Network Access Controls (NAC) now extend to the Monte Carlo UI. You can restrict application access based on source IP allowlisting using a new UI scope, ensuring that only users within your configured IP ranges can load the application after login.

The UI scope follows the same configuration pattern as existing API and cloud endpoint scopes, so you can add UI restrictions with a single API call alongside your existing NAC setup.

Learn more here: https://docs.getmontecarlo.com/docs/network-access-control

The Operations Agent now includes a ** Coverage Agent** that proactively identifies monitoring gaps across your warehouse. Ask it to help you get started, and it analyzes your warehouse metadata, query logs, and usage patterns to discover business use cases, rank them by criticality, and surface the golden tables that feed your dashboards, ML models, and downstream operations.

For each use case, the Coverage Agent highlights your most critical unmonitored tables with full context including upstream source count, read frequency, downstream consumers, and blast radius. A gap ranking shows unmonitored golden tables per use case with gap percentages, so you know exactly where to focus first.

Once you pick a scope and notification audience, the agent generates a complete monitor YAML configuration. Preview it, choose draft or active, and deploy with one click.

Available in Preview.

SLO policies let you define Time to Acknowledge (TTA) and Time to Resolve (TTR) thresholds for alerts, scoped by priority and domain. Set the response time standards that matter to your organization and track compliance directly in Monte Carlo.

The Alerts table now includes an SLO Status column you can sort and filter to surface overdue alerts at a glance. Alert detail pages display a countdown or breach timer showing exactly how much time remains or how far past your SLO an alert has gone. Optionally, configure re-notifications to alert the original audience when a threshold is breached.

Available to all accounts as an opt-in feature in public preview.

Learn more here: https://docs.getmontecarlo.com/docs/service-level-objectives-slos

The monitor creation modal now includes an “Ask AI” feature, allowing you to describe your monitoring needs in plain language. The Operations Agent then provides guided support to help configure your monitor setup.

To get started quickly, you can use example prompts - or enter more complex requests - to receive guidance and successfully create the monitors you need.

Monte Carlo now supports custom roles, giving teams the flexibility to tailor authorization to match their organization's structure, requirements, and separation-of-duty needs.

Create custom roles via the UI (with an interactive policy builder and resolved permissions preview) or via API/YAML definitions for "IAM as code" workflows. Authorization groups now support multiple roles, so you can pair a built-in role (like Editor) with a focused custom role that overrides specific permissions. Built-in roles continue to receive new permissions automatically; your custom overrides stay in place.

Permission resolution follows a clear, predictable model: secure by default, most-specific policy wins, and deny beats allow at the same specificity level. Ready-to-use recipes are included for common patterns like restricting billing access, managing sampling data access (grant, deny, or scope to specific domains), and more.

Also included: detailed user and group management pages, a change log for auditing modifications, and comprehensive new enablement docs. Learn more: https://docs.getmontecarlo.com/docs/authorization

Microsoft Teams users can now trigger Monte Carlo's Troubleshooting Agent directly from Teams alerts. When an alert fires, kick off a troubleshooting analysis right where your team is already working. Once complete, a summary of findings is delivered in-thread, along with a link to dive deeper in Monte Carlo.

The Troubleshooting Agent is now a native part of your incident response workflow in Teams, helping you investigate and resolve data issues without context switching.

You can now bring Snowflake Intelligence (Cortex Agents) under Monte Carlo's Agent Observability coverage!

From Settings > Agent Observability, connect a native Cortex Agent by selecting a Snowflake warehouse and choosing from a list of discovered agents -- no custom instrumentation or code changes required.

Once connected, Cortex trace and span data flows in automatically, powering the full Agent Observability experience: trace viewer, conversation viewer, trace dashboards, and all four agent monitor types.

Learn more and get started here: https://docs.getmontecarlo.com/docs/snowflake-intelligence-cortex-agents

Metric monitors now support out-of-the-box ML metrics. Select from prebuilt, industry-standard regression metrics — RMSE, MAE, MAPE, R-Squared, and Mean Error — to measure how far off a model's predictions are, plus classification accuracy to track how often a model gets the right answer.

Just pick the prediction and actual columns, and monitoring is configured in clicks.

Coming soon: out-of-the-box drift detection metrics including PSI, KS Test, and JS Divergence for automatic distribution shift detection

Learn more here: https://docs.getmontecarlo.com/docs/available-metrics#ml-metrics

The Asset Summary tab now surfaces all monitor results and trends in a single view.

Metric monitors, custom SQL, validations, and comparisons appear alongside existing pipeline health signals, each with a time-series chart that respects the global time window.

Inline controls let you switch fields, segments, and variables without leaving the page.

Toggle between a results view and a Monitor list view, and a customization option allows you to choose which monitors to show or hide.

Multi-turn agent interactions can now be viewed as a single, chronological conversation in Trace Explorer. Traces sharing a conversation ID are automatically grouped, displaying user messages and agent responses in order with timestamps, role labels, and turn numbers.

Each turn links directly to its underlying trace, and clicking any message opens a detail panel with the full content. This makes it easy to follow the complete back-and-forth of an agent conversation in one place and drill into any individual trace when you need more detail.