An Agentforce created a custom Agent action, but it is not being picked up by the planner service in the correct order.
Which adjustment should the Al Specialist make in the custom Agent action instructions for the planner service to work as expected?
Correct Answer:
A
When a custom Agent action is not being prioritized correctly by the planner service, the root cause is often missing or improperly defined action dependencies. The planner service determines the execution order of actions based on dependencies defined in the action instructions. To resolve this, the Agentforce Specialist must explicitly specify dependent actions using their API names in the custom action??s configuration. This ensures the planner understands the sequence in which actions must be executed to meet business logic requirements.
Salesforce documentation highlights that dependencies are critical for orchestrating workflows in Einstein Bots and Agentforce. For example, if Action B requires data from Action A, Action A??s API name must be listed as a dependency in Action B??s instructions. The Einstein Bot Developer Guide states that failing to define dependencies can lead to race conditions or incorrect execution order.
In contrast:
✑ Profiles or custom permissions (B) control access to the action but do not influence execution order.
✑ LLM model provider and version (C) determine the AI model used for processing but are unrelated to the planner??s sequencing logic.
Reference:
Salesforce Help Article: Configure Custom Actions for Einstein Bots (Section: "Defining Action Dependencies").
Einstein Bot Developer Guide: "Orchestrating Workflows with the Planner Service" (Dependency Management best practices).
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?
Correct Answer:
C
Agent is designed to enhance user interaction within Salesforce by leveraging Large Language Models (LLMs) to process and respond to user inquiries. When a user submits a request, Agent analyzes the input using natural language processing techniques. It then utilizes LLM technology to generate an appropriate and contextually relevant response, which is displayed directly to the user within the Salesforce interface. Option C accurately describes this process. Agent does not necessarily trigger a flow (Option A) or perform an HTTP callout to an LLM provider (Option B) for each user request. Instead, it integrates LLM capabilities to provide immediate and intelligent responses,
facilitating a broad range of user requests.
References:
✑ Salesforce Agentforce Specialist Documentation - Agent Overview: Details how Agent employs LLMs to interpret user inputs and generate responses within the Salesforce ecosystem.
✑ Salesforce Help - How Agent Works: Explains the underlying mechanisms of how Agent processes user requests using AI technologies.
Universal Containers?? data science team is hosting a generative large language model (LLM) on Amazon Web Services (AWS).
What should the team use to access externally-hosted models in the Salesforce Platform?
Correct Answer:
A
To access externally-hosted models, such as a large language model (LLM) hosted on AWS, the Model Builder in Salesforce is the appropriate tool. Model Builder allows teams to integrate and deploy external AI models into the Salesforce platform, making it possible to leverage models hosted outside of Salesforce infrastructure while still benefiting from the platform's native AI capabilities.
✑ Option B, App Builder, is primarily used to build and configure applications in
Salesforce, not to integrate AI models.
✑ Option C, Copilot Builder, focuses on building assistant-like tools rather than integrating external AI models.
Model Builder enables seamless integration with external systems and models, allowing Salesforce users to use external LLMs for generating AI-driven insights and automation. Salesforce Agentforce Specialist References:For more details, check the Model Builder guide here: https://help.salesforce.com/s/articleView?id=sf.model_builder_external_models.htm
Universal Containers wants to incorporate the current order fulfillment status into a prompt for a large language model (LLM). The order status is stored in the external enterprise resource planning (ERP) system.
Which data grounding technique should the Agentforce Specialist recommend?
Correct Answer:
A
✑ Context of the Requirement:Universal Containers wants to pull in real-time order status data from an external ERP system into an LLM prompt.
✑ Data Grounding in LLM Prompts:Data grounding ensures the Large Language
Model has access to the most current and relevant information. In Salesforce, one recommended approach is to use External Objects (via Salesforce Connect) when data resides outside of Salesforce.
✑ Why External Object Record Merge Fields:
✑ Why Not External Services Merge Fields or Apex Merge Fields:
✑ References and Study Resources:
In a Knowledge-based data library configuration, what is the primary difference between the identifying fields and the content fields?
Correct Answer:
A
Comprehensive and Detailed In-Depth Explanation:In Agentforce, a Knowledge-based data library (e.g., via Salesforce Knowledge or Data Cloud grounding) uses identifying fields and content fields to support AI responses. Let??s analyze their roles.
✑ Option A: Identifying fields help locate the correct Knowledge article, while content fields enrich AI responses with detailed information.In a Knowledge-based data library, identifying fields (e.g., Title, Article Number, or custom metadata) are used to search and pinpoint the relevant Knowledge article based on user input or context. Content fields (e.g., Article Body, Details) provide the substantive data that the AI uses to generate detailed, enriched responses. This distinction is critical for grounding Agentforce prompts and aligns with Salesforce??s documentation on Knowledge integration, making it the correct answer.
✑ Option B: Identifying fields categorize articles for indexing purposes, while content fields provide a brief summary for display.Identifying fields do more than categorize—they actively locate articles, not just index them. Content fields aren??t limited to summaries; they include full article content for response generation, not just display. This option underrepresents their roles and is incorrect.
✑ Option C: Identifying fields highlight key terms for relevance scoring, while content fields store the full text of the article for retrieval.While identifying fields contribute to relevance (e.g., via search terms), their primary role is locating articles, not just scoring. Content fields do store full text, but their purpose is to enrich responses, not merely enable retrieval. This option shifts focus inaccurately, making it incorrect.
Why Option A is Correct:The primary difference—identifying fields for locating articles and content fields for enriching responses—reflects their roles in Knowledge-based grounding, as per official Agentforce documentation.
References:
✑ Salesforce Agentforce Documentation: Grounding with Knowledge > Data Library Setup – Defines identifying vs. content fields.
✑ Trailhead: Ground Your Agentforce Prompts – Explains field roles in Knowledge integration.
✑ Salesforce Help: Knowledge in Agentforce – Confirms locating and enriching functions.