---
sourceDocument: Australia Enable AI
sourceDocumentLink: https://www.servicenow.com/docs/r/australia/intelligent-experiences

 Release :

    - australia

ft:locale :

    - en-US

ft:publication_title :

    - Australia Enable AI

ft:clusterId :

    - platai

bundleId :

    - platai

workflow :

    - Platform


---

# Monitoring overview

# Monitoring overview {#ariaid-title1}

Release version: Australia  
Updated September 10, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 7 minutes to read
Summarize  
![AI sparkle icon](https://servicenow.com/docs/portal-asset/ai-sparkle-icon) Summarized using AI  
This content was generated using new OpenAI-powered functionality. Results are provided on an as is basis and are not guaranteed to be accurate or complete.  

## Summary of Monitoring overview

The Monitoring overview in ServiceNow AI Control Tower provides a centralized dashboard to assess the performance, quality, and safety of your AI systems in production.
It consolidates score cards, inventory data, AI-generated insights, and trend analysis on a single page, enabling you to quickly identify low-performing AI systems and prioritize issues that require attention.
The overview is accessible to users with the AI steward or AI asset owner roles, and asset owners see summaries only for their owned AI systems.
Show full answer Show less  

## Key Features

* **AI-generated insights:** Prioritized recommendations with severity levels and outcome categories help you focus on the most critical issues. Insights include detailed analysis, root cause, and direct links to take corrective actions or mark issues as resolved.
* **Inventory widgets:** Visualize the distribution of AI systems by management status (Managed vs. Unmanaged) and asset type (Agentic AI, Generative AI, Classic AI). Only managed and Agentic AI assets are evaluated.
* **Quality and safety score cards:** Display average overall scores for Agentic AI systems with color-coded performance labels (Red: Low, Orange: Fair, Green: Good). The safety score card populates only when external AI systems return evaluated sessions.
* **Score card side panel:** Provides a detailed breakdown of composite scores, including metric weights, current scores, evaluation counts, trends over time, and scoring formulas. A bias indicator alerts when some metrics have fewer evaluations, affecting score reliability.
* **Ranked AI systems:** Lists AI systems sorted by lowest or highest quality or safety scores, allowing you to drill down into specific system evaluation details.
* **Monitor agent activity chart:** Tracks quality and safety metrics trends over time for Agentic AI systems. Differentiates between metrics contributing to overall scores and those collected but not scored. Offers filtering by metric performance and type, helping detect regressions or improvements.

## Practical Use Cases

* **Identifying quality regressions:** Quickly spot declining scores during regular reviews, investigate contributing metrics, and pinpoint system or session-level issues requiring remediation.
* **Responding to AI-generated insights:** Use severity-ranked insights to address significant drops in safety or quality metrics by analyzing root causes and collaborating with stakeholders to apply fixes.
* **Monitoring new deployments:** Track metric improvements over time for newly deployed agents, validate adjustments, and focus on metrics that need further attention to optimize agent performance.

## Access and Roles

Access the Monitoring overview through **All \> AI Control Tower \> Home \> Insights \> Monitor \> Overview**. Viewing requires the AI steward or AI asset owner roles. AI asset owners see data limited to their owned AI systems.  
Identify low-scoring AI systems quickly by reviewing quality and safety scores, AI-generated insights, and performance trends across your AI portfolio.

## Key benefits {#mon-ai-monitoring-overview__key-benefits}

* See the quality and safety health of your entire AI portfolio at a glance without opening individual system records.
* Focus your attention on the AI systems and metrics that need it most using ranked lists and AI-generated insights.
* Detect gradual regressions before they affect users by tracking metric trends over time.
* Understand exactly how a composite score is calculated by expanding the scoring breakdown for any score card.

{#mon-ai-monitoring-overview__ul_benefits}  
The monitoring overview is the starting point for assessing how your AI systems are performing in production. It brings together score cards, inventory data, AI-generated insights, and trend analysis so you can identify issues and take action from a single page.  
Note:  
If you have the AI Asset Owner \[sn_ai_asset_mgmt.ai_asset_owner\] role, the Overview page provides a summary for only the AI systems that you own.  
Figure 1. Monitoring overview

## Required roles {#mon-ai-monitoring-overview__required-roles}

The AI steward \[sn_ai_governance.ai_steward\] or AI asset owner \[sn_ai_asset_mgmt.ai_asset_owner\] role is required to view the monitoring Overview tab.

## Accessing the monitoring overview {#mon-ai-monitoring-overview__accessing}

View the monitoring overview by navigating to AllAI Control TowerHomeInsightsMonitorOverview.

## Your top recommendations {#mon-ai-monitoring-overview__top-action-items}

AI-generated insights surface the highest-priority issues affecting your AI systems. Each recommendation includes a severity level, an outcome category (for example, Drive AI Productivity), and a brief description of the issue.  
Note:  
Insights are generated daily based on sessions from the previous 24 hours. This delay allows the evaluation engine to collect enough sessions to produce meaningful analysis.

Each insight card provides the following actions:

View details
:   Opens a side panel with the full analysis, including affected metrics, impact, description, and root cause.

Open
:   Navigates to the location where you can act on the insight. For ServiceNow AI agents, this option takes you to actionable recommendations in the AI Skill Kit. For external AI systems, this option takes you to the related AI asset page in AI Control Tower.

Complete
:   Marks the insight as resolved and removes it from the list. Use this options after you have addressed the underlying issue.

Delete
:   Removes the insight from the list without marking it resolved. Use this option for insights that don't require action, such as duplicates or expected behavior.

Use the recommendations to prioritize which issues to investigate first. For example, an insight might report that a safety score dropped significantly during the past week and identify the specific session and metric driving the
decline.  
Important:  
AI-generated insights are based on automated analysis and may not reflect all contributing factors. Verify findings by reviewing session detail before taking action.

## AI systems and asset types {#mon-ai-monitoring-overview__ai-systems-inventory}

Two inventory widgets show the distribution of AI systems on your instance.

AI systems
:   Shows the total count of AI systems by management status (Managed and Unmanaged). Only managed assets are evaluated.

Asset types

:   Shows the distribution of AI systems by type (for example, Agentic AI, Generative AI, and Classic AI). Select a type to open the inventory filtered by that type. Only Agentic AI assets are evaluated.

{#mon-ai-monitoring-overview__dl_inventory}

## Average overall Quality and Safety scores {#mon-ai-monitoring-overview__score-cards}

The quality and safety score cards display average overall scores for your Agentic AI systems. Each score card shows a percentage and a performance label.

Scores are color coded for quick interpretation:

* Red (0 to 50%): Low. Investigate immediately.
* Orange (51 to 75%): Fair. Review recent sessions for trends.
* Green (76 to 100%): Good. Meeting quality or safety targets.

{#mon-ai-monitoring-overview__ul_color_coding}

The safety score card populates only when external AI systems are being evaluated and have returned scored sessions. Because ServiceNow AI systems don't have safety metrics available, the safety score card remains empty until at least one external AI system has produced evaluated sessions. If the card appears empty, verify that
evaluation scoring is active for your external AI systems and that trace data is being received.

View a scoring breakdown by selecting the side panel icon.

## Score card side panel {#mon-ai-monitoring-overview__score-card-side-panel}

Selecting a quality or safety score card opens a side panel with a detailed breakdown of how the composite score is calculated. The panel includes tabs for ServiceNow AI systems and External AI systems so you can view the scoring breakdown for each type.

* See how the score has changed over time by reviewing the average score and the percentage change from the prior period with a trend chart.
* Identify which metrics are contributing to the score by reviewing the data sources table, which shows each metric's name, weight, and current score, and evaluation count.
* Expand the scoring formula section to see a visualization of how each metric contributes to the composite, along with explanatory notes about how the score is calculated.

{#mon-ai-monitoring-overview__ul_side_panel}  
Note:  
If one or more metrics have a noticeably lower evaluation count than the others, a bias indicator appears next to the scoring formula section. A metric with fewer evaluations still counts at its full configured weight, so metrics that evaluate more consistently can carry more real influence on the score than their configured weight alone suggests.

Analyze which metrics are pulling a composite score up or down, and by how much. For details on how scores are calculated, see [How evaluation scoring works](https://www.servicenow.com/docs/lPJ171BGrbCTJBIWy_AzRA "Understand how AI Control Tower calculates quality and safety scores so you can interpret results accurately and configure scoring to reflect your priorities.").

## AI systems ranked by score {#mon-ai-monitoring-overview__ai-systems-ranked}

View which AI systems are scoring lowest or highest. Select Quality or Safety from the list, and select Lowest or Highest to change the sort
order. Select a system name to open its evaluation details.

## Monitor agent activity {#mon-ai-monitoring-overview__trend-chart}

Track how quality and safety metrics for your Agentic AI systems are trending over time in the Monitor agent activity chart. The chart plots every metric with data for the selected range so you can detect gradual regressions, confirm
that recent changes improved performance, and see which metrics are shaping your overall scores.

Two line styles distinguish how each metric contributes to scoring:

* Solid lines represent metrics that contribute to your overall quality or safety score. These metrics are configured in a metric template.
* Dotted lines represent metrics that are being collected but don't contribute to your overall quality or safety score. These metrics aren't configured in any metric template.

{#mon-ai-monitoring-overview__ul_line_types}

To add a dotted-line metric to your scoring formula or to adjust how much existing metrics contribute to your overall scores, see [Configure an evaluation metric template](https://www.servicenow.com/docs/~6Wl1cgX0wLnQujKladjaw "Adjust the scoring formula for a metric template to change which metrics contribute to quality or safety scores, or to change how much weight each metric carries.").

Choose which metrics to display by selecting an option from the list.

Lowest performing metrics
:   Show the top five metrics with the lowest average score over the selected time period. This is the default selection.

Top performing metrics
:   Show the top five metrics with the highest average score over the selected time period.

Quality metrics
:   Show only metrics in the Quality category.

Safety metrics
:   Show only metrics in the Safety category.

All metrics
:   Show every quality and safety metric that has data for the selected date range.
{#mon-ai-monitoring-overview__dl_trend_options}

Alternatively, select an individual metric from the list to view performance for that metric over time.

Point to a data point on the chart to see the exact score for that date.

## Use cases {#mon-ai-monitoring-overview__use-cases}

Identifying a quality regression during a weekly review
:   An AI steward opens the monitoring overview on Monday morning. The quality score card shows 68% (orange), down from 82% the previous week. The administrator selects the score card and sees that Task completion dropped from 88% to
    54%. The AI systems ranked by score widget shows a specific incident resolution agent at the bottom of the list. The administrator selects that system, opens the lowest-scoring session, and discovers that a downstream API integration
    started returning timeout errors over the weekend. The administrator files an incident and monitors subsequent sessions to verify the fix.

Responding to an AI-generated insight
:   The top recommendations list surfaces a critical insight: "Safety score dropped the most this week." The administrator selects View details and reads that the Instruction adherence metric dropped 18% for an
    external HR case management agent. The root cause analysis indicates the agent began straying from its approved response guidance in leave-request responses. The AI steward shares the insight with the product owner, who adjusts the
    agent's response guidelines.

Monitoring a newly deployed agent
:   After deploying a new ServiceNow ITSM agent, the product owner opens the monitoring overview and filters to the new system using the AI systems ranked by score widget. Over the first week, the trend chart shows Tool choice accuracy
    starting at 62% and improving to 78% as the team adjusts the agent's tool configuration. The owner uses the scoring breakdown side panel to confirm which metrics are improving and which still need attention.
{#mon-ai-monitoring-overview__dl_use_cases}

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