Chatbots vs. AI Agents: What’s the Difference?

Today’s customers expect swift service without compromising on quality. Our research finds that 69% of service reps say balancing customer service speed and quality is difficult — down from 76% in 2022.

Could automated service assistants be easing that pain point? We think so.

Chatbots and AI agents free up customer service reps to focus on more complex cases that require critical thinking and empathy. An added bonus: They’re both available around the clock, so customers can receive support any time of the day or night — even at 3 a.m. in a blizzard.

In this article, we’ll discuss chatbots and autonomous agents — breaking down the differences and helping you choose the right one for your business. 

What you’ll learn:

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Chatbots are software programs designed to mimic human conversation. They provide a seamless and efficient way to help with routine customer service tasks, like answering frequently-asked questions and guiding users through service processes, such as how to update billing information. 

Chabots operate within predefined parameters, responding to specific inputs and following set rules and scripts. This means that chatbots are less prepared to handle complex situations outside of those parameters. It creates a consistent, reliable, and “boxed-in” customer service experience. For some businesses, that’s the goal. (Back to top)

What is an AI agent?

AI agents, like Agentforce Service Agent, represent a massive leap forward in customer service. These agents deliver fully automated and intelligent support. Natural language processing (NLP) powers ‌AI agents’ ability to understand and respond to customers in a conversational manner. This mimics human interaction — and raises the bar on efficiency and personalization in service. It’s no wonder that 83% of decision makers plan to increase investments in AI over the next year.

Let’s look at what an AI agent can do:

  • Complex multitasking — Agents can multitask and manage complex interactions. And unlike chatbots, AI agents can seamlessly handle context switching. If a customer suddenly changes the conversation to a new topic, agents can switch gears to tackle new tasks directed by the customer. 
  • Deeper conversation — While chatbots use customer inputs and existing data from your CRM and knowledge management systems to train a predictive model, AI agents use your data to “ground” prompts sent to a Large Language Model, or LLM, and add greater context to exchanges.
  • More action  — Rather than just following a script, agents are able to decide which action is most appropriate based on a customer’s behavior and then complete actions for that customer, such as adding an order or updating a previous transaction. 

AI agents are already automating routine tasks, coordinating workflows, and facilitating real-time communication for customer service representatives. Our research finds that 81% of representatives believe customers today expect a more personal touch. Agents offer more personalized service to meet these growing expectations. (Back to top)

Comparing chatbots to AI agents for customer service

While AI agents represent the latest in automated customer service, both chatbots and agents offer a range of possibilities to elevate customer service experience and drive business success. It boils down to the specific needs of your company. These are unique tools, each with their own use cases. Let’s take a look at chatbots and AI agents side-by-side in key areas:

Task handling

Chatbots are best suited for straightforward, repetitive tasks. They excel at guiding users through simple linear processes such as resetting passwords.

In contrast, AI agents can manage both simple and complex inquiries. They address nuanced customer issues, offer personalized recommendations, and even anticipate needs based on past behavior and preferences. This adaptability makes AI agents uniquely capable of holding non-linear conversations, delivering personalized service to a whole new level.

Communication methods

Chatbots are often limited to text-based interactions, although some newer versions can handle voice commands. AI agents, however, can communicate through multiple customer service channels, including voice, text, and even visual interfaces. This versatility allows agents to understand a customer’s issues in natural language — on the channel of their choice — to proactively resolve issues.

Investment vs. cost

Chatbots operate on predefined scripts. If your chatbot won’t be using artificial intelligence, like generative AI, the bulk of your costs are paid upfront to build out the conversational design.

AI agents offer more functionality, manage a wider range of tasks, and minimize the need for human intervention. They deliver a more personalized experience. This elevated level of service enhances customer satisfaction and loyalty, ultimately leading to improved business outcomes and a higher return on investment. It might be a bigger investment, but you can expect a bigger impact, too.

Sensitive data and model training

It’s worth considering how data goes into training models for chatbots and agents. A chatbot’s model is trained using existing data, such as on-platform CRM data. An AI agent, on the other hand, uses an LLM to train its model, helping it learn from large amounts of non-platform data.

Some AI agents, like Agentforce, offer a built-in trust layer to keep your data safe by streamlining how LLMs can train models. On occasion, though, customers in regulated industries decide not to take the risk and simply opt for chatbots trained exclusively with platform data.

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The importance of strategy with AI agents

There are practical considerations to address before using agents. It’s not quite as simple as flipping a switch. To take full advantage of their promise, your team will need strategic planning and the appropriate resources, such as high-quality customer data.

Ongoing maintenance and updates, for example, keep AI agents working according to the latest trends and customer preferences. This maximizes their potential and can deliver improved efficiency again and again.  (Back to top)

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Chatbots vs. AI agents: Which is best for your business?

When deciding between a chatbot and an AI agent, consider these key factors:

  • Complexity of tasks — Chatbots are great for handling simple, repetitive tasks and FAQs. AI agents can manage simple and more complex inquiries.
  • Level of personalization — If your business requires a high degree of personalization and understanding of customer context, an agent might be the better choice. Chatbots typically offer less personalization as they rely on predefined scripts.
  • Resource allocation — Chatbots often require more upfront investment to build out logic and dialogue trees, but savings can follow if they don’t lean on AI. Agents require less upfront costs with the LLM doing the heavy lifting, but the data and integration requirements that enable the AI agent to take action are higher.
  • Customer expectations — Do your customers value quick, simple interactions? If so, a chatbot might suffice. If they expect more in-depth, personalized support, an AI agent could be more suitable.
  • Scalability and flexibility — Consider the scalability and flexibility of chatbots and AI agents. Both can handle a large volume of inquiries, but agents can adapt to a wider range of situations and even learn from interactions to improve over time.
  • Long-term strategy — If you anticipate needing more advanced customer service capabilities in the future, investing in an AI agent now might be a better strategic move. 

Carefully consider your specific needs. Now that you know the difference between chatbots and AI agents, your business can make an informed decision that balances immediate needs with long-term goals. Then, you’ll be one step closer to improving customer service and driving better business outcomes.

There’s a reason companies like Salesforce offer both chatbots and AI agents. It’s because every business is different and automated solutions aren’t one-size-fits-all. Each has different purposes and can even coexist to provide a more comprehensive and efficient user experience. (Back to top)

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