Salesforce AI agents vs. chatbots: what’s the difference?

It’s no secret that the biggest buzzword floating around the Salesforce ecosystem right now is Artificial Intelligence (AI). With everything from Einstein chatbots to Agentforce, understanding the nuances of all this new generative AI technology can be overwhelming. So, let’s break it down. In this article, we’ll take you through some of the key differences between the capabilities, use cases, and benefits of chatbots and AI agents.

Why is this topic important right now?

With teams under increasing pressure to deliver more complex and tailored solutions, AI is fast becoming a daily necessity to help enhance platform capabilities and manage team workloads. So, whether you’re currently a Salesforce admin, developer, architect, manager — or even an aspiring Salesforce professional — understanding the nuances of Salesforce’s AI features, and the AI agent vs. chatbot debate, is invaluable. With that, let’s get started with some key definitions.

What is a chatbot?

A chatbot is a computer program that follows a set of pre-determined rules, which enable it to perform simple, routine tasks, such as answering common questions using a pre-set script. For example, if you were trying to find out how to return an item of clothing to a retailer, a chatbot could use its pre-set script to advise you on the business’ returns policy. Although chatbots (e.g. Einstein bots) are designed to mimic a human conversation, they are limited by the instructions they’re given and, therefore, are unable to adapt their automated responses to real-time customer inputs — nor can they learn from their interactions.

What is an AI agent?

AI agents are more sophisticated than chatbots — using machine learning (ML) to analyze a situation in depth, adapt, and make decisions based on information gathered in real-time. AI agents can learn from their own experiences, remember how a conversation or scenario played out, and polish their own responses to achieve a better outcome. For example, if you repeatedly sent complaints to a company via their AI agent about a faulty product, the agent could identify this as a pattern and adapt its usual response — without human intervention — to bypass the standard complaints protocol and escalate your complaint to a manager.

In the Salesforce ecosystem there are different types of agents a business could use:

Einstein Copilot

The purpose of Einstein Copilot is to automate a Salesforce user’s daily workflow — to write emails, update records, and recommend next steps within the sales cycle. It works by:

Integrating with your Salesforce data (customer records, activity, and sales data)

Using natural language processing (NLP) to read and interpret unstructured data, including text from emails, social media posts, and customer feedback forms

Leveraging LLMs (large language models) to produce an appropriate response or recommendation based on the interpreted data

Salesforce Agentforce

Agentforce is the latest evolution of AI agents within the Salesforce ecosystem. The Agentforce platform enables Salesforce users to design and deploy their own customized agents to carry out autonomous operations such as personalizing complex interactions, predicting and prescribing customer service insights, and automating various sales and campaign management tasks. Examples of Agentforce AI agents include:

Service Agents: troubleshoot queries and provide tailored customer support
SDR Agents: focus on engagement, qualifying leads, and scheduling appointments within the sales pipeline
Sales Coaches: offer advice to sales representatives to improve sale outcomes
Merchants, Buyers, and Personal Shoppers: help with product discovery, purchases, and personalizing the customer shopping experience
Campaign Optimizers: coordinate campaign lifecycles

Agentforce harnesses AI intelligence like the Atlas Reasoning Engine, enabling its AI agents to comprehend the context of a sales scenario, reason and form decisions based on the data they have collected, and independently act on those insights to provide support for Salesforce users. Ultimately, this empowers users and businesses to automate manual or laborious tasks and improve the overall experience for customers or prospects and drive business success.

The history and evolution of AI agents and chatbots

Over the years, Salesforce has gradually developed its CRM’s AI features, empowering businesses to optimize their daily workflows and meet evolving customer expectations. Let’s take a look at this evolution to see how Salesforce transitioned from reactive tools like traditional chatbots, to proactive, autonomous AI agents.

Einstein GPT (2023)

Salesforce’s first major step into generative AI, Einstein GPT, focused on automating repetitive tasks. It allowed users to generate personalized emails, create tailored marketing copy, and even provide meeting transcripts. However, it was primarily reactive — responding to user prompts and suggestions. This was a powerful time-saver, but it relied heavily on user inputs to get started.

Einstein Copilot (2024)

The next leap came with Einstein Copilot, which added real-time assistance. Copilot could offer out-of-the-box actions, guide users through workflows, and tailor recommendations using real-time data from Salesforce’s Data Cloud. It also introduced the Einstein Trust Layer, which added robust security measures like data masking and compliance safeguards. Despite its capabilities, Copilot was still more of a smart assistant, requiring users to initiate actions.

Agentforce (2024)

Salesforce unveiled Agentforce at Dreamforce. Unlike their predecessors, Agentforce agents are fully autonomous. They don’t wait for instructions; they take action, make decisions, and adapt to changing conversations or workflows. This marks a shift from predictive and generative AI to agentic AI — systems that can act independently to achieve goals – an entirely new tool for business and AI-driven automation.

The development of Agentforce highlights a shift from tools that support human decisions to tools that actively drive them. By moving from reactive to proactive capabilities, Agentforce addresses the growing need for speed, accuracy, and adaptability in business processes. It’s no longer about assisting humans with tasks — it’s about augmenting them with a digital workforce that can handle high-volume, high-complexity workloads.

Communication methods of AI agents and chatbots

Both AI agents and chatbots are built to communicate with Salesforce users through an array of different channels:

Website chats to assist website visitors
In-app support via mobile apps
Messaging platforms such as WhatsApp, Facebook Messenger, and Slack
Social media monitoring to catch mentions of a business and respond to comments
Email automation for ticket management and responses
Voice channels or interactive voice response (IVR) support via humanlike, voiced conversations
SMS for efficient text-based interactions

A consistent and efficient level of service across all customer touchpoints is achieved by Salesforce’s omnichannel AI integrations. Managed in Salesforce Service Cloud, users can deploy Agentforce agents and Einstein bots throughout supported channels to automate new tasks and deal with customer interactions. But the real power of this omnichannel approach is that customers can be moved between channels to the most appropriate channel or agent (Omnichannel Routing) without having to repeat their query or information — making the customer experience seamless.

How AI agents and chatbots handle sensitive data

As with allowing any platform direct access to your data, the integration between Salesforce’s AI tools and your customer data could raise concerns about data breaches or security issues. However, Salesforce’s entire AI strategy is secured by the Einstein Trust Layer. This is a set of predefined parameters and security guidelines built into Salesforce to ensure data integrity, security, and privacy. The Trust Layer is upheld by the generative AI features in the Salesforce platform — among other things, this includes data masking and anonymizing any sensitive or personally identifiable information (PII).

However, it’s always best practice to build a data governance framework into your Salesforce development lifecycle. This ensures that AI models are trained on accurate and compliant data, minimizing risks and maintaining customer trust. DevSecOps practices like regular audits and monitoring are also crucial for maintaining data integrity and compliance when it comes to using AI.

How AI agents and chatbots benefit from model training

It's also important to note that the effectiveness of an AI agent heavily relies on its model training — the quality of data used to train an agent. Feeding an agent large amounts of training data enables it to learn patterns and understand context, in order to adapt to different scenarios and execute more complex tasks effectively.

While traditional bots use predefined scripts rather than machine learning (ML) models, modern chatbots, such as Einstein bots, also benefit from model training — particularly when using Natural Language Understanding (NLU). Even when a chatbot has a script, model training and NLU can assist a chatbot in understanding user intent by:

Extracting relevant information such as date, locations, and product names
Learning how to talk to customers to achieve positive outcomes
Improving a chatbot’s accuracy by using a mistake to re-train it

Model training is important for both AI agents and chatbots alike. Well-trained agents or bots are key to enhancing customer satisfaction with more accurate, efficient, and natural customer interactions.

Evaluating the cost and ROI of AI agents and chatbots

Understanding the financial implications of AI is just as vital as understanding what it is and how it works. When evaluating the cost of AI agents or chatbots, it's crucial to consider a range of factors. Implementing basic chatbots is generally less costly and time-consuming than developing AI agents, due to their simple scripts. AI agents tend to come with higher upfront costs as they require more input to build, customize, and integrate their more complex AI capabilities. The cost of ongoing training and maintenance is also similar, with AI agents demanding extra model training and monitoring resources. Depending on the complexity of your current Salesforce setup, and to what level you wish to integrate the various AI channels, considering infrastructure costs, such as Cloud hosting, data storage, and Salesforce platform costs is important — as is the cost of users managing the AI. For example, AI agents may require more initial oversight than chatbots, and extra resources may be needed to manage the multiple channels in Salesforce Service Cloud.

Similarly, measuring the return on investment (ROI) of AI agents and chatbots can be tricky, but there are some key areas and metrics that can help shed some light. Reduced customer service costs, more sales and lead generation, and increased customer satisfaction are all good indicators of the ROI of Salesforce’s AI tools. But to increase revenue and boost ROI, it can be beneficial to rollout AI integrations gradually and then continuously improve and optimize AI-powered workflows as you scale the integration and leverage more of Salesforce’s AI capabilities.

Salesforce offers a low-code agent builder (Prompt Builder) to help users more efficiently build successful bots and agents. Tools like this are useful when getting started with AI agents as they can help to reduce initial development time and achieve a more seamless integration.

Choosing the right AI solution

Selecting which of Salesforce’s AI tools you implement will depend on what your business needs. For example, if your sales team deals with simple, repetitive tasks, chatbots like Einstein bots are likely to be an effective solution for you. But for anything more complex, autonomous agents will probably be more beneficial for your team in the long term. It’s important to assess what your team wants to achieve with AI, what your organization’s business goals are, and if there are any pain points AI can help your team to overcome.

If your aim is to enhance operational efficiency and the productivity of your team, Einstein Copilot is a good choice. If you want AI to execute tasks with little to no human intervention, Agentforce agents are the way to go. Another point to consider is scalability. If your team currently only deals with repetitive FAQs, Einstein bots will be sufficient. But as your business and customer interactions grow in complexity, so may the need for more advanced AI solutions, such as Agentforce AI agents.

By evaluating the right solution at the start of your AI implementation, your team can make informed decisions on AI tools that maximize future business benefits.

Take your Salesforce AI knowledge to the next level

Ready to leverage the power of Salesforce AI? Take a deep dive into Salesforce Agentforce and how to successfully implement it into your team’s workflow with the latest DevOps Launchpad course: An Introduction to Agentforce.