A Salesforce Administrator is exploring the capabilities of Agent to enhance user interaction within their organization. They are particularly interested in how Agent processes user requests and the mechanism it employs to deliver responses. The administrator is evaluating whether Agent directly interfaces with a large language model (LLM) to fetch and display responses to user inquiries, facilitating a broad range of requests from users.
How does Agent handle user requests In Salesforce?

• A. Agent analyzes the user ' s request and LLM technology is used to generate and display the appropriate response.
• B. Agent will perform an HTTP callout to an LLM provider.
• C. Agent will trigger a flow that utilizes a prompt template to generate the message.

Universal Containers tests out a new Einstein Generative AI feature for its sales team to create personalized and contextualized emails for its customers. Sometimes, users find that the draft email contains placeholders for attributes that could have been derived from the recipient's contact record. What is the most likely explanation for why the draft email shows these placeholders?

• A. The user does not have permission to access the fields.
• B. The user does not have Einstein Sales Emails permission assigned.
• C. The user's locale language is not supported by Prompt Builder.

An Agentforce Specialist at Universal Containers (UC) is building with no-code tools only. They have many small accounts that are only touched periodically by a specialized sales team, and UC wants to maximize the sales operations team ' s time, UC wants to help prep the sales team for calls by:
* Summarizing past purchases
* Displaying products the contact has shown interest in (with data captured via Data Cloud)
* Providing a recap of past email and phone conversations that have transcripts Which approach should the Agentforce Specialist recommend to achieve this goal?

• A. Fine-tune the standard foundational model due to the complexity of the data.
• B. Use a prompt template grounded on CRM and Data Cloud data using standard foundation models.
• C. Deploy UC ' s own custom foundational model on this data first.

Universal Containers is building an Agentforce Service Agent to handle order cancellations. The Agentforce Specialist needs to ensure that a critical "Check Cancellation Eligibility" action is executed deterministically on every relevant turn, without relying on the reasoning engine's discretion to choose the tool. During a code review, a junior developer asks why the Agentforce Specialist called the action using the run @actions.name command instead of simply listing the action under the reasoning.actions block.
What must the Agentforce Specialist explain regarding the difference between these two invocation methods?

• A. Listing an action under reasoning.actions makes it a subjective tool that the large language model (LLM) decides whether to call; calling it with the run command forces guaranteed execution every time.
• B. The run command is only valid when nested inside reasoning.actions blocks to pass parameters; the Agentforce Specialist must list it under reasoning.actions for the large language model (LLM) to access it.
• C. Both patterns execute the action on every turn automatically; the difference is purely syntactic, as the run command is simply the newer notation for Agent Script.

Universal Containers' Agentforce Service Agent has been live for four weeks. Agent Optimization in Agentforce Observability shows the main support intent cluster scoring low quality, with score reasons citing ambiguous subagent match. The Session Trace confirms the reasoning engine is consistently selecting the wrong subagent on most turns.
What is the most viable solution to resolve the issue?

• A. Refine the classification descriptions and scope of the competing subagents to eliminate the semantic overlap that is causing the reasoning engine to misroute.
• B. Use the intent cluster data from Agent Optimization to identify the most frequently misrouted intents and add new subagents with tightly scoped classification descriptions for each.
• C. Extend the agent's data library with additional knowledge articles covering the misrouted intent scenarios identified in Agent Optimization.