---
sourceDocument: Xanadu Employee Service Management
sourceDocumentLink: https://servicenow-prod.fluidtopics.net/r/xanadu/employee-service-management

 Release :

    - xanadu

ft:locale :

    - en-US

ft:publication_title :

    - Xanadu Employee Service Management

ft:clusterId :

    - emplsm

bundleId :

    - emplsm

workflow :

    - Employee


---

# Using agent feedback to improve your case criticality predictions

# Using agent feedback to improve your case criticality predictions {#ariaid-title1}

* Release version: Xanadu
* 
* Updated August 1, 2024
* 
* ![](https://www.servicenow.com/docs/portal-asset/ico-clock) 1 minute to read

By using your agent's feedback to determine how critical a case is, you can improve the
machine learning (ML) models of the Issue Auto Resolution (IAR) application to
predict cases more correctly over time.
When an agent creates a case, Issue Auto Resolution predicts how critical the
issue is. However, in the past, these predictions sometimes needed to be improved. To improve
the classification of cases through machine learning (ML) models, a new field, called the
Resolution requires field, was introduced for HR agents to give
feedback. Agents can only see this field on an HR case form when Issue Auto Resolution has predicted the criticality of that case.​

## Using the resolution requires field {#iar-agent-feedback__section_ddw_h5n_fwb}

The Resolution requires field includes the values that are based on the feedback that the HR agents gave on the criticality prediction. The Resolution requires field includes the options that are listed in the following table.{#iar-agent-feedback__table_nbd_xzm_fwb__entry__2}

| Name | Description |
|-|-|
| Urgent agent action | Immediate agent attention is required to resolve the case. |
| Self-service content only | User could resolve the case with self-service content. |
| Nonurgent agent action | Case is noncritical. |
[Table 1. Resolution requires field values]

{#iar-agent-feedback__table_nbd_xzm_fwb}

Agents can choose the correct feedback from the field options to tell when the Issue Auto Resolution predictions are correct or not.

## Resolution priority mapping {#iar-agent-feedback__section_orl_hjg_fwb}

Agents see an informational message on the screen when they change the resolution requires,
priority, or state of the case​. For example, let's say that an agent selects the
Urgent agent action option. The priority of it being selected is
Low, so the agent sees the message "Resolution requires field is
inconsistent with case priority. Please review." By using resolution priority mapping, agents
can correct the issue when the priority is inconsistent with the urgency of the case.

Informational messages can also appear as priority changes. The resolution requires that an
agent makes changes or state changes.