Tuning agent

The Tuning Agent analyzes Monte Carlo monitors for alert noise and recommends configuration changes to reduce false positives without sacrificing real signal. It operates in a two-phase workflow: first it analyzes a monitor and produces recommendations, then — with user approval — it applies the selected changes. The agent is deliberately conservative — an empty recommendation list ("this monitor looks fine") is a first-class outcome.

How it works

Phase 1: Analyze

When triggered, the agent fetches the monitor's configuration, recent incidents, anomaly patterns, and any troubleshooting analysis history. The agent evaluates:

• Alert volume and frequency patterns
• Anomaly patterns and severity
• Incident triage statuses (how many alerts were marked NO_ACTION_NEEDED vs. FIXED vs. EXPECTED)
• Troubleshooting Agent root cause findings

Based on this analysis, the agent produces structured recommendations — each with a title, reasoning, and a summary of the proposed change. Recommendations are capped at 3 per metric/custom SQL monitor and 10 per table monitor.

The agent also checks whether applying a recommendation would reset the monitor's learned ML thresholds, and flags each recommendation accordingly so you can make an informed decision.

Phase 2: Apply

After reviewing the recommendations, you select which ones to apply. The agent then executes the configuration changes via the appropriate monitor API. For monitors managed through Monitors as Code, the agent can analyze but not apply changes — you'll need to update your MaC configuration instead.

Supported monitor types

Table monitor tuning details

For table monitors, the agent can tune each metric independently:

• Freshness (last_updated_on): Adjust sensitivity (low/medium/high) or set an explicit freshness_threshold_minutes
• Volume change (total_row_count): Switch between AUTO sensitivity and explicit OUTSIDE_RANGE thresholds with lookback
• Unchanged size (total_row_count_last_changed_on): Switch between AUTO sensitivity and explicit greater-than thresholds

Additional capabilities

• Collection remediation — The agent also checks for collection-level issues such as job execution failures, and can recommend timeout bumps or recency filter changes when collection problems are the root cause of noisy alerts.
• Split recommendations — When a single monitor covers too broad a scope, the agent can recommend splitting it into two more focused monitors.

Accessing the Tuning Agent

In the Monte Carlo UI

The Tuning Agent is accessible from multiple surfaces in the Monte Carlo UI:

• From the monitor detail page, click Tune monitor to start an analysis.
• When you mark an alert as No action needed or Expected, Monte Carlo prompts you to tune the monitor that fired it.
• From the alert detail page, where the action is available alongside other alert actions.

Once analysis completes, review the recommendations and select which ones to apply. After tuning, the UI shows whether any alerts have fired since the last tuning was applied, so you can track the effectiveness of the changes.

Via the Agent Toolkit (MCP)

The tune-monitor skill in the Agent Toolkit lets you invoke the Tuning Agent from AI coding agents like Claude Code and Cursor:

• "Tune this monitor to reduce noise"
• "Why is this monitor so noisy?"
• "Recommend sensitivity changes for monitor <uuid>"

Security & data privacy

For more detail on security and data privacy, see the AI Features and Technical Information documentation.