What is an AI agent?

AI agents are transforming the way we engage with technology. They’re ushering in a world where digital systems — apps, business platforms, and connected devices — not only follow instructions but can also make simple decisions on their own. In certain situations, these tools can anticipate the needs of people and organizations, suggest actions before they’re requested, streamline workflows, and even spot opportunities for improvement.

In this post, we explore how AI agents work and what their main features are. We’ll also look at their benefits and why they could become essential allies in both business and homes in the years ahead.

What is an AI agent?

AI agents are software entities that use artificial intelligence — including generative AI — to carry out tasks and achieve objectives on behalf of users. Depending on how they’re designed, they can perceive parts of their surroundings, perform actions, and learn from experience by processing various types of information (text, voice, audio, video, images, and code). These capabilities allow them to interact more fully with their environment. The most advanced models can converse, reason, and make decisions in addition to learning and improving over time.

AI agents are implemented across a wide range of applications to perform diverse functions. They can take many forms, from software programs to hardware like machines and drones. One prominent example of an AI agent is the technology behind self-driving cars (vehicles capable of navigating with minimal or no human intervention). Using sensors, radar, lasers, and computer vision systems, these agents perceive their surroundings, interpret signals, and make decisions to reach their destinations safely.

AI agents utilize different methods to interact with their environment, depending on their purpose. For instance, when engaging with people, they might communicate via written text or ask a series of questions to better understand the context. In customer service applications, if a user inquires about their order, the agent gathers data from multiple sources to provide an accurate response. It might first check the order system, then verify tracking with the carrier, and, if needed, factor in external conditions (weather or potential delays).

According to Ethan Mollick, a professor at the Wharton School (University of Pennsylvania), adoption of these software entities is set to accelerate. He says that in five years, AI agents will “be ubiquitous in that you will see them anywhere you’re online. You’re more likely to run into an AI agent than a person.”

How do AI agents work?

AI agents simplify and automate complex processes by following a structured workflow:

• Goal definition. It starts when the AI agent receives an instruction or objective. The entity then plans a series of steps, breaking the goal into smaller, manageable subtasks to achieve an optimal and useful outcome based on the established conditions.
• Information gathering. To complete its tasks, the agent analyzes reliable data. Depending on the application, it may pull information from the web, connect to databases, or even collaborate with other agents or machine learning models to expand its knowledge.
• Task execution. The agent carries out each task, marking it complete before moving to the next. Throughout the process, it constantly checks whether the goal is being met. If additional steps are needed, it generates new tasks, executing them until the desired result is achieved.

Characteristics of AI agents

AI agents combine a variety of techniques and technologies to achieve their goals. They may employ machine learning to identify patterns in datasets, natural language processing (NLP) to understand requests and communicate with users, or analytical methods to extract insights from large databases and Internet of Things (IoT) sensors.

Many also use reinforcement learning, a machine-learning technique similar to human trial-and-error learning. This allows them to mimic human ingenuity and make decisions based on feedback from their environment.

To function effectively, AI agents usually consist of several core elements:

• Sensors or perception mechanisms that collect data from the physical environment.
• Reasoning and decision-making modules that analyze information and determine the most appropriate actions.
• A knowledge base to store data, rules, and past experiences.
• Actuators or interfaces that enable actions in the physical world (robots and other devices) or digital environment (executing requests).

Types of AI agents

Organizations can implement different types of AI agents:

• Simple reactive agents. These respond automatically to specific situations according to predefined instructions. They simply execute programmed actions and don’t learn from experience. A smart thermostat, for example, turns on the heat when the temperature drops below a set threshold or during a scheduled time.
• Model-based reactive agents. These follow preset actions while evaluating potential outcomes before acting. Robot vacuums are a case in point: they detect obstacles, avoid them, and remember which areas they’ve cleaned to avoid redundancy.
• Goal-based agents. These agents analyze options and determine the best path to achieve a specific objective rather than just reacting to the environment. Navigation systems, for instance, evaluate routes considering traffic, distance, and estimated arrival time to recommend the most efficient path.
• Utility-based agents. These strategic decision-makers use reasoning algorithms to compare scenarios and weigh the consequences of each action. Flight search engines are a good example: they analyze hundreds of scheduling and pricing combinations across airlines to find the most optimal and cost-effective option for travelers.
• Learning agents. These include all the capabilities of the previous types but can also learn autonomously. As they interact with their environment, they add new experiences to their knowledge base to optimize performance and adapt to novel situations. E-commerce recommendation systems are a prime example, analyzing user activity and preferences to suggest tailored products and services.

Are ChatGPT and chatbots AI agents?

Yes, but not all in the same way. A basic chatbot — one that only responds to keywords with predefined phrases — functions like a simple reactive agent. It executes automatic replies without understanding context or learning from interactions.

Advanced models like ChatGPT, however, are more sophisticated AI agents. They process natural language, reason over received information, and generate new responses rather than following a script. When integrated with systems that gather data, plan actions, or learn from each interaction, they can operate as learning agents, evolving and improving with use. The latest version of ChatGPT, for example, can act on a user’s behalf, engage with external tools, and coordinate complex tasks. In other words, it functions as a true “agent” capable of responding and taking action.

Benefits of AI agents

Unlike traditional automated tools, AI agents can recognize when they don’t have enough information to make a reliable decision. In those cases, they seek additional or more precise data to enhance the quality of their outcomes.

When implemented effectively, AI agents offer significant advantages for organizations, including:

• Continuous operation. They work around the clock, and cloud-based versions can serve users, employees, or customers from anywhere.
• Task accuracy. These tools automate repetitive processes and consult up-to-date data, requesting additional information when needed.
• Process consistency. They execute procedures in a standardized way, avoiding variations caused by fatigue or differences between individuals performing the same task.
• Cost optimization. AI agents help increase effectiveness, identify inefficiencies, and prevent errors that could lead to financial losses.
• Enhanced customer experience. They provide personalized recommendations, respond faster, and enable new forms of interaction that boost engagement, conversion rates, and brand loyalty. For example, they can check whether refund requests meet the criteria or initiate return processes.

A future driven by AI agents

AI agents are no longer a distant promise. They’re already transforming how we work, learn, and engage with technology. By perceiving their environment, making decisions, and, in some cases, learning from experience, they’re paving the way for a new era of intelligent automation where machines not only execute tasks but also deliver strategic value.

For organizations and users alike, this translates into greater efficiency, better experiences, and fresh opportunities for innovation. From virtual assistants and recommendation systems to autonomous robots and advanced chatbots, AI agents are becoming key allies for optimizing processes, reducing errors, and anticipating needs.