Agentforce is a host of autonomous AI agents with extensive organization-specific business knowledge and advanced customer engagement capabilities. Essentially, it's an entire workforce packaged in one software solution. However, because of the all-encompassing nature of what Salesforce Agentforce AI can do, comprehensively describing what Agentforce is requires a longer answer.

In this article, our team of Salesforce solution implementation and management experts explain everything you should know about Agentforce, including:

• What is Agentforce?
• Agentforce's Features and Functions
• What to Expect From Agentforce AI
• A 4-Step Guide to Setting Up Agentforce

What is Agentforce?

Agentforce is an ultra-capable digital assistant that not only understands natural language but can also take action in real-time—whether for customer service, sales, or internal operations. Imagine a system where an AI agent isn't just answering common customer inquiries but can actually resolve issues, pull in data, and even create new AI sales agents on demand. With advanced reasoning and retrieval, Agentforce doesn't just regurgitate information—it figures things out.

First demonstrated at Dreamforce, Agentforce showcased its ability to build service agents across departments. At its core, it is built around a simple user interface designed for prompt engineering, where users can create and manage AI agents through natural language rather than complex coding. These agents process information using two key AI components: LLMs (large language models) that understand and categorize human input, and LAMs (large action models) that decide what actions to take based on the available data. 

These all-encompassing service agents aren't limited to one business function. They could be AI-powered sales coaches, first-line customer support and sales reps with the ability to seamlessly escalate when someone requests a human agent, and sales development representatives who can proactively engage leads. Agentforce can do all of this. It integrates with Data Cloud, APIs, and automation workflows to ensure it operates within your business's specific needs. 

In other words, beyond the ability to respond in a routine manner—Agentforce plans, reasons, and acts. By connecting to live data sources and adapting in real-time, Agentforce delivers personalized, efficient interactions without human intervention—unless, of course, you want it to hand off to a human, in which case it knows exactly when and how to do that, too. The idea behind Agentforce is to take automation beyond simple chatbots, making truly autonomous digital services or sales agents.

Agentforce's Features and Functions

So, how does Agentforce work? And what exactly can these service agents do? 

To answer both of these questions, we need to delve deeper into the specific features and functions that make Agentforce work. The following list gives you a comprehensive look into the technology backing Salesforce's Agentforce solution:

The Atlas Reasoning Engine

Agentforce's Atlas Reasoning Engine handles a wide variety of interactions, from simple requests to complex, multi-layered inquiries. For instance, for a straightforward customer query like "What is the status of my order?" an AI would use basic reasoning and quick data retrieval to generate a rapid response. However, a more intricate question such as "Given my production schedule, current raw material inventory, and supplier lead times, what is the optimal reorder strategy to prevent stockouts while minimizing holding costs?" demands enhanced reasoning capabilities. 

The Atlas Reasoning Engine goes beyond the simple conversational capabilities and reasoning of large language models. It's comprehensively attuned to all of the data in your overall system, meaning it can answer highly complex questions based on your pre-determined processes and pre-existing business information. 

Enriched Indexing

Enhanced Retrieval-Augmented Generation (RAG) with enriched indexing allows Agentforce to efficiently locate relevant information within unstructured content (AKA messy data). Traditional AI models struggle with unstructured data—emails, documents, or customer service transcripts—because they lack organization. However, Agentforce overcomes this challenge by using Data Cloud to break down unstructured content into manageable chunks, known as RAG chunks. These chunks are further enriched with metadata from the Salesforce Platform, ensuring they remain highly relevant to a company's specific business needs.

For example, imagine a customer service agent handling a warranty claim. Instead of a human agent manually searching through multiple databases, Agentforce can instantly retrieve relevant information—such as product details, warranty terms, and previous customer interactions—by leveraging enriched RAG indexing. Additionally, inline citations increase trust by showing precisely where each piece of retrieved information comes from, eliminating ambiguity and enhancing transparency. The result is a more accurate and efficient customer experience.

The Atlas Reasoning Engine takes this a step further by employing ensemble RAG techniques, which combine multiple retrieval models to improve accuracy. This approach allows agents to search across both structured (e.g., databases, CRM records) and unstructured (e.g., PDFs, chat logs) data sources, identifying similar language patterns to deliver precise and context-aware responses. As a result, Agentforce can execute informed actions with high reliability, reducing the time employees spend searching for critical information while ensuring customers receive accurate and timely support.

Agentic Loops

The Atlas Reasoning Engine is designed to think and adapt like a human problem solver, breaking down complex tasks into smaller, manageable steps and refining its approach as it gathers more information. This process, known as an agentic loop, allows AI agents to continuously evaluate their actions, adjust their strategies, and ensure optimal outcomes.

For example, imagine a manufacturer's customer submits an inquiry: "Why is my production line experiencing frequent equipment failures?" Instead of providing a generic response, the Atlas Reasoning Engine first determines the root intent—diagnosing operational inefficiencies. It then retrieves relevant data, such as maintenance logs, sensor readings, and historical repair records, and synthesizes this information into a preliminary analysis. If the data suggests potential causes but lacks definitive proof, the agent refines its approach by requesting additional details, such as production conditions or recent equipment modifications. This iterative process continues until the agent arrives at a well-supported diagnosis and can recommend specific corrective actions, such as scheduling predictive maintenance or adjusting operational parameters.

This ability to loop through multiple sources of data, evaluate responses, and refine its approach makes Agentforce highly effective at handling complex, multi-step interactions. Whether assisting customers with troubleshooting, guiding employees through intricate workflows, or optimizing business operations, the agentic loop ensures that every interaction is continuously improved for maximum accuracy and efficiency.

Topic Classification

To improve task execution, the Atlas Reasoning Engine evaluates user inputs against a broad set of topics that service agents can handle. It then selects the most relevant topic to ensure that each request is managed by the right agent. 

For instance, consider an AI-powered support agent working for a manufacturing company. If a customer asks, "What are the safety regulations for operating one of your machines?" the system classifies this as a compliance-related topic and retrieves the most relevant safety documentation from company records. Alternatively, if another user asks, "How do I schedule a maintenance check for my equipment?" the engine classifies it under service requests and directs them to the appropriate scheduling workflow. By automatically categorizing requests and routing them accordingly, the Atlas Reasoning Engine ensures that users get precise, relevant answers without unnecessary delays.

Automated Customer Queries With Natural Language Processing

Agentforce uses advanced natural language processing (NLP) to understand, interpret, and respond to customer queries with accuracy and efficiency. This means that customers can interact with the system as if they were speaking to a human, making interactions more intuitive and seamless. By leveraging an extensive network of partner skills available through the AppExchange, businesses can extend Agentforce's capabilities with custom topics and actions. These enhancements enable companies to integrate specialized functionalities, such as AI Employee Service Agents connected to Workday, or contract management solutions from partners like Asymbl, Docusign, and Neuron 7.

For example, consider a customer reaching out to a support chatbot on an e-commerce site asking, "Can I return my item if it was a gift?" The NLP-powered Agentforce will not only understand the intent behind the question but also retrieve the specific return policy applicable to gifted items from the company's database. If the policy varies based on different product categories, the system may ask a follow-up question like, "Which product are you referring to?" to refine its response. This ability to process complex queries, gather relevant context, and provide tailored responses significantly improves customer satisfaction and operational efficiency.

Moreover, Agentforce's recommendation engine allows businesses to generate new AI agents in seconds simply by using natural language descriptions. The Agent Builder identifies relevant topics and instructions while drawing from an existing library of skills, enabling businesses to deploy digital labor efficiently without needing technical expertise.

Native Integration With Salesforce CRM

Agentforce offers native integration with Salesforce Customer 360, enabling agents to tap into a full spectrum of customer data from Sales, Service, Commerce, and Marketing applications. This integration empowers agents with valuable, real-time insights that help them make informed decisions on the fly. 

Imagine an agent reviewing a customer's past interactions and noticing they frequently purchase eco-friendly products. Using this insight, the agent can quickly offer a tailored promotion for new green products, sending a personalized email to boost sales—all while staying within the workflow. This approach not only saves time but enhances the customer experience, driving better results.

Seamless Integration With Slack

Agentforce 2.0 takes integration to the next level by embedding directly into Slack, enabling employees to collaborate with AI agents without leaving their familiar workspace. Whether they start a conversation with an agent through the Agentforce Hub or mention agents in Slack DMs or channels, users can instantly access digital labor in real-time. For example, imagine a project manager needs to send updates to the team about a new client request. Instead of manually posting updates in each Slack channel, the manager can use Agentforce's prebuilt Slack Actions, like "Create Canvas" or "Message Channel," to automate the process. Additionally, Slack Enterprise Search helps Agentforce pull up relevant chat history or data, making the AI's responses more accurate. This seamless flow not only enhances productivity but also streamlines communication—allowing teams to focus on what truly matters.

Automated AI Autonomous Agents

Agentforce brings a more efficient way to interact with customers by using autonomous AI agents that handle various tasks across sales, marketing, service, and field operations. These agents are designed to follow your specific rules so they can nurture leads, respond to customer inquiries, and assist with everyday operations—without the constant need for human intervention. For example, an AI agent could engage with a new lead, qualify them based on preset criteria, and schedule a meeting—all on its own.

For sales teams, the new Sales Development and Sales Coaching skills allow agents to provide real-time feedback during prospecting calls, helping sales reps improve their performance without the need for constant supervision. In addition to sales, Agentforce supports other areas like marketing campaigns, service scheduling, and field operations, making it easier to manage customer interactions and tasks from one platform.

Agentforce integrates seamlessly with various channels like web and mobile chat, email, SMS, and Slack, so it can assist customers wherever they are. For example, a customer visiting your website could start a conversation with an AI agent about a broken product. The agent would provide troubleshooting steps and best practices, and if the issue requires further assistance, it can smoothly hand off to a human team member. This ensures that customers get the support they need without unnecessary delays.

Real-Time Actionable Insights 

Agentforce leverages Tableau Skills to provide teams with powerful data visualizations and predictive analytics directly within their workflows. The integration of Tableau Topics and Actions unlocks deeper understanding by offering real-time data on agent performance, responses, and even customer sentiment. By using Tableau Semantics, the system ensures that the answers provided are not only accurate but also rich in relevant business context, making the insights actionable for a wide range of users.

For example, imagine a customer service team analyzing customer interactions. With the new Tableau features, they can instantly visualize trends in customer feedback, pinpoint areas where agents may need support, and even predict future customer journey behavior based on current conversations. This level of insight helps improve decision-making, identify opportunities for training, and optimize customer success strategies—all without needing to dive into complex data analysis.

Prebuilt Workflows

Agentforce makes it easier to automate business processes with prebuilt Flows that agents can leverage to streamline tasks. These Flows are designed to help agents execute specific actions, and the outputs are used to guide their responses to customer prompts. In addition, agents can be embedded directly into these Flows, allowing them to handle even more complex tasks autonomously.

For example, imagine a customer service team needs to manage a large volume of support tickets. With Agentforce, an agent could run a Flow that automatically elevates the priority of tickets based on specific criteria—like customer urgency or issue type. Once the ticket is flagged, the agent can respond accordingly, ensuring that high-priority cases get the attention they need without manual intervention. In simple terms, this automation saves time and reduces the chance of human error while still offering personalized experiences.

What to Expect From Agentforce AI

Agentforce AI's ability to augment the work of HR professionals, customer service agents, sales teams, and a whole host of other departments' business functions with digital labor is enormous. The efficiency gains alone are staggering. For example, Indeed anticipates reducing time-to-hire by 50% with Agentforce 2.0, while the CTO of 1-800 Accountant reports that Agentforce is managing up to 90% of their incoming requests. Across all users, customer journey automation is highly successful.

"Since launching in October, Agentforce is now solving 83% of customer queries without a human, has halved the number of issues that require human intervention, and has nearly doubled its average number of weekly conversations." - Salesforce News & Insights

If you want to estimate what your organization's Agentforce ROI would be? This calculator will give you a starting point.

How to Set Up Agentforce in Your Salesforce Ecosystem

If this expanded understanding of what Agentforce is, you might be ready to implement it in your system. However, when it comes to integrating new solutions into your business infrastructure, the implementation process is just as important as the software itself. 

The following four-step guide walks you through the steps, challenges, and best practices when it comes to implementing Agentforce:

Step 1. Set Up The Foundations of Agentforce

First and foremost, it's important to lay the groundwork by activating Salesforce Foundations. This is essentially a prerequisite, ensuring your system is ready to support the advanced capabilities of Agentforce. It's like getting a new version of Salesforce with an updated user interface, which streamlines the setup and optimizes performance.

Once the foundational setup is complete, you can move on to connecting your data sources. This process is straightforward and typically doesn't require much effort. Agentforce needs access to the right data to operate effectively, and Salesforce handles most of the heavy lifting for you. The goal here is to make sure your agents are equipped with all the necessary information to handle tasks and respond to both common questions and complex customer inquiries. From there, you'll use Agent Builder to customize out-of-the-box agents for your specific business needs. You'll define natural language topics, set instructions, and select the actions your agents will perform. Additionally, you'll decide on the communication channels through which your agents will operate, whether that's email, chat, or other channels where your customers are most active.

Step 2. Train Your Sales Team on The Key Features of Salesforce Agentforce

Once the foundations are in place, it's time to educate your sales team on the core features of Agentforce. Your team should understand how Agentforce integrates AI into everyday workflows and the value it brings to customer interactions. In particular, it's imperative that your employees are aware of Agentforce AI offerings like conversation insights, predictive analytics, and AI-powered actions directly within your CRM.

With the conversation insights feature, your team can access a customer conversation history or extract key themes from interactions, saving valuable time. Additionally, the predictive analytics feature can forecast customer behavior, allowing your team to proactively address needs before they arise. Furthermore, Agentforce can take action within the CRM itself, automatically triggering digital labor based on customer interactions. Training your team on these capabilities ensures they understand how to leverage the tool to improve their sales approach, enhance customer service, and increase efficiency.

Step 3. Brainstorm Specific Use Cases for Agentforce AI to Improve Customer Experiences and Automate Repetitive Tasks

Now comes the creative part: brainstorming use cases for Agentforce. The goal is to identify specific tasks that can be automated to streamline operations and enhance customer experiences.

For example, imagine a situation where a chatbot interacts with a customer about their order. With Agentforce, the chatbot can automatically update the order, process a change, or even upsell additional products without requiring a human to step in. Furthermore, AI can handle these tasks in real-time, which reduces the burden on staff and speeds up response times.

Another use case could involve the automation of routine customer service tasks like answering frequently asked questions or providing personalized product recommendations. The key is to identify areas where AI can relieve employees from repetitive, time-consuming tasks, allowing them to focus on more complex issues that require human intervention.

Step 4. Defining and Documenting Complex Processes to Map Out Business Functions

To ensure that your AI agents deliver consistent and accurate results, it's crucial to define and document the processes they will follow. This step involves mapping out the specific steps your Agentforce AI will follow so your agents know exactly what to do in any given situation.

Take the time to clearly document each process, outlining the steps an agent should take when responding to a customer or handling a task. For example, if an agent is tasked with updating an order, you'll need to break down the sequence of actions involved, from verifying customer information to confirming the update. You'll also want to specify the tone and approach the AI should use when engaging with customers. This helps guide the agent in providing responses that match your company's voice and values.

Fine-tuning the AI model is also important during this phase. Essentially, you're teaching the AI how to respond to different scenarios and ensuring the process is followed correctly every time.

By clearly defining the processes and providing detailed instructions, you ensure that your agents act consistently and deliver the results you expect. This will also help improve the overall user experience and make the implementation of Agentforce more effective.

First Things First: What You Need to Know Before Implementing Agentforce

Agentforce is a highly advanced digital assistant powered by Salesforce's AI technology, designed to enhance customer service, sales, and internal operations. Its key feature is its ability to act on natural language input, offering a scalable solution that can create custom AI agents capable of executing complex tasks in real-time.

Beyond simple automation, Agentforce is designed to improve efficiency and enhance the customer experience through features like automated insights, NLP-driven interactions, and seamless integration with platforms like Slack. It not only handles routine tasks but also adapts and learns through agentic loops, which refine its responses over time. This allows businesses to deploy AI agents that can manage everything from troubleshooting customer issues to personalizing marketing outreach, all without constant human intervention.

To learn more about the specific challenges and opportunities an Agentforce implementation offers your organization, reach out to our team of Salesforce solution experts. In the meantime, get ready with this step-by-step guide to software implementation planning.