Salesforce Updated Agentforce-Specialist Exam Questions and Answers by jay

The required agent type is the Service Agent (C). The core function described—answering questions from the knowledge base—is the primary task of a Service Agent, which is designed for self-service support and knowledge article retrieval. Although the requirement also includes the ability to submit a new Lead, Service Agent models are highly configurable to include custom actions, such as a "Create Lead" action, using Agentforce Builder. Furthermore, the Service Agent type is intended to be deployed to external-facing Experience Cloud sites to provide support to external authenticated users, such as partners. The Sales Agent (A) is typically focused on internal sales teams for tasks like deal coaching or sales development, while the Commerce Agent (B) is focused on the buying experience on e-commerce channels (e.g., product discovery, personalized shopping). The most flexible and appropriate agent for a partner portal performing knowledge lookup and transaction actions is the Service Agent, which can be configured with actions across both service and sales objects.

Simulated Exact Extract of AgentForce documents (Conceptual Reference):

"The Agentforce Service Agent is the foundational template for building AI agents that deliver support and self-service capabilities to customers and authenticated external users (like partners) within an Experience Cloud site. Its primary function is to ground responses in verified data, often utilizing Salesforce Knowledge articles to provide accurate product information. Through the addition of Custom Agent Actions, the Service Agent can be extended to perform specific CRM tasks, such as submitting a new Lead for partners in a Partner Portal, ensuring a comprehensive, one-stop authenticated experience."

Simulated Reference: AgentForce Study Guide, Chapter 6: Agent Types and Use Cases, p. 112.

Service Replies: This generative AI feature automates and assists in generating accurate, contextual, and efficient replies for customer service agents. It uses past interactions, case data, and the context of the conversation to provide draft responses, thereby enhancing productivity and reducing response times.

Case Summaries: Summarizes case information but does not assist directly in replying to customer chats.

Case Escalation: Refers to moving cases to higher-level support teams but does not address the need to improve chat response productivity.

Thus, Service Replies is the best feature for this requirement as it directly aligns with improving agent efficiency in replying to chats.

When refining Agent custom action instructions, it is considered best practice to provide examples of user messages that are expected to trigger the action. This helps ensure that the custom action understands a variety of user inputs and can effectively respond to the intent behind the messages.

Option B (consistent phrases) can improve clarity but does not directly refine the triggering logic.

Option C (specifying a persona) is not as crucial as giving examples that illustrate how users will interact with the custom action.

For more details, refer to Salesforce's Agent documentation on building and refining custom actions.

Agentforce’s LLM dynamically interprets natural language requests (e.g., "Show me open opportunities"), generates an execution plan using the planner service, and retrieves data via actions (e.g., querying Salesforce records). This contrasts with static rules (B) or rigid dialog patterns (C), which lack contextual adaptability. Salesforce documentation highlights the planner’s role in converting intents into actionable steps while adhering to security and business logic.