In conversations where multiple agents participate, evaluating individual contributions accurately can be challenging. To address this, Sprinklr has introduced enhancements to its AI Scoring mechanism, allowing quality managers to evaluate each agent separately with greater flexibility and precision, ensuring an accurate assessment of individual contributions in multi-agent interactions.

​A new Scoring Type field has been added at the checklist level within the overview section, enabling users to define how scoring should be applied. This feature offers two options:

1. Single Agent Scoring, where scores are attributed to a specific agent (For example, the first engaged agent, most engaged agent, last engaged agent, or based on User Level Case custom criteria).

2. Multi-Agent Scoring, which allows for the evaluation of each agent individually. For example, an organization can now assess whether all agents in a conversation demonstrated courtesy or limit the evaluation of greetings to only the first agent. These updates ensure that performance evaluations are more tailored and relevant, providing actionable insights into team dynamics and individual contributions in multi-agent interactions.

Adding Agent-Level Scoring in Cases with Multiple Agents

1. Click the New Tab icon. Under Platform Modules, click All Settings within Listen.

2. Search and select Audit Checklists.

3. In the top right corner of the Audit Checklists window, click + Checklist and then select Automated.

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4. You can also Edit an existing checklist.

5. The checklist details screen will open. You can enter the Name and Description.

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6. The Scoring Type (a mandatory field) field can have two possible values:

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   Note: For other fields, refer to Automated Checklist Configuration - Field Description .

   1. Single Agent Scoring

      1. If you choose this field, it means scoring is applied to only one agent within a multi-agent conversation.

      2. Additional Field: Score on Agent

         • If you select Single Agent Scoring, a new field, Score on Agent, becomes visible at the checklist level. It is a mandatory field.

         • This field allows you to specify which agent should be scored, with the following possible options:

           • First Engaged: Assign the score to the first agent who engaged within the interaction.

           • Last Engaged: Assign the score to the last agent who engaged within the interaction.

           • Most Engaged: Assign the score to the agent who has the most number of messages within the interaction.

           • User Custom Field: Assign the score to the custom field user.

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             For Example, if you select the Most Engaged Agent option under Score on Agent, the scoring will automatically be attributed to the agent who had the highest overall engagement in the conversation. No changes are required at the item level; all items in the checklist will be evaluated based on the entire conversation, and the score will be assigned to the most engaged agent. This ensures that the agent who played the most significant role in the interaction, regardless of when they participated, is appropriately scored for their contributions.

   2. Multiple Agents Scoring

      If you select Multi-Agent Scoring, the scoring process becomes more detailed and agent-specific, with scoring being introduced at the item level. Based on your selection, the item score will be assigned differently. Here are the available options for scoring:

      

      1. Evaluate and Assign Same Score to Each Participating Agent: In this option, the item score will not be evaluated individually for each agent. Instead, the same score will be assigned to all agents who participated in the interaction.

      2. Evaluate Single Agent Only:

      • When this option is selected, a mandatory field "Score on Agent" will appear at the checklist item level. The item score will be evaluated specifically for the selected agent only. The available choices for this field are:

        • First Engaged: Assign the score to the first agent who interacted within the interaction.

        • Last Engaged: Assign the score to the last agent who interacted within the interaction.

        • Most Engaged: Assign the score to the agent with the most interaction during the conversation.

        • User Custom Field: Assign the score based on a custom field defined by the user.

      3. Evaluate and Score Each Agent Differently:

         • In this option, the item score will be evaluated separately for each participating agent, allowing individual scoring based on each agent’s contributions during the conversation.