Passed Exam Today Agentforce-Specialist

In Salesforce Agentforce, a prompt template designed to populate a custom field (like "Latest Opportunities Summary" on the Account object) requires dynamic data to be fed into the template for AI to generate meaningful output. Here, the task is to gather data from the three most recently opened opportunities related to an account. The most robust and flexible way to achieve this is by using a Flow (Option B). Salesforce Flows allow the Agentforce Specialist to define logic to query the Opportunity object, filter for the three most recent opportunities (e.g., using a Get Records element with a sort by CreatedDate descending and a limit of 3), and pass this data as variables into the prompt template. This approach ensures precise control over the data retrieval process and can handle complex filtering or sorting requirements.

Option A: Selecting the "latest Opportunities related list as a merge field" is not a valid option in Agentforce prompt templates. Merge fields can pull basic field data (e.g., {!Account.Name}), but they don’t natively support querying or aggregating related list data like the three most recent opportunities.

Option C: There is no "Account Opportunity object" in Salesforce; this seems to be a misnomer (perhaps implying the Opportunity object or a junction object). Even if interpreted as selecting the Opportunity object as a resource, prompt templates don’t directly query related objects without additional logic (e.g., a Flow), making this incorrect.

Option B: Flows integrate seamlessly with prompt templates via dynamic inputs, allowing the Specialist to retrieve and structure the exact data needed (e.g., Opportunity Name, Amount, Close Date) for the AI to summarize.

Thus, Option B is the correct method to gather the necessary data efficiently and accurately.

Why is "Messaging Session" the correct answer?

In Agentforce, the Messaging Session object stores the conversation transcript between the customer and the agent.

Key Features of the Messaging Session Object:

Stores the Entire Customer-Agent Conversation

The Messaging Session object maintains a record of the full chat history, including timestamps, messages, and interactions.

This ensures that past interactions can be referenced during follow-ups.

Supports AI-Powered Work Summaries

Einstein AI uses Messaging Sessions to generate summaries of chat interactions for agents.

These summaries are stored and accessible for later reference.

Links with Service Cloud for Case Resolution

If a conversation escalates into a case, the Messaging Session object can be linked to it.

This allows support teams to review the conversation history without switching contexts.

Why Not the Other Options?

❌ A. Messaging End User

Incorrect because this object stores details about the customer (e.g., name, contact details) but not the conversation transcript.

❌ C. Case

Incorrect because Cases store structured service requests but do not contain raw conversation transcripts.

Instead, cases may reference the Messaging Session object.

Agentforce Specialist References

Salesforce AI Specialist Material confirms that Messaging Sessions store chat conversations and support Einstein Work Summaries​.

According to the AgentForce Data Configuration and Retrieval Guide, when an organization like Universal Containers wants to enable its AI agent to answer questions using historical case data, the correct implementation is to create an Unstructured Data Model Object (UDMO) based on the Case object, then index that data for retrieval.

The documentation clearly explains:

“When using previous case records to power AI-driven Q&A or similarity-based retrieval, create a UDMO mapped to the Case object. UDMOs allow the system to process and semantically index unstructured text fields such as Case Description, Resolution, and Comments, enabling the LLM to surface contextually similar resolved cases.”

This allows the AgentForce retrieval engine to perform semantic searches across historical support data, returning cases that are most contextually relevant to the user’s query.

Option A is incorrect because past cases cannot be used automatically without indexing them.

Option B is incorrect because a DMO is for structured data (tables, numeric fields) and doesn’t support semantic text retrieval.

Therefore, Option C is correct and aligns fully with Salesforce’s documented best practices.

References (AgentForce Documents / Study Guide):

AgentForce Data Configuration Guide: “Using UDMOs for Case-Based Reasoning”

AgentForce Implementation Handbook: “Indexing Historical Case Records for Semantic Search”

AgentForce Study Guide: “Creating Unstructured Data Model Objects from Case Objects”

Per the AgentForce Data Cloud Integration Guide, the AI Agent Session (AIAGENTSESSION) object represents an overarching container that tracks a continuous interaction between one or more AI agents and a user. The document describes: “AI Agent Session is the parent container for a contiguous set of AI Agent interactions. It captures metadata, start and end times, and the relationship to individual messages exchanged during the session.”

Option A, AIAGENTINTERACTION, represents a single step or event within the session, while AIAGENTINTERACTIONMESSAGE (Option B) represents individual messages or exchanges within that interaction. Therefore, AIAGENTSESSION is the correct DLO for reporting on broader agent behavior and performance across an entire engagement.

References (AgentForce Documents / Study Guide):

AgentForce Data Cloud Reporting Guide: “Understanding Agent DLO Hierarchy”

AgentForce Study Notes: “AI Agent Session, Interaction, and Message Object Relationships”

Salesforce Data Cloud for AI Agents: “AI Agent Session Overview”