---
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


---

# Evaluated sessions

# Evaluated sessions {#ariaid-title1}

Release version: Brazil  
Updated September 10, 2026  
![](https://www.servicenow.com/docs/portal-asset/ico-clock) 2 minutes to read  
View all evaluated AI sessions across your portfolio, compare quality and safety scores by AI system, and identify the sessions that need investigation.

## Key benefits {#mon-ai-evaluated-sessions-overview__key-benefits}

* Compare quality and safety scores across AI systems to identify which systems are declining or performing well.
* Find low-scoring sessions quickly by sorting and filtering the session list.
* View score trends by AI system over weeks or months to detect patterns.
* Open any session directly to investigate its traces and spans.

{#mon-ai-evaluated-sessions-overview__ul_benefits}  
Figure 1. Evaluated sessions

## Required roles {#mon-ai-evaluated-sessions-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 evaluated sessions.

## Accessing evaluated sessions {#mon-ai-evaluated-sessions-overview__accessing}

View evaluated sessions by navigating to AllAI Control TowerHomeInsightsMonitor and selecting the Evaluated sessions tab.

## AI systems with evaluated sessions {#mon-ai-evaluated-sessions-overview__ai-systems-chart}

Compare quality and safety scores across your AI systems using the bar chart. Each AI system displays a pair of bars representing its quality and safety scores.

* Group scores by Months, Weeks, or Days to adjust the time granularity.

  For example, after deploying an update to an AI system, group the chart by
  Days to see whether the update improved scores without affecting other systems.
* Select Largest score decline or Top performers from the list to change how AI systems are sorted in the chart.
{#mon-ai-evaluated-sessions-overview__ul_chart_controls}

## Evaluated sessions list {#mon-ai-evaluated-sessions-overview__sessions-table}

Find the sessions that need attention by reviewing the evaluated sessions table. Scores are color coded for quick scanning: red (0 to 50%), orange (51 to 75%), green (76 to 100%).

Name
:   Session identifier. Select the name to open the session detail page and begin investigating traces and spans. 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.").

Traces
:   Number of traces in the session. A session with many traces represents a longer, multi-turn interaction. Low scores on sessions with few traces can indicate a fundamental issue, while low scores on sessions with many traces may
    point to degradation over the course of the conversation.

Spans
:   Number of spans across all traces in the session. A high span count relative to the number of traces can indicate the AI system is taking many steps, such as tool calls, to complete its task, which can also help explain elevated latency or token usage.

AI system
:   The AI system associated with the session. Select the name to view aggregated quality and safety scores across all evaluated sessions for that system.

Quality score
:   Composite quality score for the session, calculated from the weighted metrics in your quality template. Sort by this column to surface the lowest-scoring sessions first.

Safety score
:   Composite safety score for the session, calculated from the weighted metrics in your safety template. A red safety score warrants immediate investigation.

Start time
:   When the session began. Use this to correlate low scores with known events such as deployments, configuration changes, or outages.

End time
:   When the session ended or timed out.

Total runtime tokens
:   Combined input and output tokens consumed by the AI system during this session. This reflects the cost of running the interaction, not the cost of evaluating it. Unusually high token counts can indicate inefficient agent behavior
    such as excessive retries or overly verbose responses.

Latency (ms)
:   Combined language model response time, in milliseconds, across all traces in the session. This measures time spent in language model calls, not total session duration. Unusually high latency can point to a slow-responding model or a high number of tool-calling steps within the session.
{#mon-ai-evaluated-sessions-overview__dl_sessions_columns}

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