7 reasons to change your chatbot for an AI Agent

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Traditional chatbots have long been used to automate responses and handle basic tasks in areas such as sales and customer service. Most of us have interacted with them via live chat, WhatsApp, SMS or even via the phone in the form of an IVR (Interactive Voice Response) a.k.a. a phone menu.

These chatbots rely on rule-based programming, where an input produces the same output every time. As an example, if you call a company with an IVR system in place, you’ll be presented with a set of options, and every time you press an option you’ll get routed to the same place. In contrast, AI Agents leverage LLMs like OpenAI’s GPT4o or Google’s Gemini to power interactions. These systems have the ability to understand queries, reason and take action, just like a human agent would. Furthermore, the most advanced AI Agents leverage Multi-Agent Systems where specialized sub-agents handle specialized tasks like changing a shipping address or retrieving an order status.

So what’s the difference between a chatbot and an AI Agent?

Chatbots rely on pre-written conversation scripts that must be manually created, whereas AI Agents leverage generative AI, large language models (LLMs), and natural language processing (NLP) to comprehend, respond to, and act upon customer inquiries. Essentially, chatbots repeat set information, while AI Agents can analyze and reason through interactions. Because of their nature, chatbots can only resolve a common set of predefined scenarios and can’t handle unexpected or unstructured input. This causes frustration on customers that frequently receive unnatural answers or error messages. An AI Agent can handle any type of input, it can assess the urgency of the customer inquiry, reason and take immediate action. Here are 7 reasons why you should consider changing your chatbot for an AI Agent.

1. AI Agents can handle edge cases gracefully

Let’s take a very simple example of an e-commerce business that ships products to customers. This business wants to deploy an automated customer service system that can help customers locate their orders without human interaction. The business might choose a traditional chatbot, which at first sight might get the job done. The interaction will start with a menu or pre-programmed options, including “Locate my order”. Once selected the chatbot will request an order number, and if the input is correct it will output the order’s location. Simple right? Not so fast. What if the customer can’t find their order number? They are stuck. The chatbot won’t understand the input and will ask for a valid order number, and the customer will get frustrated and want to speak with a human.

This interaction can be handled much more gracefully by an AI Agent. In this case the chat session can start as an open-ended conversation. The customer can simply make their requests in natural language, e.g.: “Where is my order?” or “Has my order shipped?”, and the AI agent will understand the intent and answer accordingly “Sure! Let me help you with that, can you please provide me with your order number”. If the customer then says “I don't know where to find that”, the AI Agent can tap into their knowledge-base and training to quickly guide the customer in locating their order number. Once the number is located and provided to the AI Agent, they can proceed with locating the package.

2. AI Agents provide a personalized customer experience

Let’s continue by using the previous interaction. Once the AI Agent locates the order and is ready to respond to the customer, it has also retrieved additional information like the customers’ name. So when the AI Agent generates a response, it can respond in a much more personalized manner by stating the customers first name, like: “Jessy, I’ve located your package. It will be delivered tomorrow morning between 9AM and 12PM. Here is your tracking number: 200290229847. You can use this link to track your order.”

Once the customer is identified, AI Agents can leverage memory to remember past interactions, customer preferences and much more.

3. AI Agents can automate post-interaction work

Now, imagine the store is out of stock for an item Jessy really wants. The AI Agent can tell Jessy they will notify them when the item is back in stock. The AI Agent proceeds to create an SMS notification and pings the e-commerce team via Slack that Jessy has requested an out-of-stock item. Once the item is replenished, Jessy will receive the SMS notification, but with a little caveat. Jessy can now respond in natural language and speak to the AI Agent in plain english. For example:

AI Agent: “Hi Jessy! The new Bosch KitchenAid is back in stock. Do you want to order one?”

Jessy: “Great! What’s the price? I can’t remember if it’s $399 or $499”

AI Agent: “It’s $399.”

Jessy: “Cool, let’s do it.”

‍AI Agent: “Awesome Jessy. Should we ship it to your “Home” shipping address? And should we charge your payment method on file?”.

‍Jessy: “Yes”

AI Agent: “Great! I’ve processed your payment. Your order number is 18928099, and you can use this tracking link to track your order. Let me know if you have any questions.”

4. AI Agents can handle text, voice and vision input (multi-modality)

AI Agents go beyond just text communications, they can process both audio and video input. For example, a customer might upload a photo of a product they are interested in. The AI Agent can analyze the image to determine if the product is in stock or recommend similar products. Here is an example of a case where a text interaction with visual support could make sense:

Customer: “I need help finding a laptop with these specifications.” (Customer uploads a photo of a list of specifications.)

AI Agent: “I’ve analyzed the specifications you provided and found a few laptops that match. Here are your options:” (Lists laptops with similar specs, including links to product pages.)

With the introduction of GPT4o and its native voice capabilities, Voice Agents are about to burst into the scene. OpenAI introduced some key elements that make this a major breakthrough:

• Reduced latency: This is crucial to make the conversation fluid and natural. Current state of the art AI solutions average 800ms to 1 second in latency. GPT4o averages 320ms, good enough to mimic human to human conversations. • Emotion: GPT-4o introduces emotional intelligence to voice interactions, allowing AI to not only understand but also convey emotions. This advancement enables the AI to detect emotional cues from users and respond in a more human-like manner, with varying tones of voice that can express excitement, calmness, or empathy. This capability enhances user engagement and satisfaction, creating more natural and effective interactions. By recognizing and responding to emotions, AI Agents can provide a more personalized and impactful customer experience, setting a new standard for AI-human communication

5. AI Agents work in every language

Another crucial aspect of AI Agents is their ability to handle almost any language seamlessly. Impressively, you can train and instruct AI Agents in one language, and they’ll respond in whatever language the user prefers, whether via text or voice.

When building a chatbot or an IVR system, multi-language management introduces massive complexity. For chatbots, each language requires a separate instance, necessitating multiple updates for each new change, which can quickly become burdensome and slow operations. For IVR systems, managing multiple languages means adding layers of options (e.g., “Press 1 for English, presione 2 para español”) and rebuilding the entire system for each language. This not only increases operational complexity but also degrades the user experience.

In contrast, voice-powered AI Agents allow customers to speak freely in their preferred language, eliminating the need for long, recursive menus. Furthermore, AI Agents can handle more complex tasks in any language, just as efficiently as text-based AI Agents, providing a smoother and more intuitive experience for users.

6. AI Agents are easier to program & maintain

An interesting aspect is that AI Agents are programmed / instructed in natural language through prompts. Obviously advanced capabilities can be achieved via integrations and that requires software development skills and expertise. What this means is that anyone with basic skills can build an AI Agent that responds to frequently asked questions and also can execute automations leveraging pre-built integrations by the platform provider, e.g.: query Salesforce to retrieve data from an object’s field like the contact’s date of birth to send a happy birthday message plus a gift card! How cool is that?

As shown in point 5, native multi-language management greatly reduces complexity both in initial setup and maintenance. This extends to how AI Agents handle intent, entities and more. Imagine having to program every keyword for routing, to store variables, etc. That’s how traditional chatbots and IVR systems work, and it’s a nightmare.

One thing that might not be obvious to most people (yet) is that LLM’s and AI Agents are a new type of software, and they represent a paradigm shift in how software works. Traditional software is deterministic and rule-based, an input will reliably produce an output, for example.: Pressing 1 for english in a phone menu should route you to the english sub-menu. LLMs (Large Language Models) and AI Agents are predictive software.

However, AI, and AI Agents in particular, operate on fundamentally different principles. Unlike traditional software, AI systems, especially those powered by Large Language Models (LLMs), are non-deterministic. This means they can produce different results even with small changes in input or system configuration (e.g., base model, temperature settings, or prompt variations). Given that AI Agents typically communicate through natural language, whether text or voice, the range of potential inputs and interactions is virtually infinite.

For this reason new platforms are required to deploy AI Agents. Vendors sprinkling legacy platforms with an AI integration to GPT won’t get the job done. More on that here.

7. AI Agents are getting better at an exponential rate

Finally, something that never happened in the history of software is the ability to get an upgrade that makes your solution 2x or 3x better than before, through ONE line of code.

Well, GPT4 represents a 10x improvement compared to GPT3.5. Traditional SaaS vendors would take months or even years at a time to deliver meaningful updates to their customers (maybe 10% better product?). AI Agents are powered by foundational models like GPT4o, and with every OpenAI release your AI Agent will get exponentially better through faster inference, better reasoning, larger context windows and much more. Innovation in the AI space is happening in every layer of the stack, from core infrastructure like chips (see Groq), to new powerful foundational models all the way to the application layer.

Closing thoughts

AI Agents are not just an upgrade from traditional chatbots; they represent a transformative leap forward. With the ability to handle complex queries, provide personalized experiences, automate post-interaction tasks, and operate seamlessly in multiple languages, AI Agents are set to revolutionize the way businesses engage with their customers. The continuous improvements in AI capabilities, driven by substantial investments in innovation, mean that AI Agents will only become more effective over time. Companies that adopt AI Agents now will benefit from this accelerating progress, gaining a competitive edge through enhanced efficiency and customer satisfaction. Embracing AI Agents is not merely about keeping up with the latest technology; it’s about positioning your business for long-term success.