AI agents are the future of artificial intelligence – and as the top AI trend for 2025, they’re becoming more and more popular as AI technology continues to advance.
AI agents are a broad category, spanning:
- LLM agents that use large language models for conversational AI tasks
- Multi-agent systems that coordinate complex tasks
- Customer support AI chatbots that upsell, cross-sell, and reset passwords
- Smartphone-based AI assistants like Siri and Alexa
So let's dive into the wide world of AI agents and what you can use them for.
What is an AI agent?
An AI agent is a software that performs tasks on behalf of a user. They can automate processes, make decisions, and intelligently interact with their environment.
“AI agents are like magic,” said Patrick Hamelin, software engineer lead at Botpress. “They’re these magical entities that go beyond typical chatbots.”
AI agents are entities designed to perceive their environment and take actions in order to achieve specific goals. These agents can be software-based or physical entities.
They perceive their environment through sensors, process the information using algorithms or models, and then take actions using actuators or other means.
What do AI agents mean for the workforce?
While it’s easy to imagine a world full of autonomous software completing an office building’s worth of tasks, AI agents will assist human employees for the near future – not replace them.
AI agents need human triggers to complete their workflows. While the use of AI will continue to grow across industries – like scaling support with customer service chatbots, or creating lead generation agents within AI sales funnels – AI agents and chatbots aren’t designed to replace human employees.
We’ll likely see an increase in education and training for employees to use artificial intelligence in their workflows, particularly in industries that can easily automate tasks. If done properly, this upskilling will allow workers to increase the amount of time they spend on complex or more strategic tasks. This should improve employee productivity and job satisfaction.
In fact, there are already many real world use cases of AI agents. And they'll only continue to expand as technology becomes more advanced.
But critics are right – the introduction of more autonomous agents to the workforce needs to be done with intention and care towards the humans they’ll work alongside.
What’s the difference between an AI agent and an AI chatbot?
AI agents and chatbots differ in their purpose and capability. Chatbots are designed to interact with humans, while agents are designed to complete autonomous tasks.
The biggest difference is their ability to take autonomous actions. Since AI chatbots are designed for conversation with humans, they’re not usually programmed to take autonomous action – their purpose is to directly assist a human.
AI agents, on the other hand, may not interact with a user at all. In some cases, they’ll receive a task from a developer and follow through on it independently, without interacting with another human.
They also take different forms. Chatbots are often text- or voice-based, while AI agents can take the form of a robotic vacuum cleaner or a smart thermostat.
However, they have many similarities. Among other overlap, they both use:
- Natural language processing to understand text
- A large language model to power their output (like GPT from OpenAI or Gemini from Google)
- Vector databases to better understand textual input from a human interaction
Characteristics of AI agents
Autonomy
AI agents can operate without human intervention, making decisions and acting on them independently. Their autonomy allows AI agents to handle complex tasks and make real-time decisions on how to best complete a process, but without a human coding the specific steps for a given task.
While the idea of an autonomous agent may conjure images of HAL 9000, the talking computer from 2001: A Space Odyssey, AI agents still rely on human instructions. A user or developer will need to spend time telling the agent what to do – but the agent will problem-solve how to best complete the task.
Continuous learning
Feedback is essential for the AI agent's improvement over time. This feedback can come from two sources: a critic or the environment itself.
The critic can be a human operator or another AI system that evaluates the agent's performance. The AI agent’s environment can provide feedback in the form of outcomes resulting from the agent's actions.
This feedback loop allows the agent to adapt, learn from its experiences, and make better decisions in the future. It will learn to create better outcomes as it experiences more tasks. Because of their ability to learn and improve, AI agents can adapt to rapidly changing environments.
Reactive and proactive
AI agents are both reactive and proactive in their environments. Since they take sensory input, they’re able to change the course of action based on changes in the environment.
For example, a smart thermostat can sense the temperature of the room getting colder as an unexpected thunderstorm begins. As a result, it’ll decrease the intensity of the air conditioning.
But it’s also proactive – if the sun shines into a room at approximately the same time each day, it will proactively increase the air conditioning to coincide with the emergence of the sun’s warmth.
Components of an AI Agent
An AI agent seems complicated at first glance. That’s because they are. But a better understanding of the key components of an AI agent can help you grasp its inner workings.
These elements are crucial in order to create AI tools that can perform tasks autonomously.
What is an agent function?
The agent function is the core of an AI agent. It defines how the agent maps the data it has collected to actions.
In other words, the agent function allows the AI to determine what actions it should take based on the information it has gathered. This is where the "intelligence" of the agent resides, as it involves reasoning and selecting actions to achieve its goals.
What are percepts?
Percepts are the sensory inputs that the AI agent receives from its environment. These provide information about the current state of the observable environment in which the agent operates. For example, if the AI agent is a customer service chatbot, percepts can include:
- Messages
- User profile Information
- User location
- Chat history
- Language preferences
- Time and date
- User preferences
- User emotion recognition
What is an actuator?
Actuators are mechanisms that allow AI agents to physically interact with their environment. These actions can range from steering a self-driving car to typing text on a screen.
Actuators can be thought of as the muscles of the AI agent, executing the decisions made by the agent function.
Examples of actuators include:
- Text response generators are responsible for generating and sending text-based responses to users. They take the chatbot's text-based reply and deliver it to the user through a chat interface.
- A chatbot might need to integrate a system – like the company's CRM system – to access customer data, create support tickets, or check the status of orders. Service integration APIs allow the chatbot to interact with external systems and retrieve or update information as needed.
- Actuators can send notifications and alerts, like email notifications, or SMS messages. They can be used to keep users engaged and informed by sending push notifications to alert them about upcoming appointments, order status changes, promotions, or other relevant updates.
What is a knowledge base?
The knowledge base is where the AI agent stores its initial knowledge about the environment. This knowledge is typically pre-defined or learned during training. It serves as the foundation for the agent's decision-making process.
For instance, a self-driving car might have a knowledge base with information about rules of the road and county bylaws. Meanwhile, an autonomous agent for customer service will have access to databases of a company's products and return policies.
Any business using an AI agent will need to train it on company data. While a large language model can make use of the wider internet, an agent with a specific function will need to create an output specific to the user's journey.
Applications of AI Agents
AI agents have a wide array of applications – they’re beginning to make waves across numerous industries around the world. Here are a few of the most common:
Customer Service
Customer service chatbots are one of the most common types of AI agent deployment.
Because they can be plugged into company data, a business can use an AI agent to act as a customer assistant. They can provide access directly to the user’s device anywhere in the world, including a webpage via their computer or different apps (like WhatsApp or Facebook Messenger).
These chatbots and virtual agents can point customers towards specific policies, give them an idea of what items might fulfill their needs, or even provide access to their account by resetting a password.
It’s becoming expected for companies to offer customer service chatbots – most are powered by large language models and can complete specific tasks. The best ones are also able to take action on behalf of a business, like book a table or update a customer’s record.
Autonomous Vehicles
One of the flashiest uses of AI agents are self-driving cars and drones. These vehicles can operate with limited human input, thanks to the power of AI agents.
AI agents are integral to their functioning – they perceive the car’s environment and make informed decisions (like when it’s safe to turn or when to slow down). They can identify when the car is approaching a stop sign or explore a new type of terrain by accounting for environmental inputs.
Virtual Assistants
Agents like Siri, Alexa, and Google Assistant use AI to understand natural language, assist with tasks, provide information, and control smart devices.
The concept of an AI assistant is already familiar to us. AI agents allow for the next step of deeply personalized planning – if you’re planning a vacation, it can not only suggest locations for a new destination and identify hotels, but act as a personal travel agent. Since an AI agent can complete tasks autonomously, a travel bot will only take a moment to book reservations on your behalf, from plane tickets to your hotel.
Other applications
- AI agents can control and optimize smart home devices – like changing the temperature via your heating system or setting up a burglar alarm.
- AI agents are used in robotics, since they can perform autonomous tasks like building. Once given a task, they have the ability to complete it based on their own assessment of best practice.
- Similar to their use in smart home devices, AI agents can be used in cybersecurity. They’re capable of completing actions like threat detection, anomaly identification, and security management, defending against cyber attacks and ensuring system integrity.
- In supply chain processes, AI agents can be used to optimize routes, manage inventory, predict demand, and enhance overall efficiency in logistics operations – they can identify solutions that the humans operating them may not have previously seen.
Types of AI Agents
There are a few different types of AI agents – the optimal one will depend on the task at hand.
Simple Reflex Agents
These agents operate based on a set of predefined condition-action rules. They react to the current percept and do not consider the history of previous percepts.
They’re suitable for tasks with limited complexity and a narrow range of capabilities. An example of a simple reflex agent would be a smart thermostat.
Model-Based Reflex Agents
Model-based agents have a more advanced approach. They maintain an internal model of the environment and make decisions based on their model's understanding. This allows them to handle more complex tasks.
They’re used in the development of self-driving car technology, since they can collect data like the speed of the car, the distance between the car in front of it, and an approaching stop sign. The agent can make informed decisions about when to brake based on the car’s speed and braking capabilities.
Utility-Based Agents
Utility-based agents make decisions by considering the expected utility of each possible action. They are often employed in situations where it's essential to weigh different options and select the one with the highest expected utility. If you want an agent to recommend things – like a course of action or different types of computers for a certain task – a utility-based agent can help.
Learning Agents
These agents are designed to operate in unknown environments. They learn from their experiences and adapt their actions over time. Deep learning and neural networks are often used in the development of learning agents.
They’re often used in e-commerce and streaming platform technology to power personalized recommendation systems, since they learn what users prefer over time.
Belief-Desire-Intention Agents
These agents model human-like behavior by maintaining beliefs about the environment, desires, and intentions. They can reason and plan their actions accordingly, making them suitable for complex systems.
Logic-Based Agents
Logic-based agents use deductive reasoning to make decisions, typically over logic rules. They are well-suited for tasks that require complex logical reasoning.
Goal-Based Agents
These agents act to achieve their goals and can adapt their actions accordingly. They have a more flexible approach to decision-making based on the future consequences of their current actions.
A common application for goal-based agents is robotics – like an agent that navigates a warehouse. It could analyze potential pathways and select the most efficient route to their goal destination.
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The Future of AI Agents
The AI era is only just beginning. And it’s come a long way – from the first computers, to the internet, to the first large language model, to new agent technology, technology expands our world with each passing day.
AI development is set to create a new world of business. Connecting with an AI assistant has already become the norm when interacting with large businesses – as technology advances and agents become more capable of completing various tasks independently, they’ll expand in scope across industries.
Create an AI Agent with Botpress
Botpress is a next-generation AI chatbot builder. Because of its highly extensible and customizable design, you can use it to create AI agents.
You can jumpstart your project with pre-built templates, customize its behavior, and seamlessly deploy it across multiple channels.
Whether you're building a personal assistant, a customer service chatbot, or another AI agent, Botpress provides you with the tools you need to get started.
Do you want to create an AI agent? Start building today. It’s free.
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