Carlo now tracks end-user feedback for Snowflake Cortex and Databricks AI/BI Genie agents, displaying ratings and comments directly on traces.
Rate the issues Bayes finds through the Reinforcement Loop
Users can now provide feedback on issues identified by Bayes, Monte Carlo's AI agent. This allows users to ignore irrelevant issues and flag false positives, helping the agent improve its focus on actionable problems.
Agent summaries now show how conversations went, and every metric opens the records behind it
Monte Carlo's agent summary tab now includes conversation metrics such as duration, error rates, and token usage. Users can drill down from these metrics to view the underlying traces and conversations for improved observability.
REST API v2 (Public preview): onboard Monte Carlo from your AI tool, Terraform or a script
Monte Carlo's REST API v2 is now in public preview, enabling automated onboarding and deployment of collection agents via scripts, Terraform, or AI tools.
Scope domains to individual custom agents
Monte Carlo now supports scoping domains to specific custom code agents. This allows administrators to restrict user access to traces, conversations, and issues associated with particular agents.
Filter dbt Cloud events by environment
Monte Carlo now allows filtering dbt Cloud events by environment. This enables production accounts to ingest only production runs, ensuring that alerts and lineage accurately reflect the production state.