Universal Containers (UC) wants its Agentforce Service Agent to provide accurate and trustworthy responses to...

Salesforce Agentforce Service Agent allows organizations to “ground” generative AI responses in their own trusted, proprietary data sources using the Agentforce Data Library. When connected with Salesforce Knowledge, the Agentforce Data Library enables the AI to retrieve contextually relevant information directly from internal Knowledge articles—both structured fields (such as titles, summaries, categories) and unstructured text (like article bodies or attachments).

This grounding ensures that responses are accurate, explainable, and trustworthy, as the AI is limited to referencing verified enterprise data rather than relying on its global model training data.

Option A is too limited and static—mapping fields does not provide dynamic understanding or grounding.

Option C is incorrect because Salesforce’s large language model (LLM) does not use global datasets to restructure customer data; instead, it uses grounding techniques via the Data Library for contextual accuracy and data governance.

Referenced Salesforce Materials:

Salesforce Spring ’24 Release Notes – Agentforce Data Cloud Integration and Data Library Enhancements (introduces data grounding for accuracy).

Service Cloud Consultant Exam Guide – Interaction Channels Domain (covers AI and generative tools in Service Cloud).

Salesforce Help: “Ground Agentforce Responses with Data Library and Knowledge” (details AI grounding for trustworthy output).