What’s the Difference Between Agentforce and Einstein GPT?
By Jenna Trott | 5 Minute Read

At A Glance
01. Understand the difference: Einstein GPT empowers teams with personalized insights and content, while Agentforce executes complex, autonomous workflows.
02. Learn how features like RAG, Salesforce Flow, and the Trust Layer ensure secure, accurate, and impactful AI operations.
03. Explore Agentforce’s no-code tools to create AI agents that automate multi-step tasks across systems and channels.
04. See how Einstein GPT refines communication with real-time CRM data, delivering contextually relevant and brand-aligned outputs.
Products Highlighted
Einstein GPT
Agentforce
Salesforce Flow
Salesforce Data Cloud
Atlas Reasoning Generation (RAG)
Salesforce, ever at the forefront of innovation, has given us some truly incredible products and services over the last few years, especially within the world of AI. In 2022, we saw the release of ChatGPT, and since then, the tech landscape has seen an explosion of AI-driven tools, each vying to cement their position as the best way for organizations to maximize data potential and engage with customers.
Salesforce themselves have unveiled two standout solutions: Einstein GPT and its latest innovation, Agentforce. Both are designed to help organizations automate processes and improve customer interactions but what exactly makes them different?
Is Agentforce truly different from Einstein GPT? Let’s take a closer look.
What is Einstein GPT?
In March 2023, Salesforce unveiled Einstein GPT, a groundbreaking AI tool that generates auto-created content directly within the Salesforce platform—no extra tabs or software needed. By seamlessly merging public and private AI models with CRM data, Einstein GPT responds to simple natural language prompts, continuously updating its output to match real-time changes in customer information.
The result? Less time spent on manual content creation and more time dedicated to what really counts: building meaningful customer relationships.
How It Stands Out from Other AI Tools
Unlike other GPT models, Einstein GPT goes beyond generic text generation; it refines it using the real-time data your team has stored in Salesforce. That means your marketing content, sales messages, and service responses aren’t just “close enough”—they’re relevant, spot-on, and perfectly aligned to your brand voice.
And you don’t have to worry about the “shoddy generative AI” cliché; instead, Einstein GPT uses dynamic grounding to ensure that each piece of generated text is both contextually accurate and highly personalized for its recipient.
The Einstein GPT Trust Layer
If you followed ChatGPT’s early news cycle, you might’ve seen headlines about data breaches and compromised accounts. Salesforce wanted to rewrite that narrative with Einstein GPT by introducing its Trust Layer.
A key feature of the Einstein Trust Layer is its data masking capability. Before prompts reach the large language model (LLM), Einstein GPT automatically identifies and masks sensitive information—like personally identifiable data (PII) or security-tagged Salesforce fields. It uses pattern-based masking (leveraging machine learning to detect items such as Social Security numbers) and field-based masking (referencing metadata tags and encryption settings to hide sensitive fields). After the LLM returns a response, Einstein GPT “demasks” the information so you see the accurate data in context.
This rigorous process ensures your AI-driven workflows remain fast and fluid—without sacrificing security or brand reputation.
Image retrieved from Salesforce.
What is Agentforce?
So…that’s Einstein GPT–what’s Agentforce?
Announced in September and generally available in October of 2024, Agentforce is a low-code platform developed by Salesforce and designed to help organizations create, customize, and deploy AI agents that support both employees and customers. It allows teams with minimal coding experience to build AI agents using natural language queries through the Agent Builder generator. This makes it accessible for businesses looking to implement AI solutions without needing deep technical expertise.
How does Agentforce work?
Agentforce utilizes natural language processing and large language models (LLMs) to interpret user intent and execute tasks without human intervention. Through the Agent Builder tool, users can create agents by describing their desired actions and workflows in plain English, making the setup process accessible even for those with limited coding experience. Agentforce accomplishes this in a few key ways:
Agent Builder: Your No-Code Creation Hub
Agentforce Studio: Agentforce Studio features the Agent Builder, a user-friendly interface where you define agent behaviors without writing a single line of code. You outline workflows, tasks, and guardrails using natural language prompts. Want an agent to schedule appointments or handle basic troubleshooting? Simply tell Agent Builder the specifics, and it automatically transforms your instructions into actionable capabilities.
Natural Language Setup: Define topics and restrictions like you’re chatting with a colleague, rather than crafting complicated logic.
Multi-Channel Deployment: Launch the same agent to handle emails, voice calls, chat messages, or social channels for a truly omnichannel experience.
Topics serve as the building blocks that determine what your agent can and can’t do. They frame the scope of each conversation, ensuring Agentforce sticks to the tasks you’ve approved.
Dynamic Topic Handling: If a customer needs to switch gears mid-conversation—say, from updating an address to checking a delivery schedule—Agentforce seamlessly transitions to the right topic without missing a beat.
Guardrails for Safety: If a request falls outside your defined boundaries, the agent politely redirects or declines to tackle that specific task.
Image retrieved from Salesforce.
Actions: Powering Real-World Tasks
Once a topic is established, you can assign Actions for specific tasks like retrieving data or processing a transaction. Actions can tie into Salesforce Flows, Apex classes, or specialized prompts.
Flow Integration: Imagine a user wants to see open appointment slots. Agentforce taps a preconfigured Flow to fetch availability, filters out non-preferred times, and confirms a date—no human intervention necessary.
Retrieval-Augmented Generation (RAG)
To take Agentforce’s AI reasoning to the next level, Retrieval-Augmented Generation (RAG) taps into Salesforce Data Cloud—and other internal data sources—to ground the agent’s responses in real, up-to-date information.
Unlike an off-the-shelf large language model (LLM) that’s limited by its public training data, RAG dynamically embeds your company’s unique, proprietary data (think emails, PDFs, and chat logs) into each prompt. That means Agentforce isn’t just “winging it” with generic content; it’s pulling from your own vector database to answer questions with real context and fewer hallucinations.
Ensuring Safety and Guardrails
Agentforce isn’t just about efficiency—it’s also designed to operate ethically and responsibly:
Off-Topic Redirection: When a user asks something outside the agent’s scope, it redirects them or politely bows out.
Harm and Toxicity Detection: Inappropriate or harmful interactions trigger an immediate refusal.
Prompt Injection Defense: Agentforce can spot—and block—attempts to hijack or reverse-engineer the AI.
Image retrieved from Salesforce.
So, what’s the difference between Agentforce and Einstein GPT?
I get it, this is where it can get a bit confusing because both Agentforce and Einstein GPT are Salesforce products using generative AI, so on the surface, they sound like two versions of the same thing.
However, the primary distinction between Agentforce and Einstein AI lies in their ability to manage workflows, particularly the complexity and autonomy of their operations. Agentforce is built for handling intricate, multi-step processes autonomously, seamlessly interacting with diverse systems and data sources.
Imagine a customer asking about an order: Agentforce can check the inventory system, update the customer on delivery status, and even initiate an order exchange, all without human involvement. This level of automation streamlines efficiency and delivers fast, end-to-end solutions.
On the other hand, Einstein AI specializes in tasks that require less complexity and more user involvement. While it excels in predictive analytics and decision support, it generally does not operate autonomously in executing tasks.
For instance, when a user inquires about an order status, Einstein AI can provide relevant insights or suggestions but would not independently check databases or perform order management tasks. Instead, it acts as a highly capable assistant, empowering users with the information they need to make decisions and take action.
Requirements for Effective Agentforce Deployment
At the heart of any AI deployment–whether it’s Agentforce, Einstein AI, or something else, is the golden rule: garbage in, garbage out. If your data is messy or incomplete, your Agentforce agent—no matter how advanced—will only be as effective as the information it’s fed. That’s why clean, well-structured data is crucial, giving agents quick access to what they need to operate efficiently and make real-time decisions.
Bear in mind, however, that clean data is just one piece of the puzzle; your agent also needs to speak to the various platforms, applications, and systems in your tech ecosystem. When integration falters, multi-step workflows become tangled, causing delays and inaccuracies. Achieving this level of data quality and integration isn’t typically a do-it-yourself endeavor. In fact, it can even result in significant tech debt.
Data engineering demands specialized know-how in architecture, governance, and integration—a tough ask for internal teams already juggling daily tasks. To sidestep costly mistakes and lingering technical debt, it’s wise to partner with experienced data professionals. They’ll ensure your data is in top shape and that your Agentforce deployment hits the ground running.
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