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

 Release :

    - brazil

ft:locale :

    - en-US

ft:publication_title :

    - Brazil Enable AI

ft:clusterId :

    - platai

bundleId :

    - platai

workflow :

    - Platform


---

# Review scores for an AI system

# Review scores for an AI system {#ariaid-title1}

Release version: Brazil  
Updated September 10, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 4 minutes to read  
Determine whether a specific AI system is meeting quality and safety targets by reviewing its scores, identifying which metrics are affecting performance, and checking for regressions over time.

## Before you begin

Role required: sn_ai_asset_mgmt.ai_asset_owner or sn_ai_governance.ai_steward

## About this task

When a specific AI system needs attention, determine which metrics are affecting its scores and whether performance is trending in the right direction. Review the Monitor tab on the AI system asset record
after spotting a low-scoring system on the monitoring overview, receiving an AI-generated insight about a specific system, or being assigned a task related to an underperforming asset.

## Procedure

1. Navigate to the Monitor tab for the AI system that you want to review in one of the following ways:  
   * Navigate to AllAI Control TowerHomeInventory. Select the AI system asset and then select the Monitor tab.
   * Navigate to AllAI Control TowerHomeInsightsMonitor and select a system name in the AI systems ranked by score widget.
   * Navigate to AllAI Control TowerHomeInsightsMonitorEvaluated sessions and select the AI system name in the AI system column.
   {#mon-ai-review-ai-system-scores__ul_accessing}
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. Assess overall quality performance by reviewing the Average overall Quality score card.  
   For example, if the quality score is 88%, the data sources table might show that Task completion (weight 40%) scored 88% while Answer completeness (weight 35%) scored 86%. A low score on a heavily weighted metric has a larger impact on the composite.
4. Assess overall safety performance by reviewing the Average overall Safety score card.  
   For example, if the Instruction adherence score is lower than Secrets detection or Sexism detection, the agent may be handling sensitive data and language correctly but still straying from its approved response guidance. Reviewing the traces where Instruction adherence dipped lowest will show which specific exchanges the judge flagged and why.
5. Confirm which metrics are scoring this AI system by reviewing the Metrics evaluated card.  
   This card lists every metric currently scoring this AI system, whether the metric comes from your organization's global metric configuration or was added specifically for this system. To change which metrics are evaluated, see [Configure metrics evaluated for an AI system](https://www.servicenow.com/docs/PYQcHeLFPCx~8irCYZQS4A "Add or remove metrics for a specific AI system to tailor evaluation coverage without changing your organization's global metric configuration.").
6. View the full scoring formula by selecting the side panel icon on a score card.
   1. View a stacked bar visualization of how each metric contributes to the composite score in the How is this score calculated? section.
   2. Learn how the average score is derived and how different scoring formats affect the calculation in the Nuances to consider section.
   {#mon-ai-review-ai-system-scores__substeps_side_panel}
7. Assess the AI system's operational performance by reviewing its performance cards, such as latency and token usage.
   1. Determine whether recent agent activity or another change is affecting response times by reviewing the Avg latency per session card.
   2. Estimate the cost of running this AI system by reviewing the Avg tokens per sessions card.
   3. Understand how much the AI system relies on external tools by reviewing the Avg tool call per trace card.

   {#mon-ai-review-ai-system-scores__substeps_performance}  
   Each card shows its change from the prior period. For the Avg latency per session card, select the expand icon to open a chart of total latency by day, summed across all sessions evaluated that day.
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. |
   |-|-|

   {#mon-ai-review-ai-system-scores__choicetable_lkl_yqs_4kc}  
   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://www.servicenow.com/docs/8cBFk73agy0WSm2qUJUfQA "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.").
9. Identify sessions that need investigation by reviewing the Recent evaluated sessions table.
   1. Sort sessions by Quality score or Safety score to surface the lowest-scoring sessions.
   2. Select a session name to open the session detail page and begin investigating its traces and spans.

   {#mon-ai-review-ai-system-scores__substeps_sessions}  
   For example, a session with a red safety score (below 50%) warrants immediate investigation. For details on investigating sessions, see [Investigate a low-scoring session](https://www.servicenow.com/docs/nuDqtrAOPaflF2Lmzm3ZMQ "Determine the root cause of a low quality or safety score by working through the session detail, trace timeline, and span-level input and output data.").
{#mon-ai-review-ai-system-scores__steps_review}

*[\>]: and then