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

It’s very common to confuse AI agents with chatbots. While both can chat, AI agents open up a range of possibilities that traditional chatbots do not allow.

In today’s article, we’ll explore the differences between them and why AI agents are revolutionizing the market.

What is a Chatbot?

A chatbot is a program designed to simulate a conversation with a human user through different channels. In recent years, their popularity has grown in companies because they automate the handling of demand across various channels. However, they have some limitations:

1. Rule-based: They operate through a predefined script or “decision tree.” They follow a structure designed by a human, which doesn’t always cover all possible situations in a real conversation.
2. Generate frustration: Users know they are interacting with a bot, and often these bots don’t fully understand their needs due to the limitations mentioned. This can lead to a poor experience.
3. Difficulty extracting insights: Since they don’t fully understand the context of conversations, it’s challenging to obtain valuable insights. This requires a human to manually analyze the interactions to identify patterns or common needs.
   

These limitations can result in unsatisfactory user experiences and complicate the work of those who optimize and supervise these systems.

What is an AI Agent?

An AI agent is a program capable of interacting with its environment, gathering data, and using it to perform tasks to achieve specific goals. Unlike chatbots, AI agents have the following characteristics:

1. Goal-oriented: Humans define the objectives, and the agent interacts with the environment to gather information and perform tasks that meet those objectives.
2. Versatile interaction: They can communicate via text, voice, and video, and are not limited to a chat. Additionally, they can integrate with various APIs to execute more complex workflows.
3. Natural conversation: By effectively simulating a human conversation, they can follow dialogues seamlessly and avoid frustrations. They maintain the context of the conversation to provide coherent responses.
4. Thinking units: They can function as components of a broader process, where multiple agents interact with each other. This allows for breaking down complex tasks into micro-tasks that each agent can handle efficiently.
5. Learning capability: They can learn from a company’s information, read databases, PDFs, and any other relevant resources to achieve their final goal.
6. Provision of insights: An agent dedicated to extracting insights can analyze chatbot conversations, summarize them, and extract valuable information.

Examples

• Chatbot: In retail, it can answer basic questions about opening hours, return policies, or product availability. It follows a script and provides predetermined answers based on the user’s input.
• AI Agent: It can schedule meetings, manage emails, and give personalized content recommendations. For example, a sales agent can qualify a customer in real-time, assign a score, schedule a meeting with a salesperson if the criteria are met, and update the CRM.

Here’s a demo of an AI agent presented by David Grandes, CEO of Patagon AI:

In Summary

AI agents are fundamentally different from chatbots: they act according to defined goals, interact naturally in various formats, learn from the information provided to them, enable insights analysis, and can function as thinking units for complex tasks.

Businesses that understand these differences will thrive, as this technology far surpasses current alternatives.