Salesforce Salesforce Agentforce Specialist Practice Exams
Last updated on Oct 21,2026- Exam Code: Salesforce Agentforce Specialist
- Exam Name: Salesforce Agentforce Specialist
- Certification Provider: Salesforce
- Latest update: Oct 21,2026
Question #1
Based on the user utterance, ‘Show me all the customers in New York’, which standard Agent action will the planner service use?
- A . Query Records
- B . Fetch Records
- C . Select Records
Correct Answer: A
A
Explanation:
Why is Query Records the Correct Answer?
In Agentforce, the Planner Service is responsible for interpreting user requests and selecting the appropriate Copilot Action to fulfill them.
When a user issues a command like:
"Show me all the customers in New York",
the system must retrieve a list of customers filtered by location.
The Query Records action is designed precisely for this purpose.
Key Features of Query Records in Agentforce:
Retrieves Data Based on Specific Field Values
This action fetches Salesforce records that match a set of criteria, such as customers located in New York.
o Uses standard or custom object fields (e.g., BillingState = ‘New York’).
Works with Large Language Models (LLMs) and Copilot Actions
When a user asks for filtered data, Query Records is the default action assigned by the Planner Service.
Optimized for Structured Data Retrieval
o Ensures AI retrieves relevant CRM records quickly and accurately.
Why Not the Other Options?
B. Fetch Records
This is not a standard term in Einstein Copilot or Agentforce.
No defined Agentforce action exists under this name.
C. Select Records
Select Records is used to pick records from an already presented list, not to retrieve them initially.
If the user had already retrieved records and wanted to refine their selection, Select Records might be appropriate.
However, since the user’s request is to retrieve records, Query Records is the correct action.
Agentforce Specialist Reference
This information is confirmed from the Salesforce AI Specialist Material and Questions Document, where the Query Records action is explicitly defined as the appropriate standard action for retrieving filtered CRM records.
A
Explanation:
Why is Query Records the Correct Answer?
In Agentforce, the Planner Service is responsible for interpreting user requests and selecting the appropriate Copilot Action to fulfill them.
When a user issues a command like:
"Show me all the customers in New York",
the system must retrieve a list of customers filtered by location.
The Query Records action is designed precisely for this purpose.
Key Features of Query Records in Agentforce:
Retrieves Data Based on Specific Field Values
This action fetches Salesforce records that match a set of criteria, such as customers located in New York.
o Uses standard or custom object fields (e.g., BillingState = ‘New York’).
Works with Large Language Models (LLMs) and Copilot Actions
When a user asks for filtered data, Query Records is the default action assigned by the Planner Service.
Optimized for Structured Data Retrieval
o Ensures AI retrieves relevant CRM records quickly and accurately.
Why Not the Other Options?
B. Fetch Records
This is not a standard term in Einstein Copilot or Agentforce.
No defined Agentforce action exists under this name.
C. Select Records
Select Records is used to pick records from an already presented list, not to retrieve them initially.
If the user had already retrieved records and wanted to refine their selection, Select Records might be appropriate.
However, since the user’s request is to retrieve records, Query Records is the correct action.
Agentforce Specialist Reference
This information is confirmed from the Salesforce AI Specialist Material and Questions Document, where the Query Records action is explicitly defined as the appropriate standard action for retrieving filtered CRM records.
Question #2
Universal Containers needs to provide insights on the usability of Agents to drive adoption in the organization.
What should the Agentforce Specialist recommend?
- A . Agent Analytics
- B . Agentforce Analytics
- C . Agent Studio Analytics
Correct Answer: A
A
Explanation:
Agent Analytics: This tool is specifically designed to provide usability insights for Salesforce agents. It tracks metrics like adoption rates, task completion times, and efficiency levels, helping organizations identify areas where agents excel or need additional support.
Agentforce Analytics: This term does not correspond to a recognized Salesforce feature.
Agent Studio Analytics: This is unrelated to analyzing agent usability, as it primarily supports customization or development features rather than providing analytics for adoption.
Thus, Agent Analytics is the correct recommendation as it offers actionable insights to drive agent adoption and productivity.
Reference: "Boost Adoption with Analytics Tools | Salesforce" .
A
Explanation:
Agent Analytics: This tool is specifically designed to provide usability insights for Salesforce agents. It tracks metrics like adoption rates, task completion times, and efficiency levels, helping organizations identify areas where agents excel or need additional support.
Agentforce Analytics: This term does not correspond to a recognized Salesforce feature.
Agent Studio Analytics: This is unrelated to analyzing agent usability, as it primarily supports customization or development features rather than providing analytics for adoption.
Thus, Agent Analytics is the correct recommendation as it offers actionable insights to drive agent adoption and productivity.
Reference: "Boost Adoption with Analytics Tools | Salesforce" .
Question #3
What is An Agentforce able to do when the “Enrich event logs with conversation data" setting in Agent is enabled?
- A . View the user click path that led to each copilot action.
- B . View session data including user Input and copilot responses for sessions over the past 7 days.
- C . Generate details reports on all Copilot conversations over any time period.
Correct Answer: B
B
Explanation:
When the "Enrich event logs with conversation data" setting is enabled in Agent, it allows An Agentforce or admin to view session data, including both the user input and copilot responses from interactions over the past 7 days. This data is crucial for monitoring how the copilot is being used, analyzing its performance, and improving future interactions based on past inputs.
This setting enriches the event logs with detailed conversational data for better insights into the interaction history, helping Agentforce Specialists track AI behavior and user engagement.
Option A, viewing the user click path, focuses on navigation but is not part of the conversation data enrichment functionality.
Option C, generating detailed reports over any time period, is incorrect because this specific feature is limited to data for the past 7 days.
Salesforce Agentforce Specialist
Reference: You can refer to this documentation for further insights:
https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_event_logging.htm
B
Explanation:
When the "Enrich event logs with conversation data" setting is enabled in Agent, it allows An Agentforce or admin to view session data, including both the user input and copilot responses from interactions over the past 7 days. This data is crucial for monitoring how the copilot is being used, analyzing its performance, and improving future interactions based on past inputs.
This setting enriches the event logs with detailed conversational data for better insights into the interaction history, helping Agentforce Specialists track AI behavior and user engagement.
Option A, viewing the user click path, focuses on navigation but is not part of the conversation data enrichment functionality.
Option C, generating detailed reports over any time period, is incorrect because this specific feature is limited to data for the past 7 days.
Salesforce Agentforce Specialist
Reference: You can refer to this documentation for further insights:
https://help.salesforce.com/s/articleView?id=sf.einstein_copilot_event_logging.htm
Question #4
Universal Containers (UC) is building a Flex prompt template. UC needs to use data returned by the flow in the prompt template.
Which flow element should UC use?
- A . Add Flex Instructions
- B . Add Prompt Instructions
- C . Add Flow Instructions
Correct Answer: C
C
Explanation:
Context of the Question
Universal Containers (UC) wants to build a Flex prompt template that uses data returned by a Flow.
“Flex Prompt Templates” allow admins and Agentforce Specialists to incorporate external or dynamic data into generative AI prompts.
Why “Add Flow Instructions” Is Needed
Passing Flow Data into Prompt Templates: When configuring the prompt, you must specify how data from the running Flow is passed into the Flex template. The designated element for that is typically “Flow Instructions,” which map the Flow outputs to the prompt.
o Other Options:
Add Flex Instructions: Typically controls how the AI responds or structures the output, not how to bring Flow data into the template.
Add Prompt Instructions: Usually for static or manual instructions that shape the AI’s
response, rather than referencing dynamic data from the Flow.
Outcome
“Add Flow Instructions” ensures the prompt can dynamically use the data that the Flow returns―making Option C correct.
Salesforce Agentforce Specialist Reference & Documents
Salesforce Help & Training: Using Prompt Templates with Flow
Explains how to pass Flow variables into a prompt template via a specialized step (e.g., “Flow Instructions”).
Salesforce Agentforce Specialist Study Guide
Outlines how to configure generative AI prompts that reference real-time Flow data.
C
Explanation:
Context of the Question
Universal Containers (UC) wants to build a Flex prompt template that uses data returned by a Flow.
“Flex Prompt Templates” allow admins and Agentforce Specialists to incorporate external or dynamic data into generative AI prompts.
Why “Add Flow Instructions” Is Needed
Passing Flow Data into Prompt Templates: When configuring the prompt, you must specify how data from the running Flow is passed into the Flex template. The designated element for that is typically “Flow Instructions,” which map the Flow outputs to the prompt.
o Other Options:
Add Flex Instructions: Typically controls how the AI responds or structures the output, not how to bring Flow data into the template.
Add Prompt Instructions: Usually for static or manual instructions that shape the AI’s
response, rather than referencing dynamic data from the Flow.
Outcome
“Add Flow Instructions” ensures the prompt can dynamically use the data that the Flow returns―making Option C correct.
Salesforce Agentforce Specialist Reference & Documents
Salesforce Help & Training: Using Prompt Templates with Flow
Explains how to pass Flow variables into a prompt template via a specialized step (e.g., “Flow Instructions”).
Salesforce Agentforce Specialist Study Guide
Outlines how to configure generative AI prompts that reference real-time Flow data.
Question #5
In the context of retriever and search indexes, what best describes the data preparation process in Data Cloud?
- A . Data preparation focuses on real-time data ingestion and dynamic indexing to generate dynamic grounding reference data without preprocessing steps.
- B . Data preparation entails aggregating, normalizing, and encoding structured datasets to ensure compliance with data governance and security protocols.
- C . Data preparation Involves loading, chunking, vectorizing, and storing content in a search-optimized manner to support retrieval from the vector database.
Correct Answer: C
C
Explanation:
Why is "Loading, Chunking, Vectorizing, and Storing" the correct answer?
Agentforce AI-powered search and retriever indexing requires data to be structured and optimized for retrieval. The Data Cloud preparation process involves:
Key Steps in the Data Preparation Process for Agentforce:
Loading Data
Raw text from documents, emails, chat transcripts, and Knowledge articles is loaded into Data Cloud.
Chunking (Breaking Text into Small Parts)
AI divides long-form text into retrievable chunks to improve response accuracy.
o Example: A 1000-word article might be split into multiple indexed paragraphs.
Vectorization (Transforming Text for AI Retrieval)
o Each text chunk is converted into numerical vector embeddings.
o This enables faster AI-powered searches based on semantic meaning, not just keywords.
Storing in a Vector Database
o The processed data is stored in a search-optimized vector format.
o Agentforce AI retrievers use this data to find relevant responses quickly.
Why Not the Other Options?
C
Explanation:
Why is "Loading, Chunking, Vectorizing, and Storing" the correct answer?
Agentforce AI-powered search and retriever indexing requires data to be structured and optimized for retrieval. The Data Cloud preparation process involves:
Key Steps in the Data Preparation Process for Agentforce:
Loading Data
Raw text from documents, emails, chat transcripts, and Knowledge articles is loaded into Data Cloud.
Chunking (Breaking Text into Small Parts)
AI divides long-form text into retrievable chunks to improve response accuracy.
o Example: A 1000-word article might be split into multiple indexed paragraphs.
Vectorization (Transforming Text for AI Retrieval)
o Each text chunk is converted into numerical vector embeddings.
o This enables faster AI-powered searches based on semantic meaning, not just keywords.
Storing in a Vector Database
o The processed data is stored in a search-optimized vector format.
o Agentforce AI retrievers use this data to find relevant responses quickly.
Why Not the Other Options?
Question #6
Universal Containers (UC) needs to save agents time with AI-generated case summaries. UC has implemented the Work Summary feature.
What does Einstein consider when generating a summary?
- A . Generation is grounded with conversation context, Knowledge articles, and cases.
- B . Generation is grounded with existing conversation context only.
- C . Generation is grounded with conversation context and Knowledge articles.
Correct Answer: A
A
Explanation:
When generating a Work Summary, Einstein leverages multiple sources of information to provide a comprehensive and accurate case summary for agents.
Conversation Context:
Einstein analyzes the details of the customer interaction, including chat or email threads, to extract relevant information for the summary.
Knowledge Articles:
It considers linked Knowledge Articles or articles referred to during the case resolution process, ensuring the summary incorporates accurate resolutions or additional resources provided to the customer.
Cases:
Einstein also examines historical cases and related case records to ground the summary in context from past resolutions or interactions.
Option A is correct as it includes all three: conversation context, Knowledge articles, and
cases.
Option B is incorrect because it limits the grounding to conversation context only, excluding other critical elements.
Option C is incorrect because it omits case data, which Einstein considers for more accurate and contextually rich summaries.
Reference: "Einstein Work Summary and AI Case Management | Salesforce Trailhead" .
A
Explanation:
When generating a Work Summary, Einstein leverages multiple sources of information to provide a comprehensive and accurate case summary for agents.
Conversation Context:
Einstein analyzes the details of the customer interaction, including chat or email threads, to extract relevant information for the summary.
Knowledge Articles:
It considers linked Knowledge Articles or articles referred to during the case resolution process, ensuring the summary incorporates accurate resolutions or additional resources provided to the customer.
Cases:
Einstein also examines historical cases and related case records to ground the summary in context from past resolutions or interactions.
Option A is correct as it includes all three: conversation context, Knowledge articles, and
cases.
Option B is incorrect because it limits the grounding to conversation context only, excluding other critical elements.
Option C is incorrect because it omits case data, which Einstein considers for more accurate and contextually rich summaries.
Reference: "Einstein Work Summary and AI Case Management | Salesforce Trailhead" .
Question #7
For an Agentforce Data Library that contains uploaded files, what occurs once it is created and configured?
- A . Indexes the uploaded files in a location specified by the user
- B . Indexes the uploaded files into Data Cloud
- C . Indexes the uploaded files in Salesforce File Storage
Correct Answer: B
B
Explanation:
Comprehensive and Detailed In-Depth
In Salesforce Agentforce, a Data Library is a feature that allows organizations to upload files (e.g., PDFs, documents) to be used as grounding data for AI-driven agents. Once the Data Library is created and configured, the uploaded files are indexed to make their content searchable and usable by the AI (e.g., for retrieval-augmented generation or prompt enhancement). The key question is where this indexing occurs. Salesforce Agentforce integrates tightly with Data Cloud, a unified data platform that includes a vector database optimized for storing and indexing unstructured data like uploaded files. When a Data Library is set up, the files are ingested and indexed into Data Cloud’s vector database, enabling the AI to efficiently retrieve relevant information from them during conversations or actions.
Option A: Indexing files in a "location specified by the user" is not a feature of Agentforce Data Libraries. The indexing process is managed by Salesforce infrastructure, not a user-defined location.
Option B: This is correct. Data Cloud handles the indexing of uploaded files, storing them in its vector database to support AI capabilities like semantic search and content retrieval.
Option C: Salesforce File Storage (e.g., where ContentVersion records are stored) is used for general file storage, but it does not inherently index files for AI use. Agentforce relies on Data Cloud for indexing, not basic file storage.
Thus, Option B accurately reflects the process after a Data Library is created and configured in Agentforce.
Reference: Salesforce Agentforce Documentation: "Set Up a Data Library" (Salesforce Help: https://help.salesforce.com/s/articleView?id=sf.agentforce_data_library.htm&type=5)
Salesforce Data Cloud Documentation: "Vector Database for AI" (https://help.salesforce.com/s/articleView?id=sf.data_cloud_vector_database.htm&type=5)
B
Explanation:
Comprehensive and Detailed In-Depth
In Salesforce Agentforce, a Data Library is a feature that allows organizations to upload files (e.g., PDFs, documents) to be used as grounding data for AI-driven agents. Once the Data Library is created and configured, the uploaded files are indexed to make their content searchable and usable by the AI (e.g., for retrieval-augmented generation or prompt enhancement). The key question is where this indexing occurs. Salesforce Agentforce integrates tightly with Data Cloud, a unified data platform that includes a vector database optimized for storing and indexing unstructured data like uploaded files. When a Data Library is set up, the files are ingested and indexed into Data Cloud’s vector database, enabling the AI to efficiently retrieve relevant information from them during conversations or actions.
Option A: Indexing files in a "location specified by the user" is not a feature of Agentforce Data Libraries. The indexing process is managed by Salesforce infrastructure, not a user-defined location.
Option B: This is correct. Data Cloud handles the indexing of uploaded files, storing them in its vector database to support AI capabilities like semantic search and content retrieval.
Option C: Salesforce File Storage (e.g., where ContentVersion records are stored) is used for general file storage, but it does not inherently index files for AI use. Agentforce relies on Data Cloud for indexing, not basic file storage.
Thus, Option B accurately reflects the process after a Data Library is created and configured in Agentforce.
Reference: Salesforce Agentforce Documentation: "Set Up a Data Library" (Salesforce Help: https://help.salesforce.com/s/articleView?id=sf.agentforce_data_library.htm&type=5)
Salesforce Data Cloud Documentation: "Vector Database for AI" (https://help.salesforce.com/s/articleView?id=sf.data_cloud_vector_database.htm&type=5)
Question #8
What is an appropriate use case for leveraging Agentforce Sales Agent in a sales context?
- A . Enable a sates team to use natural language to invoke defined sales tasks grounded in relevant data and be able to ensure company policies are applied. conversationally and in the now or work.
- B . Enable a sales team by providing them with an interactive step-by-step guide based on business rules to ensure accurate data entry into Salesforce and help close deals fatter.
- C . Instantly review and read incoming messages or emails that are then logged to the correct opportunity, contact, and account records to provide a full view of customer interactions and communications.
Correct Answer: A
A
Explanation:
Agentforce Sales Agent is designed to let sales teams perform tasks via natural language commands, leveraging Salesforce data while adhering to policies. For example, agents can ask the AI to "update the opportunity stage to Closed Won" or "generate a quote," with the system enforcing validations and data security. This use case aligns with Salesforce’s vision of conversational AI streamlining workflows without compromising compliance.
Step-by-step guides (B) are typically handled by tools like Dynamic Forms or Guided Selling, not Agentforce.
Logging messages/emails (C) is managed by Email-to-Case or Service Cloud, not a sales-specific AI agent.
Reference: Salesforce Help Article: Agentforce for Sales ("Use Cases and Capabilities" section).
Einstein Agentforce Specialist Trailhead: "Sales Automation with Agentforce" (Natural Language Task Execution).
A
Explanation:
Agentforce Sales Agent is designed to let sales teams perform tasks via natural language commands, leveraging Salesforce data while adhering to policies. For example, agents can ask the AI to "update the opportunity stage to Closed Won" or "generate a quote," with the system enforcing validations and data security. This use case aligns with Salesforce’s vision of conversational AI streamlining workflows without compromising compliance.
Step-by-step guides (B) are typically handled by tools like Dynamic Forms or Guided Selling, not Agentforce.
Logging messages/emails (C) is managed by Email-to-Case or Service Cloud, not a sales-specific AI agent.
Reference: Salesforce Help Article: Agentforce for Sales ("Use Cases and Capabilities" section).
Einstein Agentforce Specialist Trailhead: "Sales Automation with Agentforce" (Natural Language Task Execution).
Question #9
When creating a custom retriever in Einstein Studio, which step is considered essential?
- A . Select the search index, specify the associated data model object (DMO) and data space, and optionally define filters to narrow search results.
- B . Define the output configuration by specifying the maximum number of results to return, and map the output fields that will ground the prompt.
- C . Configure the search index, choose vector or hybrid search, choose the fields for filtering, the data space and model, then define the ranking method.
Correct Answer: A
A
Explanation:
Comprehensive and Detailed In-Depth
In Salesforce’s Einstein Studio (part of the Agentforce ecosystem), creating a custom retriever involves setting up a mechanism to fetch data for AI prompts or responses. The essential step is defining the foundation of the retriever: selecting the search index, specifying the data model object (DMO), and identifying the data space (Option A). These elements establish where and what the retriever searches:
Search Index: Determines the indexed dataset (e.g., a vector database in Data Cloud) the retriever queries.
Data Model Object (DMO): Specifies the object (e.g., Knowledge Articles, Custom Objects) containing the data to retrieve.
Data Space: Defines the scope or environment (e.g., a specific Data Cloud instance) for the
data.
Filters are noted as optional in Option A, which is accurate―they enhance precision but aren’t mandatory for the retriever to function. This step is foundational because without it, the retriever lacks a target dataset, rendering it unusable.
Option B: Defining output configuration (e.g., max results, field mapping) is important for shaping the retriever’s output, but it’s a secondary step. The retriever must first know where to search (A) before output can be configured.
Option C: This option includes advanced configurations (vector/hybrid search, filtering fields, ranking method), which are valuable but not essential. A basic retriever can operate without specifying search type or ranking, as defaults apply, but it cannot function without a search index, DMO, and data space.
Option A: This is the minimum required step to create a functional retriever, making it essential.
Option A is the correct answer as it captures the core, mandatory components of retriever setup in Einstein Studio.
Reference: Salesforce Agentforce Documentation: "Custom Retrievers in Einstein Studio" (Salesforce
Help: https://help.salesforce.com/s/articleView?id=sf.einstein_studio_retrievers.htm&type=5)
Trailhead: "Einstein Studio for Agentforce" (https://trailhead.salesforce.com/content/learn/modules/einstein-studio-for-agentforce)
A
Explanation:
Comprehensive and Detailed In-Depth
In Salesforce’s Einstein Studio (part of the Agentforce ecosystem), creating a custom retriever involves setting up a mechanism to fetch data for AI prompts or responses. The essential step is defining the foundation of the retriever: selecting the search index, specifying the data model object (DMO), and identifying the data space (Option A). These elements establish where and what the retriever searches:
Search Index: Determines the indexed dataset (e.g., a vector database in Data Cloud) the retriever queries.
Data Model Object (DMO): Specifies the object (e.g., Knowledge Articles, Custom Objects) containing the data to retrieve.
Data Space: Defines the scope or environment (e.g., a specific Data Cloud instance) for the
data.
Filters are noted as optional in Option A, which is accurate―they enhance precision but aren’t mandatory for the retriever to function. This step is foundational because without it, the retriever lacks a target dataset, rendering it unusable.
Option B: Defining output configuration (e.g., max results, field mapping) is important for shaping the retriever’s output, but it’s a secondary step. The retriever must first know where to search (A) before output can be configured.
Option C: This option includes advanced configurations (vector/hybrid search, filtering fields, ranking method), which are valuable but not essential. A basic retriever can operate without specifying search type or ranking, as defaults apply, but it cannot function without a search index, DMO, and data space.
Option A: This is the minimum required step to create a functional retriever, making it essential.
Option A is the correct answer as it captures the core, mandatory components of retriever setup in Einstein Studio.
Reference: Salesforce Agentforce Documentation: "Custom Retrievers in Einstein Studio" (Salesforce
Help: https://help.salesforce.com/s/articleView?id=sf.einstein_studio_retrievers.htm&type=5)
Trailhead: "Einstein Studio for Agentforce" (https://trailhead.salesforce.com/content/learn/modules/einstein-studio-for-agentforce)
Question #10
Universal Containers (UC) configured a new PDF file ingestion in Data Cloud with all the required fields, and also created the mapping and the search Index. UC Is now setting up the retriever and notices a required fleld is missing.
How should UC resolve this?
- A . Create a new custom Data Cloud object that includes the desired field.
- B . Update the search index to include the desired field.
- C . Modify the retriever’s configuration to include the desired field..
Correct Answer: B
B
Explanation:
Why is "Update the search index to include the desired field" the correct answer?
When configuring a retriever in Data Cloud for PDF file ingestion, all necessary fields must be included in the search index. If a required field is missing, the correct action is to update the search index to ensure it is available for retrieval.
Key Considerations for Fixing Missing Fields in Data Cloud Retrievers:
Search Index Controls Which Fields Are Searchable
The search index defines which fields are indexed and accessible to the retriever.
oIf a field is missing, it must be added to the index before it can be queried.
Ensures Complete and Accurate Data Retrieval
Without indexing, the retriever cannot reference the missing field in AI responses.
oUpdating the index makes the field available for AI-powered retrieval.
Supports AI-Grounded Responses
Agentforce relies on Retriever-Augmented Generation (RAG) to ground AI responses in searchable Data Cloud content.
o Ensuring all relevant fields are indexed improves AI-generated answer accuracy.
Why Not the Other Options?
B
Explanation:
Why is "Update the search index to include the desired field" the correct answer?
When configuring a retriever in Data Cloud for PDF file ingestion, all necessary fields must be included in the search index. If a required field is missing, the correct action is to update the search index to ensure it is available for retrieval.
Key Considerations for Fixing Missing Fields in Data Cloud Retrievers:
Search Index Controls Which Fields Are Searchable
The search index defines which fields are indexed and accessible to the retriever.
oIf a field is missing, it must be added to the index before it can be queried.
Ensures Complete and Accurate Data Retrieval
Without indexing, the retriever cannot reference the missing field in AI responses.
oUpdating the index makes the field available for AI-powered retrieval.
Supports AI-Grounded Responses
Agentforce relies on Retriever-Augmented Generation (RAG) to ground AI responses in searchable Data Cloud content.
o Ensuring all relevant fields are indexed improves AI-generated answer accuracy.
Why Not the Other Options?