Review scores across your AI portfolio
Assess the quality and safety of your AI systems by reviewing scores, identifying low-scoring systems, and learning which metrics are affecting each score.
Before you begin
Role required: sn_ai_asset_mgmt.ai_asset_owner or sn_ai_governance.ai_steward
Procedure
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Navigate to All > AI Control Tower > Home > Insights > Monitor.
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Set the date range for the data you want to review.
Select the date range picker and choose a time period. The default is Last 30 days. Select Custom range to specify an exact start and end date, up to 18 months apart.
All score cards, trends, and session data on the page update to reflect the selected range.
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Check for critical issues that need immediate attention by reviewing Your top recommendations.
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Review each insight card for its severity level and outcome category.
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Select View details to open the full analysis in a side panel, including affected metrics, impact, description, and root cause.
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Understand which metrics are affecting a score by selecting the score card to open the scoring breakdown.
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Select the ServiceNow AI systems or External AI systems tab to view the breakdown for a specific AI system type.
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Review each metric's name, weight, current score, and change over time.
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Expand the scoring formula section to see a stacked bar visualization of how each metric contributes to the composite score.
For example, if the Quality score is 68%, the side panel might show that Task completion (weight 25%) scored 54% while Answer completeness (weight 40%) scored 91%. The low Task completion score is pulling down the composite.
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Identify which AI systems need attention by reviewing the AI systems ranked by score widget.
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Select Quality or Safety from the list to choose which score to rank by.
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Select Lowest or Highest to change the sort order.
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Select a system name to view its evaluation details.
For example, sorting by Lowest quality might reveal that an incident resolution agent is scoring 52% while all other systems are over 80%. This tells you where to focus your investigation.
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Assess overall quality performance by reviewing the Average overall Quality score card.
The score card displays a composite weighted average as a percentage. For example, a Quality score of 88% labeled Good (green) indicates that your AI systems are meeting quality targets overall.
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Assess overall safety performance by reviewing the Average overall Safety score card.
The Safety score card displays a composite weighted average as a percentage. For example, a Safety score of 70% labeled Fair (orange) indicates that one or more safety metrics need attention.
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Check performance over time by reviewing the Monitor agent activity trend chart.
Option Description Lowest performing metrics View the top five metrics with the largest decline in quality and safety over time. Highest performing metrics View the top five metrics with the largest increase in quality and safety over time. Quality metrics View the performance of quality metrics over time. Safety metrics View the performance of safety metrics over time. All metrics View the performance of all metrics over time. Select a specific metric View the performance for a specific metric by selecting it from the list. Solid lines represent metrics that contribute to this AI system's overall quality or safety score. Dotted lines represent metrics that are collected but don't contribute to those scores. You can point to a data point on the chart to see the exact score for that date.
For example, a gradual decline in Task completion from 90% to 72% over three weeks indicates a quality regression for this AI system that warrants session-level investigation.
To add a dotted-line metric to your scoring formula, see Configure an evaluation metric template.
Parent Topic:Reviewing quality and safety scores
---
title: Review scores across your AI portfolio
description: Assess the quality and safety of your AI systems by reviewing scores, identifying low-scoring systems, and learning which metrics are affecting each score.
locale: en-US
canonical_url: https://www.servicenow.com/docs/r/intelligent-experiences/mon-ai-review-scores-task.html
release: brazil
topic_type: task
last_updated: "2026-09-10"
reading_time_minutes: 3
keywords: [ServiceNow Otto, AI Agents, generative AI, agentic AI]
breadcrumb: [Reviewing scores, Monitoring and evaluating AI systems, Monitor AI assets, AI Control Tower, Establishing AI governance, Enable AI Experiences]
---
# Review scores across your AI portfolio
Assess the quality and safety of your AI systems by reviewing scores, identifying low-scoring systems, and learning which metrics are affecting each score.
## Before you begin
Role required: sn\_ai\_asset\_mgmt.ai\_asset\_owner or sn\_ai\_governance.ai\_steward
## Procedure
1. Navigate to **All** > **AI Control Tower** > **Home** > **Insights** > **Monitor**.
2. Set the date range for the data you want to review.
Select the date range picker and choose a time period. The default is **Last 30 days**. Select **Custom range** to specify an exact start and end date, up to 18 months apart.
All score cards, trends, and session data on the page update to reflect the selected range.
3. Check for critical issues that need immediate attention by reviewing **Your top recommendations**.
1. Review each insight card for its severity level and outcome category.
2. Select **View details** to open the full analysis in a side panel, including affected metrics, impact, description, and root cause.
4. Understand which metrics are affecting a score by selecting the score card to open the scoring breakdown.
1. Select the **ServiceNow AI systems** or **External AI systems** tab to view the breakdown for a specific AI system type.
2. Review each metric's name, weight, current score, and change over time.
3. Expand the scoring formula section to see a stacked bar visualization of how each metric contributes to the composite score.
For example, if the Quality score is 68%, the side panel might show that Task completion \(weight 25%\) scored 54% while Answer completeness \(weight 40%\) scored 91%. The low Task completion score is pulling down the composite.
5. Identify which AI systems need attention by reviewing the **AI systems ranked by score** widget.
1. Select **Quality** or **Safety** from the list to choose which score to rank by.
2. Select **Lowest** or **Highest** to change the sort order.
3. Select a system name to view its evaluation details.
For example, sorting by Lowest quality might reveal that an incident resolution agent is scoring 52% while all other systems are over 80%. This tells you where to focus your investigation.
6. Assess overall quality performance by reviewing the **Average overall Quality score** card.
The score card displays a composite weighted average as a percentage. For example, a Quality score of 88% labeled Good \(green\) indicates that your AI systems are meeting quality targets overall.
7. Assess overall safety performance by reviewing the **Average overall Safety score** card.
The Safety score card displays a composite weighted average as a percentage. For example, a Safety score of 70% labeled Fair \(orange\) indicates that one or more safety metrics need attention.
8. Check performance over time by reviewing the **Monitor agent activity** trend chart.
|Option|Description|
|------|-----------|
|**__Lowest performing metrics__**|View the top five metrics with the largest decline in quality and safety over time.|
|**Highest performing metrics**|View the top five metrics with the largest increase in quality and safety over time.|
|**Quality metrics**|View the performance of quality metrics over time.|
|**Safety metrics**|View the performance of safety metrics over time.|
|**All metrics**|View the performance of all metrics over time.|
|**Select a specific metric**|View the performance for a specific metric by selecting it from the list.|
Solid lines represent metrics that contribute to this AI system's overall quality or safety score. Dotted lines represent metrics that are collected but don't contribute to those scores. You can point to a data point on the chart to see the exact score for that date.
For example, a gradual decline in Task completion from 90% to 72% over three weeks indicates a quality regression for this AI system that warrants session-level investigation.
To add a dotted-line metric to your scoring formula, see [Configure an evaluation metric template](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/brazil/markdown/intelligent-experiences/mon-ai-configure-metric-templates.md).
**Parent Topic:**[Reviewing quality and safety scores](https://raw.githubusercontent.com/ServiceNow/ServiceNowDocs/brazil/markdown/intelligent-experiences/mon-ai-reviewing-ai-system-scores.md)
| title | Review scores across your AI portfolio |
| description | Assess the quality and safety of your AI systems by reviewing scores, identifying low-scoring systems, and learning which metrics are affecting each score. |
| locale | en-US |
| canonical_url | https://www.servicenow.com/docs/r/intelligent-experiences/mon-ai-review-scores-task.html |
| release | brazil |
| topic_type | task |
| last_updated | 2026-09-10 |
| reading_time_minutes | 3 |
| keywords | ServiceNow OttoAI Agentsgenerative AIagentic AI |
| breadcrumb | Reviewing scoresMonitoring and evaluating AI systemsMonitor AI assetsAI Control TowerEstablishing AI governanceEnable AI Experiences |