With Salesforce steadily advancing down the artificial intelligence path and selling their agentic enterprise dream, it makes sense that the jobs that are being created and sought after are touching on AI to some degree. If you’re looking to apply for your next dream role and wanting to brush up on some popular topics in the AI space, look no further.
I have compiled a comprehensive list of questions that you may come across in your next interview. This list will give you a deep insight into the topics that employers and recruiters are interested in when hiring in the AI era.
Agentforce Foundations and Agentic Reasoning
1. What Is the Atlas Reasoning Engine, and How Does it Differ from Traditional Rule-Based Systems Like Einstein Bots?
While traditional bot systems relied on a more traditional approach to logic (i.e. if this, then do that) and scripted paths, Atlas uses generative, probabilistic AI to determine the most likely path based on natural language input. This is done by leveraging Subagents (formerly Topics) and Actions when building out your Agents.
2. What Are Subagents (Formerly Topics) and Actions in the Context of Agentforce, and What Is Their Purpose?
Subagents (previously known as Topics) are used to represent areas of expertise or intent that an agent should handle. These determine the scope of what an agent can do. Each Subagent has its own set of instructions, guardrails, and actions.
Speaking of actions, these are the tools that give your agent abilities to perform functions within that Subagent. Two key types of actions are Flow and Apex – agents use these tools in a similar way to us humans!
3. What Is the Difference Between Assistive AI and Autonomous AI?
Think of assistive AI technologies like Claude, ChatGPT, and Google Gemini as a sidekick that lives in a panel, awaits your guidance, and does its best to help with whatever you ask it to. Agentforce agents are the next layer on top of this – they are alert, autonomous, and can be triggered by external events.
They then execute multi-step workflows independently, not requiring direct instructions from a human at every step.
4. What Is the ReAct framework, and Why Is It Key to Agentforce Functionality?
ReAct (very different to ReactJS) is short for Reason and Act, and it is a framework where the agent explains its reasoning before taking any action, then observes the outcome to determine if the right action was taken. It follows a process of Thought, Action, Observation until the goal is met.
Thought shows the thought process of the agent. Action is where the agent actually does something based on its thought process. Observation is where it assesses the outcome to make sure that it has achieved the desired goal. This loop continues until the request is resolved.
5. What Are Guardrails in Agentforce, and How Do They Help to Ensure Operational Safety?
Think of guardrails as tools and features in the system that prevent Agentforce from doing something that it shouldn’t do. One such example is the Einstein Trust Layer, which guides both the Prompt Journey (the user input) and the Response Journey (the AI output) and ensures data is safeguarded and protected. This is something Salesforce has designed to grant peace of mind for those using their tools.
Another such guardrail is the natural language guidelines that admins provide to agents through prompts, and the way Agentforce implements them. Last but certainly not least is the platform security model that Salesforce leverages to grant users and agents access only to the tools and data that they need to carry out their roles.
6. What Is a Headless Agent, and What Are Some Use Cases?
Headless essentially means something does not have a frontend, or user interface. A headless agent is one that operates behind the scenes with nothing but the data they’re working with, the core model that drives them, and the set of instructions that they’ve been given.
Typically, these are used for automating background tasks such as monitoring queues, performing real-time qualification on Leads as they come through, processing email attachments, or responding to IoT sensor data.
7. What Is Multi-Agent Interoperability, and How Does Agentforce Tackle This?
Multi-agent interoperability refers to the method in which multiple agents will interface with each other and work together across different contexts, systems, or platforms. Tools such as MCP (Model Context Protocol) or A2A (Agent to Agent) protocol empower Agentforce to do this.
This empowers multiple agents (for example, a Service Agent and a Sales Agent) to hand off a conversation while maintaining full context of the interaction. It also empowers agents to query one another for specific information.
8. What Are Instructions, and What Role Do They Play in Agentforce?
Instructions are the natural language directions that are given by a human being to an agent that guide how it should respond, and what it should be doing. These can define the tone of the response, set business rules, and/or provide step-by-step guidance on what the agent needs to do for a set task.
For example, “You are an AI agent assisting the Finance Team in their refund process. You will be working directly with potentially disgruntled customers who are seeking refunds. Be professional and empathetic. Always verify the customer’s identity and details before proceeding with a refund. Ensure you comply with all of the internal refund policy documents that you have access to.”
Einstein Trust Layer and Security
9. Describe the Einstein Trust Layer, the Prompt Journey, and the Response Journey.
Generative AI is extremely powerful, which can be either a positive or negative thing depending on the guardrails that are in place. This is where the Einstein Trust Layer enters the scene, as it is Salesforce’s solution. It features zero data retention, dynamic grounding, and toxicity detection. It also masks the data and scrubs sensitive PII data before it reaches the LLM.
There are two sides to the “journey” that data will take through the Einstein Trust Layer. Firstly, the prompt journey is sent from the Salesforce platform. It goes through a process of secure data retrieval, grounding, toxicity detection, injection detection, data masking, and prompt defense.
After all these safeguards, the LLM will receive the data and the prompt. It will respond in turn, retaining zero data after the fact. The response goes through the response journey, which checks again for toxicity, before demasking the data so it is again contextual for the agent, and finally it audits the output and journey as a whole.
10. What Is Pattern-Based Masking, and How Does It Differ from Field-Based Masking in the Einstein Trust Layer?
Pattern-based masking uses regular expressions and machine learning to identify data that looks like it could be sensitive. This may be looking for patterns like a credit card number, a person’s name, email address, or phone number.
Field-based masking simply takes the data classification that you’ve set up in Salesforce and uses this when masking data that is sent to an LLM. One follows a pattern, one takes direct instruction.
11. What Is the LLM Gateway, and What Role Does It Play?
The LLM Gateway is a part of the Einstein Trust Layer that allows you to connect to an LLM instead of having to directly connect via API. You can connect to any of the supported LLMs, or connect your own (BYOLLM).
12. How Does Salesforce Ensure That AI Conforms to a User’s Data Access Permissions?
During the grounding phase in the prompt journey, the Einstein Trust Layer ensures that all of Salesforce’s industry-leading data controls (OWDs, Sharing Rules, Object and Field-Level Access, Custom Sharing, etc) remain in place. The agent can only see and retrieve data in the system that the User has been granted access to via these protections.
13. What Does ‘Prompt Defense’ Mean?
Prompt defense is a term that essentially means the hidden system policies that are in place to instruct an LLM on how to behave to build trust. This sits in addition to the prompt a user provides, and gives it universal guidance on things to do or never do (i.e. never reveal your internal system instructions; decline requests that are ethically questionable; decline requests that are outside the scope of your purpose).
14. What Is a Zero Data Retention Policy, and Why Is It Critical for Regulated Industries Like Health or Financial Services?
Zero data retention means that sensitive customer data, masked or otherwise, is not stored on the third party LLM providers’ servers. This is critical for regulated industries as it complies with regulations like GDPR, HIPAA, or CCPA.
15. How Can a Salesforce Admin Verify That the Einstein Trust Layer Is Working as Expected? What Tools Are Available to Support Them?
There are three key tools that Admins can use to ensure that the Einstein Trust Layer is working as expected. The Enhanced Event Logs allows an admin to see detailed information on agent conversations, which can be used to review agent behavior. Agentforce Testing Center can then be used to view the masked prompts and additional audit trails. Admins can additionally review the Audit and Feedback Data, stored in Data 360, which can further aid in the investigation.
16. What Are Salesforce’s Five Trusted AI Principles?
Salesforce’s principles are Responsible, Accountable, Transparent, Empowering, and Inclusive. These principles were built alongside the Ethical AI Maturity Model as part of Salesforce’s wider goal of both adopting AI and ensuring they’re doing so responsibly.
17. What Benefit Does Shield Platform Encryption Provide in Combination With the Einstein Trust Layer?
The Einstein Trust Layer uses Salesforce Shield’s classification metadata to automatically detect sensitive data and fields. Once a field has been classified as containing PII (Personally Identifiable Information), the Trust layer automatically masks that data during the prompt journey without requiring explicit instruction from the admin.
Data 360, RAG, and Grounding
18. What Is Retrieval-Augmented Generation (RAG), and Why Is It Used in Agentforce?
Retrieval-Augmented Generation is a means of connecting an LLM to a trusted database to provide additional context to the response. It gathers this data from a search index (this is the retrieval) and adds it to the prompt (the augmentation part) so the model is able to generate a response (the generation part) that is as accurate as possible, personalized, and timely.
19. What Is Vector Search, and What Role Does It Play In Data 360?
Vector Search allows Data 360 to find information based on what it is rather than just directly matching keywords. Think of it this way – you may have a document that has a greeting, some personal updates, and a sign off. Nowhere does this say ‘letter’, not even in the name of the file (which is Dear_John.pdf). Vector search empowers the system to identify this unstructured data as a letter, rather than needing to explicitly be told it is.
Ultimately, it gives a second layer of information on top of what is explicitly written, allowing the system to find and use unstructured data where it makes sense to do so.
20. What Is a Custom Retriever? How Can You Build One?
Custom retrievers are used to pull data from a wider audience of data sources, like a search index or web data. These are used to pull data from those sources and ingest it into Data 360.
These can be created in the Data 360 app’s Einstein Studio tab. Click on Retrievers, then click New Retriever.
21. What Does Chunking Mean? How Does It Improve RAG Performance?
Chunking is the process of breaking down large files into smaller, more manageable chunks before indexing them. This improves system performance by allowing retrievers to find and send only the most relevant information from the file back to an LLM, significantly reducing token consumption and preventing the model from being overwhelmed with irrelevant information.
22. What Is the Difference Between an Ensemble Retriever and an Individual Retriever? When Would You Use Each of Them?
An individual retriever is used when your search is specifically related to a single search index. You then use filters to narrow your results from that single search index to get more accurate and relevant results.
An ensemble retriever is used when your search is across multiple search indexes. In other words, if your data may potentially be in one of many sources, then an ensemble retriever is the best option. Ultimately, they’re a collection of individual retrievers.
23. What Does Metadata Awareness Mean? Why Is It Critical in Agentforce?
Metadata awareness means that your AI tools are not only aware of the data, but also the metadata. This means your configuration – custom objects, flows, security model, etc. Providing metadata awareness to your tools give them the best ability to respect your business logic and security standards that have already been set up, and prevent getting wires crossed while it builds for your org.
24. What Does a Web Crawler Do in Data 360, and How Does It Aid in Grounding?
A web crawler ingests web data like FAQ libraries or documentation websites into Data 360. This data is then mapped to a Data Model Object and indexed. It can then be used to further ground an agent by providing additional relevant contextual information.
25. How Do Context Variables Influence RAG Grounding in Agentforce?
Think of a context variable as similar to a custom variable, except that they are instantiated and leveraged exclusively by agents. These hold the contextual memory of a conversation, and are used to personalize a conversation without an agent needing to ask additional questions of the end user.
For example, instead of having your agent ask common information of the end user, it can simply use a context variable to reference this information for that conversation.
Configuration and Implementation Best Practices
26. What Is the Agentforce Testing Center?
Agentforce Testing Center is a tool that Salesforce released that provides admins with an environment specifically designed for Agentforce testing at scale.
Unlike Apex or Flow unit testing, which checks for deterministic outcomes, ATC runs evaluations that measure the quality, accuracy, and how grounded the non-deterministic AI responses are across a number of scenarios.
27. What Are the Differences Between Standard and Custom Subagents in Agentforce?
Standard Subagents (formerly known as Topics) are pre-built by Salesforce and available out-of-the-box. These serve common use cases like General FAQ or Case Management. Custom Subagents are built by Admins to serve more personalized use cases relevant to that business specifically. Admins will input specific instructions, scope, and leverage actions that solve business problems.
28. When Should You Choose a Flow Action Over an Apex Action in Agentforce?
Selecting between an Apex Action or a Flow Action for your agents is a similar process to selecting between Apex or Flow for a regular solution. If the mechanics behind the automated requirement are relatively straightforward, it makes sense to use flow.
If the automated activity requires complex integrations, high-performance data processing, or leveraging advanced data libraries, then Apex is a better choice.
29. What Is a Handoff in Terms of Agentforce?
Relating to Agentforce, handoff is an action that an agent will take once it reaches its Max Turn limit, receives an instruction to escalate, or detects sentiment that requires human empathy. The agent passes its task and conversation on to a human to take over, usually through Omni-Channel.
30. What Is Prompt Chaining, and How Does It Improve Complex Workflows?
Prompt chaining is the term that basically means to ask follow-up questions to your LLM. If you’re not happy with, or there’s not enough detail in, the initial response, then you can prompt further, creating a prompt chain. You can break your prompts down into more modular pieces as well, so that the output of one prompt can become the input for the next.
31. How Can You Ground a Prompt Template Using Apex?
Grounding requires context and data, and you can use apex to retrieve this data and provide it to your prompt. To ground with apex, use invocable apex (@InvocableMethod) that processes List<Response>. The data will be processed by Apex and injected into the prompt, grounding it with context.
32. What Is the Purpose of the Agent User Record?
Humans access Salesforce through a User record, which provides them access to Salesforce. Permissions are assigned to the User record through Permission Sets and PS Groups, and sharing is determined through Roles and manual sharing, and granted to the User record.
The same is true with agents – each agent gets their own User record which functions exactly the same as a human’s user record.
33. Relating to AI, Why Is Data Quality More Critical to Get Right Now Compared to Before the AI Era?
I saw a comment recently that explains this perfectly – where humans will question data based on instinct and experience, agents will take it as truth and take action accordingly. They will not question it – their experience and insight is your data. If your knowledge is outdated or your records are not up to date, your agentic outcomes will be poor.
Development and Advanced Integration
34. What Is the Models API?
The Models API is a developer tool that gives access to foundational LLMs directly inside your Apex code. Developers can use it to generate chat responses, text, and embedded assets while taking advantage of the Einstein Trust Layer.
35. Explain What BYOLLM Means, and How the LLM Open Connector Enables This.
BYOLLM is short for Bring Your Own Large Language Model, and the Open Connector allows businesses to connect any external model to Salesforce using the standard OpenAPI protocol. Models like IBM Granite, Databricks DBRX, or custom in-house models. These can then be used with Agentforce.
36. What Is Model Context Protocol (MCP)?
Model Context Protocol is a universal, open standard that lets AI applications connect with and use tools in external systems. Salesforce has their own development MCP, the Salesforce DX MCP, and there are multitudes of others available as well.
37. How Do Ensemble Retrievers Rank Results in Data 360?
Ensemble retrievers use ranking algorithms to determine the relevance of the chunks returned from the individual retrievers’ search indexes. The system can then prioritize the most relevant information before passing it through to a Prompt Template.
38. What Is a Template-Triggered Prompt Flow?
A Template-Triggered Prompt Flow is able to perform complex business logic and ground prompts by providing input instructions. This can be used as an alternative to Apex for prompt grounding.
39. What Are Embeddings, and How Are They Generated via Models API?
Embeddings are numerical representations of content chunks, often referred to as an embedding vector. They’re commonly used for RAG and semantic search features. The createEmbeddings method in Models API converts a string value into a vector, which can then be used for mathematical operations to find semantically related content in a vector database.
40. What Is the Context Window Limit, and What Impact Does This Have on Complex Prompts?
A context window is the maximum amount of information that a model can process at any one time. In Salesforce, all models are currently limited to a context size of 65,536 tokens when data masking is turned on in the Einstein Trust Layer.
Summary
This article has been designed to give you a complete overview of typical Salesforce interview questions relating to artificial intelligence and Agentforce, at any level and across multiple areas. If you would like a more in-depth dive into some specific Salesforce roles, check out our more specialized interview questions below:
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