An Al Specialist is tasked with creating a prompt template for a sales team. The template needs to generate a summary of all related opportunities for a given Account.
Which grounding technique should the Al Specialist use to include data from the related list of opportunities in the prompt template?
Correct Answer:B
In Salesforce, when creating a prompt template for the sales team, you can include data from related objects such as Opportunities that are linked to an Account. The best method to ground the AI model and provide relevant information from related records, like Opportunities, is by using merge fields.
Merge fields in Salesforce allow you to dynamically reference data from a record or related records, like Opportunities for a given Account. In this scenario, the Agentforce Specialist needs to pull data from the default related list of Opportunities associated with the Account. This is achieved by using merge fields, which pull in data from the standard relationship Salesforce creates between Accounts and Opportunities.
Option A (referencing a custom related list) and Option C (using formula fields with Einstein-related lists) do not align with the standard, practical grounding method for this
task. Custom lists would require additional configurations not typically necessary for a basic use case, and formula fields are typically not used to directly fetch related list data for prompt generation in templates. The standard and straightforward method is using merge fields tied to the default related list of opportunities.
Salesforce References:
✑ Merge Fields in Templates: https://help.salesforce.com/s/articleView?id=000387601&type=1
✑ Grounding Data in Prompts: https://developer.salesforce.com/docs/atlas.en- us.salesforce_ai.meta/salesforce_ai/grounding_data_prompts
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).