AI Agents vs. Chatbots | The Ultimate Guide

Is an AI agent a relevant business concept or an idea that belongs in a sci-fi B-movie? The fact is that AI agents are already here, and they will only become more capable and more applicable in the coming years. By learning about AI agents — including their structure, applications, benefits, and risks — you’ll have a leg up as leading businesses increasingly use them.

Defining AI Agents

An artificial intelligence (AI) is a software system that can mimic human reasoning and capabilities using sophisticated decision-making algorithms. People interact with AI systems on a daily basis, from chatbots to email filtering applications. Assuming you already have a decent grasp on AI systems in general, what separates an ordinary AI system from an AI agent?

While no hard-and-fast rule governs this distinction, AI agents generally demonstrate the following characteristics to a greater degree than ordinary AI systems:

• The ability to interact with many external tools and systems
• The ability to plan longer-term sequences of actions to accomplish goals
• Memory capacity for storing data about past actions and interactions

 Each of these is discussed further below.

FAQ:What Do AI Agents Do?

AI agents plan multistep actions to accomplish user goals. They do this by interacting with external tools and systems and drawing on a memory of their past actions and user interactions.

Some tasks performed by AI agents include: 

• Organizing meetings
• Providing customer support
• Performing data entry
• Navigating vehicles

Characteristics of AI Agents

Let’s further explore the defining characteristics of AI agents.

Interaction With External Systems

AI agents interact with external tools and systems during their reasoning processes and actions. For example, an AI agent might use a search tool like Google Maps to find geographical data while processing user input and then use an external calendar app to schedule an event for the user. 

By contrast, non-agentive AI systems generally only use their own internal algorithms to process inputs and can only act by producing an output within their own front-end interface, such as a textbox (in the case of a natural language processing model). For instance, email filtering systems in common email applications like Gmail use AI algorithms to sort emails in your inbox. However, they do not interact with tools outside the email application.

Long-Term Planning

AI agents can plan a series of extended actions to accomplish user goals. Suppose you wanted to organize a meeting sometime next week. An AI agent could do this by taking the following actions: 

1. Access your calendar to determine when you’re available
2. Send out emails to those invited requesting their availability
3. Determine the best time for the meeting based on the responses
4. Send notifications to the attendees about the chosen time
5. Add the event to your calendar

By contrast, a non-agentive system takes inputs and produces a response in moments but cannot plan out complicated sequences of actions like an AI agent can. An email filter, for example, sorts emails into categories in seconds but cannot craft and execute a detailed strategy for optimizing your inbox.

Memory

An AI agent stores past user requests and actions. By learning from past experiences, AI agents can interact more effectively with external systems and better satisfy user requests.

For instance, an AI agent with email access might remember that a user sets a notification each time they receive an email from a specific sender. Drawing on this data, the agent might offer to automatically set a notification each time that email is received. To do this effectively, the AI agent might draw on past instances when it interacted with the notification application, taking note of past errors it has encountered.

Non-agentive AI systems tend to have very limited memory and often do not store past user interactions.

Types of AI Agents

AI agents are distinguished by their degree of complexity. The following are four commonly defined kinds of AI agents, from least complex to most complex.

Simple Reflex Agents

These agents possess more basic planning abilities and more limited memory forms than other AI agents. A simple reflex agent takes in user input, performs limited input processing, and determines an action based on simple rules. A simple reflex agent might be able to send emails in response to a user request, for example, but would not be able to plan out a series of actions that require complex reasoning, like organizing a meeting.

Model-Based Reflex Agents

What differentiates model-based agents from simple reflex agents is the possession of a world model. This means model-based agents have realistic information about how things generally work, enabling them to tackle more complicated tasks. 

Organizing a meeting, for example, requires significant knowledge about how the world works, like the fact that people can’t attend two events in different places at the same time. While basic, an agent without a world model would lack this knowledge and would, therefore, be less effective.

Goal-Based Agents

Goal-based agents are supplemented with broader goals that describe desirable outcomes. A goal-based agent can accomplish complicated task sequences, but only those that achieve the same result. A goal-based agent may also understand both what it is instructed to do and why it is instructed to do it. 

Thus, if a goal-based agent is instructed to organize a meeting to discuss testing results, but it also learns that the testing is delayed, it may suggest delaying the meeting because it can reason about the goals behind the action of organizing the meeting.

Multi-Agent Systems

Multi-agent systems are comprised of several different AI agents acting together. Each agent specializes in a specific part of a task and communicates with other agents to collaboratively complete the overarching task. 

For instance, a multi-agent system may consist of a central agent that interprets inputs and sends commands to other agents that are responsible for interacting with different external tools. A multi-agent system that organizes meetings could contain individual agents interacting with calendar and email applications. These agents would take commands from the central agent, send back information from the email and calendar systems, and then take further commands to send emails and set calendar appointments.

FAQ: Is ChatGPT an AI Agent?

While ChatGPT is a sophisticated AI system, it is generally not considered an AI agent. ChatGPT only interacts with you through its own text interface. It cannot, for example, interact with your email application or word processor. 

Furthermore, ChatGPT cannot do long-term planning. As a chatbot, it answers text-based prompts in a few seconds and cannot carry out complicated sequences of actions over an extended period. Even though ChatGPT can now store data about past conversations, it is not considered an AI agent due to the lack of these capacities.

Structure of AI Agents

While the internal structure of AI agents can vary, the following are high-level components found in nearly all AI agents:

• Input systems or sensors: These components take in data from external systems. These can include cameras and microphones, especially on robotic AI agents — like autonomous cars or vacuum cleaners — or text input forms on purely digital AI agents.
• Output systems or actuators: These components send data to external systems. They can include electrical components that send signals to wheels, robotic arms, or software components that create and send files to external systems.
• Central processing systems: These components process the data from sensors to plan courses of action and send instructions to actuators. The exact structure of the central processing system can vary significantly between agents, but they often use large language models (LLMs) to understand and reason about human inputs.
• Database systems: These components store data about the external world, past interactions, and other relevant information. They constitute an AI agent’s memory.

Applications of AI Agents

AI agents already exist in a wide variety of everyday contexts. Here are some of the areas where they are being applied.

Virtual Assistants

Most of us have used virtual assistants like Siri or Google Assistant. These applications can receive data in forms including video, audio, and text to answer user queries and interact with external devices like TVs, thermostats, and cars.

As AI agents become more sophisticated, they will be able to perform basic tasks like changing the thermostat temperature and complex tasks like optimizing the thermostat schedule for changing weather patterns and home resident behaviors.

Autonomous Vehicles

Autonomous vehicles, including self-driving cars, buses, ships, planes, and drones, navigate without human intervention. They require the ability to understand a general goal, like “Navigate home,” and plan out a series of steps to accomplish that goal while responding to and interacting with systems inside and outside the vehicle. Most modern cars have some form of autonomous driving, and some, like the Jaguar I-Pace, can even drive fully autonomously.

Business Process Automation

AI agents can be used to fully or partially automate many business processes. For instance, Beam AI advertises autonomous agents that perform order processing, data extraction, and data entry, while AptEdge’s Product Support Copilot can partially automate customer support and provide business-to-business (B2B) technical support. If AI technology continues to improve at its current pace, nearly all business processes will be subject to some degree of automation.

FAQ: What Are Real-Life Examples of AI Agents?

Real-life examples of AI agents include virtual assistants like Siri, autonomous vehicles like the Jaguar I-Pace, and business process automation tools like AptEdge’s Product Support Copilot.

Benefits of AI Agents

Compared to manual performance of tasks, AI agents have the following benefits:

• Faster performance: AI agents process data and take actions faster than any human can, leading to quicker performance time on tasks like writing, data entry, data analysis, and customer support. This lets you boost productivity with AI agents.
• Higher consistency: While AI systems certainly make mistakes, they tend to be more consistent in performing routine tasks. Even in complex tasks like vehicle navigation, a lot of data indicates that AI agents make fewer mistakes than human drivers in real-life conditions.
• Lower costs: Automation generally drives down costs, and it is likely that automation through AI agents will be no exception.

Risks and Ethical Considerations of AI Agents

Society continues to grapple with the rise of powerful AI technologies. Here are some business risks and ethical considerations arising from using AI agents.

Public Accountability

Adverse incidents involving AI agents tend to receive outsized public blowback compared to incidents involving humans. For example, despite evidence favoring the superior safety of autonomous vehicles, crashes involving autonomous vehicles have received significant negative press. In one incident, Waymo, a self-driving taxi company, faced protests in February 2024 after a series of accidents.

Transparency

It can be challenging to explain why AI systems, including AI agents, make certain decisions. This is sometimes called the AI “black box.” The AI black box is an issue when users want and deserve explanations of AI agent decisions. 

For example, users may be frustrated if they cannot understand the reasoning behind an AI support agent’s message. This can be a compelling reason to use an AI agent that keeps a human in the loop, such as the AptEdge Product Support Copilot. Copilot also shares the sources it uses to compose an answer, further increasing transparency.

Job Displacement

As AI agents perform tasks previously done by people, job displacement may become an issue. It remains to be seen whether AI agents will complement or replace human workers and whether AI technology will lead to new kinds of work that will replace automated jobs.

The Future of AI Agents

No one knows the precise future of AI development or the future of customer support. Perhaps AI agents will automate all work while humans enjoy significantly more leisure time. While this future unfolds, consider using intelligent agents that complement human workers to resolve human problems.