Artificial Intelligence Agents carry out tasks independently by analyzing and understanding their environment
Not a day goes by in this technological era that we are living in that a new term related to artificial intelligence does not appear: Prompt, neural networks, Language models or as in this case, AI Agents.
In fact, much of the content of this humble blog is about the concept of LAM that promises the Rabbit r1, a kind of AI Agent that can execute actions on websites and apps with our consent but only with voice instructions.
And we are currently in a time when Artificial Intelligence fills the internet blogs - just ask me - and is developing at a dizzying pace and almost exponential.
It is true that the concept of AI Agent is not new, and even if we are not aware of it, it is implemented in many of the programs and applications that we use on a daily basis as assistants —Google Home or Alexa—.
And we are going to focus on explaining in the most understandable way possible what an Ai Agent is, how they work, what they can be applied to and how we can take advantage of them to help us with different tasks.
What are AI Agents?
Let's give a definition. AI Agents —in Spanish Artificial Intelligence Agents— are software programs designed to perceive their environment, process information and make decisions autonomous in order to achieve specific objectives.
This is particularly interesting, since as we see, They have an extraordinarily useful capacity and it is to recognize what surrounds them with the information that comes to them and to be able to act accordingly to execute an action.
Its main characteristic is the ability to act independently, without direct human intervention, to achieve the goals assigned to them. This is essential for another very useful aspect for us: Automation.
Unlike generative AI models that respond to text input, AI Agents operate proactively and autonomously within their environment, so that they can make their own decisions according to their criteria. Isn't it amazing?
Ok, now we have a simple idea of what an Artificial Intelligence Agent is. ¿How about we see some examples I'm sure you already know even if you didn't know it?
Examples of AI Agents
These are some practical examples of AI agents that we use today and I'm sure you know many of them.
Virtual assistants
You've probably heard of Alexa, Siri or Google Assistant. In fact I'm sure you not only know them but interact with them almost daily. Well, they are a perfect example of how we have the AI Agents present closer than we believe.
In this case, the tasks for which they are programmed are:
- Answer questions and queries. They can search for information on the internet to give us an answer through the searches they find.
- Control smart home devices. They can make use of the plugs, light bulbs and other smart devices to which we give them access, turning them on, off or scheduling actions.
- Make purchases online. In the particular case of Alexa, you can make purchases directly from Amazon just by asking it with your voice.
- Schedule appointments and reminders. Another of the most common uses of these AI Agents is precisely that they can save alarms on your mobile phone and schedule events just by asking them.
As you can see, they are very useful situations and I'm sure you have used it on some occasion. Well, all of them are carried out by these AI Agents.
autonomous vehicles
100% autonomous cars are not yet seen in Spain, but many cars They have built-in sensors that assist us on the road thanks to these Agents, and they help us in more complicated situations or if we have made a mistake.
In the United States there are companies like Waymo o Cruise whose cars are completely autonomous and They are piloted by these artificial intelligences without the human being having any interaction other than enjoying the trip.
How do they do that? Very easy.
- They perceive the environment through sensors. They carry cameras and sensors that tell them the position they occupy, the situation of other vehicles and the signs nearby.
- Process information about traffic and road conditions. They are capable of understanding the state of the road they are traveling on and acting accordingly.
- Make decisions for safe driving without human intervention. They can make decisions, as if they were people and decide to take one action or another.
One of the things that caught my attention when I saw one of these vehicles for the first time was that sometimes, if it was the most optimal, They crossed the amber traffic lights as any of us would do if we are around him.
Medical diagnoses and treatments
Although at the user level I will always recommend going to a doctor when faced with artificial intelligenceAt a professional level in the health field, AI agents contribute to:
- Analyze medical images and patient data. By crossing the data available, which can be millions, these agents are able to create a map of all of them, obtaining a lot of information.
- Perform faster and more accurate diagnoses. Thanks to the above, they can detect patterns and help the doctor reach diagnoses that would be more complicated to make by hand.
- Recommend personalized treatment plans. They can also improve care once the disease is diagnosed.
There are many more uses of AI Agents such as the detection of financial fraud, robotics or even customer service —yes, those chatbots that ask you questions when you call technical services or the bank are also AI Agents.
What are the main types of AI agents
We can say that There are different types of artificial intelligence agents depending on the objective we have, and each of them has its own capabilities and applications. The main types are:
Simple reflex agents
They are the most basic agents that operate following rules condition-action predefined. They respond only to the current perception of the environment without considering history or future implications. They are used in simple tasks and controlled environments, such as automatic doors, manufacturing robots, and thermostats.
An example would be a motion sensor to turn on lights automatically when it detects movement in a room. Simple and useful at the same time. And known by everyone.
Model-Based Reflex Agents
In addition to current perceptions, they maintain an internal model of the state of the world that allows them to consider the history of previous perceptions. This allows them to make more sophisticated decisions, like in chess games.
Some of the ones we commonly use are traffic control systems that use a model of current traffic conditions and passes to optimize traffic light times. Hasn't it happened to you that your traffic lights are always red? fault of the AI Agents…
Goal-based agents
These agents They have specific objectives or goals and choose the actions that bring them closest to achieving them. They use planning and reasoning techniques to determine the best sequence of actions. They are applied in complex tasks such as natural language processing and robotics.
You Roomba, for example. A robot vacuum cleaner that aims to clean the entire surface of a flat following an efficient route plan. And that we hope will comply and save us from having to clean ourselves…
Utility-based agents
They make decisions evaluating the expected utility or benefit of possible actions through a utility function. They seek to maximize global utility, according to the different parameters at their disposal to give us the answer.
You are going to travel? Well, the systems flight recommendation The ones that suggest the options that maximize comfort (seats, ladders) and minimize the cost and duration of the trip are AI Agents.
Learning agents
They have the ability to learn and improve your performance with experience using machine learning techniques. They learn from past events and improve for the future, as we should.
An example is the spam filters that you learn to identify new spam patterns as you receive more data from emails marked as spam or non-spam.
Hierarchical agents
Perhaps the most complex. Are systems organized in a hierarchy of high- and low-level agents, where high-level agents decompose complex tasks and delegate them to lower-level agents. They are used in manufacturing processes and other environments with structured tasks.
These AI agents find applications in various fields, from virtual assistants and autonomous vehicles to recommendation systems and customer support, promoting the automation and optimization of tasks.
As you can see, from the simplest to the most complex, AI Agents are everywhere, and we will surely end up having them as faithful allies in the technology that is yet to come.
