Review scores for an AI system

  • Release version: Zurich
  • Updated June 30, 2026
  • 3 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 All > AI Control Tower > Home > Inventory. Select the AI system asset and then select the Monitor tab.
      • Navigate to All > AI Control Tower > Home > Insights > Monitor and select a system name in the AI systems ranked by score widget.
      • Navigate to All > AI Control Tower > Home > Insights > Monitor > Evaluated sessions and select the AI system name in the AI system column.
    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.
      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. 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.
    6. Check the evaluation configuration by reviewing the Asset monitoring details card.
      The card shows the sample rate and data retention period for this AI system. If the sample rate is less than 100%, scores are based on a subset of sessions.
    7. Check for regressions over time by reviewing the Monitor agent activity trend chart.
      1. Choose which metrics to display by selecting All metric categories, Quality metrics, or Safety metrics from the list.
      2. Point to a data point on the chart to see the exact score for that date.

      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.

      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.

    8. 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.
      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.