Salesforce Agentforce Interview Questions and Answers – Part 2

Welcome to Part 2 of our Salesforce Agentforce interview questions series! In Part 1, we covered the foundational concepts of Agentforce, including its architecture, key components like Prompt Builder, and the standard AI Sales Coach. If you are new to Agentforce or want to refresh your knowledge on the basics, we highly recommend you check out Part 1 of our guide first. Now, it’s time to dive deeper.

Let’s get started!

1. What is the role of the Atlas Reasoning Engine?

The Atlas Reasoning Engine is the “brain” of every AI Agent. While Data Cloud provides the context (the “what”) and the Large Language Model (LLM) provides the language, the Atlas Reasoning Engine provides the logic (the “how”).

Its primary role is to interpret a user’s intent and dynamically create a multi-step plan to fulfill the request. When a user asks a complex question, the engine:

1. Interprets Intent: Understands what the user is really asking for.
2. Creates a Plan: Breaks the request down into a logical sequence of tasks.
3. Selects Tools: Scans its library of available Agent Actions (skills) to find the right tools for each task.
4. Executes and Adapts: Runs the selected actions in order to get the final answer.

2. How does the Einstein Trust Layer work with Agentforce?

The Einstein Trust Layer is the foundational security framework that sits between your Salesforce data and the Large Language Models (LLMs). It ensures your data is handled safely and privately. It works on three main pillars:

1. Secure Data Retrieval: Uses “Grounding” to securely pull relevant, real-time data from your Data Cloud and Salesforce records.
2. Data Masking: Before sending a prompt to the LLM, the Trust Layer automatically masks Personally Identifiable Information (PII) like names and emails. The LLM only sees the masked data.
3. Zero Retention & Toxicity Monitoring: Enforces a zero-retention policy with LLM partners (your data is never stored by them) and monitors prompts/responses for harmful content.

To read more about Einstein Trust layer refer the salesforce help article .

3. What is the difference between an AI Agent and a traditional Einstein Bot?

This is a critical distinction.

• Einstein Bot (Rule-Based): A traditional Einstein Bot is scripted. You must manually define a conversation path with specific dialogs and rules. It’s excellent for structured, predictable conversations (like “What are your store hours?”) but fails when a user goes “off-script.”
  
• AI Agent (Reasoning-Based): An AI Agent is dynamic. It uses the Atlas Reasoning Engine to understand user intent. You don’t build a rigid script; you give it a set of “skills” (Agent Actions), and the agent figures out which ones to use and in what order to solve the user’s problem.

In short, you have to tell an Einstein Bot exactly what to do. You just have to teach an AI Agent what it can do, and it figures out the rest.

4. Explain the primary use case for the Agentforce SDR Agent.

The Agentforce SDR Agent is a pre-built AI Agent for Sales Cloud designed to automate the repetitive, top-of-funnel tasks of a human Sales Development Representative (SDR).

Its primary use case is to qualify leads and expand outreach at scale. Instead of human SDRs spending hours sending introductory emails and logging activities, the SDR Agent handles it. It can autonomously send personalized follow-up emails, nurture new leads, log all activities, and hand off the lead to a human SDR as soon as the prospect shows positive intent (like replying to an email).

Refrer our detailed article on What is the Agentforce SDR Agent and it full setup Click here

5. How does the SDR Agent integrate with Sales Engagement (Cadences)?

The SDR Agent works directly with Sales Engagement and its Cadences. A Cadence is a pre-defined sequence of outreach steps (e.g., Email 1, Wait 2 Days, Call Task, Email 2). The SDR Agent can automate the “email” steps of this cadence.

Real-World Scenario:

1. A new lead is added to a “Webinar Follow-up” Cadence.
2. Day 1: The SDR Agent automatically executes the first step, “Send Intro Email.”
3. Day 3: The lead hasn’t responded. The SDR Agent automatically executes the next step, “Send Case Study Email.”
4. Day 5: The lead replies, “This looks interesting.”
5. The SDR Agent recognizes this positive intent, creates a “Follow-up Task” for the human SDR, and removes the lead from the automated cadence.

6. What are the key prerequisites for setting up the Agentforce SDR Agent?

To function correctly, the SDR Agent relies on several key Sales Cloud features. An admin must set these up first:

• Sales Engagement (Cadences): This is essential for defining the outreach sequences the agent will follow.
• Einstein Activity Capture (EAC): This is needed to automatically log the emails the agent sends and to capture the replies from prospects.
• Salesforce Inbox: This is required for the agent to send emails from the user’s connected email account.
• Data Cloud: This is used for auditing, feedback, and providing the agent with analytics.
  

7. What is the standard “Service Agent” for Service Cloud?

The Service Agent is a pre-built AI Agent for Service Cloud. Its primary use case is to be a customer’s first point of contact, automating common service inquiries and deflecting cases from human agents.

Key Service Agent Tasks:

• Answering Questions: Uses the Knowledge Base to answer the questions.
• Handling Inquiries: Fulfills common requests like “Where is my order?” or “What’s my case status?” by using Agent Actions.
• Intelligent Escalation: When an issue is too complex or the customer is frustrated, it can intelligently transfer the conversation and a full summary to the correct human agent.

8. How would an AI Agent use “Case Summarization” in the Service Console?

This is a key assistive feature for human agents. When a human agent opens a complex case with a long history of emails and activities, they can invoke the AI Agent to help.

The “Summarize Case” action will:

1. Read the entire case history (emails, case comments, field changes).
2. Provide the human agent with a concise, bulleted summary of the problem, what steps have already been taken, and what the customer’s current sentiment is.

This saves the agent 5-10 minutes of reading time and allows them to understand the customer’s issue immediately.

9. When would you use Apex vs. Flow to create an Agent Action?

This choice depends on the complexity of the task you want the AI Agent to perform.

• Use Flow for Agent Actions when:
  • The task is a standard, declarative process.
  • You need to create, update, or delete Salesforce records (e.g., “Create a Case,” “Update an Opportunity Stage”).
  • You are a low-code admin and want to build skills quickly.
• Use Apex for Agent Actions when:
  • You need to make a callout to an external API (e.g., check shipping status from FedEx).
  • The logic is highly complex and involves calculations or loops that are difficult in Flow.
  • You need to perform a mass operation on a large set of records with high efficiency.

For detailed guidance on these action check the official article : Agent Actions with Flow | Agent Actions with Apex

10. How can an AI Agent make a callout to an external system?

An AI Agent makes a callout by invoking an Agent Action that is fulfilled by an Apex class. You cannot make a callout directly from a Flow used by an agent. The Apex class must be defined with the @InvocableMethod annotation and perform the HTTP callout.

Real-World Scenario: Checking Shipping Status

1. User Asks: “Where is my order?”
2. Agent’s Plan: The Atlas Reasoning Engine invokes the “Check External Shipping Status” Agent Action.
3. Action Executes: This Agent Action is linked to an @InVokableMethod Apex class.
4. Apex Code: The Apex method makes a REST API callout to an external shipping carrier’s API (e.g., UPS) and returns the status.
5. Agent Responds: “Your order is currently ‘In Transit’ and is scheduled for delivery tomorrow.”

11. What are some limitations when building an Agent Action?

• Governor Limits: Agent Actions, whether in Flow or Apex, still run within the standard Salesforce transaction and are subject to all governor limits (SOQL queries, DML statements, CPU time).
• Parameter Passing: The AI Agent must be able to “fill in” the required input parameters for the action. If your Flow needs an AccountId but the agent can’t determine which account the user is asking about, the action will fail.
• Single-Purpose: Actions should be small and single-purpose (e.g., “Get Order Status” or “Create Case”). A single, massive action that tries to do 10 things is inefficient and hard for the reasoning engine to use.
• Clear Naming: The name and description of the Agent Action are critical. This is what the Atlas Reasoning Engine “reads” to understand what the skill does. Name them clearly (e.g., “FindOrderFromOrderNumber”).

12. How do you test an Agent Action before assigning it to the agent?

You can and should test your Agent Actions independently to ensure they work before you let the AI Agent rely on them.

• For Flows: You can use the “Debug” tool in Flow Builder to run the flow and provide sample inputs, just as you would with any other flow.
• For Apex: You must write an Apex Unit Test that calls your @InvocableMethod directly and asserts that it returns the expected output.
• In Agent Builder: Once added to an agent, you can use the “Preview” panel to test the action by manually selecting it and providing inputs, even without conversational text.

13. What is “Grounding” in Agentforce and why is it important for trust?

Grounding is the process of providing an LLM with specific, relevant, and real-time data from your company’s trusted sources at the moment a request is made.

It is the single most important concept for ensuring trust and preventing AI “hallucinations” (when the AI makes up incorrect information). Grounding “grounds” the AI’s response in fact.

Grounding data can come from:

• Data Cloud records
• Salesforce records (e.g., the Contact or Account)
• Salesforce Knowledge articles
• Apex Actions that retrieve external data

14. What is “Retrieval Augmented Generation” (RAG) and how does Agentforce use it?

Retrieval Augmented Generation (RAG) is the specific technology used to achieve Grounding with unstructured data, like Salesforce Knowledge. It is an AI technique that combines two ideas searching for information and creating text. It first looks up facts or data from a database or knowledge source, then uses that information to write more accurate and meaningful responses.

It works in two steps:

1. Retrieval: When a user asks a question, the agent first performs a semantic search on your Knowledge Base to retrieve the most relevant articles or snippets of text.
2. Augmentation: The agent then augments the prompt by adding this retrieved text. It tells the LLM, “Using the following information I found in our Knowledge Base: [snippet]… answer the user’s question: [user’s question].”

This allows the agent to answer complex “how-to” questions using your own approved documentation, rather than making up an answer.

15. What is a “Data Library” in Agentforce?

A “Data Library” is a feature that allows you to ground an AI Agent in data sources other than Salesforce Knowledge. While Knowledge is the primary source for RAG, a Data Library lets you point the agent to other repositories of information, such as specific files or external web content, to use as a source for its answers.

16. What’s the difference between “Grounding” and “Prompting”?

• Prompting is the act of giving instructions to the AI (e.g., “You are a helpful sales assistant. Be polite and concise.”).
• Grounding is the act of providing data and context within that prompt (e.g., “…here is the customer’s order history: [data]. Now, answer their question.”).

A good prompt template uses both: it has clear instructions (prompting) and is enriched with real-time data (grounding).

17. How does Agentforce handle multi-language support?

Agentforce is designed to be multi-lingual. The Atlas Reasoning Engine and the LLMs it uses can understand and respond in multiple languages (like Spanish, French, German, Japanese, etc.) out of the box.

For best results, you can provide translated versions of your Prompt Templates. For grounding in Salesforce Knowledge, you would need to have your Knowledge articles translated into the desired languages for the agent to be able to retrieve and use them effectively.

18. How does “Secure Data Retrieval” in the Einstein Trust Layer work?

Secure Data Retrieval is the “grounding” component of the Trust Layer. It’s an intelligent process that dynamically fetches only the data relevant to the user’s query.

Instead of sending the entire Account record to the LLM, if the user asks “What is this customer’s shipping state?”, the secure retrieval process will only pull the ShippingState field and ground the prompt with that single piece of data. This “just-in-time” data retrieval minimizes data exposure and is a core principle of the Trust Layer.

19. What are the key KPIs you would track to measure an agent’s ROI?

This is a great follow-up to monitoring. To measure Return on Investment (ROI), you need to track metrics that translate to business value:

1. Case Deflection Rate (Service Agent): The percentage of customer inquiries resolved by the agent without a human. This is a direct cost saving.
2. Reduction in Average Handle Time (AHT): For agents assisting humans, measure if features like “Case Summarization” are reducing the average time a human agent spends on a case.
3. Lead Conversion Rate (SDR Agent): Are leads nurtured by the SDR Agent converting to qualified opportunities at a higher or faster rate than before?
4. Customer Satisfaction (CSAT): Are customers who interact with the AI Agent reporting high satisfaction scores?
5. Agent Action Usage: Which actions are used most? This shows where the agent is providing the most value and can inform what to build next.
   
   And there could be other parameter bases on business use case.

20. How are user permissions and security enforced for an AI Agent?

This is a critical security concept: An AI Agent always runs in the context of the user who is interacting with it.

The agent does not have system-level or admin access. If a sales rep asks an agent, “Show me the revenue for the ‘Project Titan’ opportunity,” and that rep does not have permission to see that record due to sharing rules, the agent also cannot see it. The agent will respond, “I’m sorry, I don’t have access to that record.”

This ensures that Agentforce automatically respects all your existing Salesforce security, including:

• Object-Level Security
• Field-Level Security
• Organization-Wide Defaults (OWDs)
• Sharing Rules

You can further restrict an agent by controlling which profiles have access to which Agent Actions.