While AI agents have made tech headlines around the world, real-world examples of AI agents aren’t always as obvious. In this article, I’ll walk you through the different types of AI agents and examples. But keep in mind: despite the type, most advanced AI systems are a combination of multiple types of AI agents.
For example, self-driving cars – or autonomous vehicles – involve utility-based agents, goal-based agents, model-based reflex agents, and learning agents. It’s a complex process, requiring many moving parts. And it’s the same premise for AI agents in supply chain management. They’ll use multiple types of agents to optimize logistics, inventory management, stock, and shipping.
But to make it easier, let’s dive into what each type of AI agent is trying to achieve, with some examples of how it’s already happening in the real world. For example, AI agents are increasingly being used as enterprise bots for tasks that were previously impossible to automate. Flexible AI building platforms mean the use cases are endless.
Ecommerce AI agents are used to place orders, track and provide shipping updates, facilitate image-based searches, send follow-ups on abandoned carts, provide product reviews from previous customers, and provide personalized product suggestions to users.
Most AI agents built on Botpress are used for sales and marketing functions, such as AI lead generation or other ways to use AI in sales. These agents can create prospect lists, send personalized communications, and qualify leads (even better than humans). They can strategize and facilitate marketing campaigns, and run competitor analysis.
In AI customer support, AI agents can take actions on behalf of users, such as changing passwords or managing refunds. They can provide product suggestions and even advanced technical support. (Our clients have reduced their support tickets by 65% with AI agents.)
Hotels and other hospitality businesses are a natural fit for AI assistants: they are multilingual, 24/7, and easily accessible to guests. AI agents for hotels can streamline room service, suggest nearby amenities, improve hotel services, and help staff coordinate needs.
What are examples of AI in real life?
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| What are examples of AI in real life? |
Artificial Intelligence (AI) or artificial intelligence, a term we often hear today, is the use of technology that allows us to imitate the ability to think and learn like humans. Currently, AI has become part of our daily lives and is inseparable, the presence of AI helps to facilitate activities and increase efficiency. Here are some examples of the role of AI in our lives
1. Utility-Based Agents
Unlike simpler agents that simply react to environmental stimuli, utility-based agents evaluate their potential actions based on their expected utility. They will predict how useful or beneficial each option is relative to a stated goal.
Utility-based agents excel in complex decision-making environments with multiple potential outcomes - such as balancing different risks to make an investment decision, or weighing the side effects of treatment options.
The utility function of these intelligent agents is a mathematical representation of their preferences. The utility function maps the world around it, deciding and ranking which options are most preferable. The utility agent can then choose the optimal course of action. Because they can process large amounts of data, they are useful in any field that involves high-stakes decision-making.
a. Financial Trading
Utility-based agents are well-suited to the stock market and cryptocurrencies - they can buy or sell based on algorithms that aim to maximize financial gain or minimize loss. This type of utility function can take into account both historical data and real-time market data.
b. Dynamic Pricing Systems
Ever pay extra for an Uber or Lyft when it’s raining? That’s how utility-based agents work – they can adjust prices in real time for flights, hotels or rideshares, based on demand, competition or the time of booking.
c. Intelligent Grid Controllers
This type of intelligent agent is the ‘smart’ in a smart grid: utility-based agents control the distribution and storage of electricity. They optimise resource usage based on forecasts of demand and energy prices to increase efficiency and reduce costs.
d. Personalised Content Recommendations
You finish watching a movie and Netflix recommends 3 more similar movies. Streaming services like Netflix and Spotify use utility-based agents to suggest similar content to users. The utility being optimised here is how likely you are to click on it.
2. Goal-Based Agents
Goal-based AI agents are – you guessed it – designed to achieve a specific goal using artificial intelligence. Rather than simply responding to stimuli, these rational agents are able to consider the future consequences of their actions, so they can make strategic decisions to achieve their goals.
Unlike simple reflex agents, which respond directly to stimuli based on condition-action rules, goal-based agents evaluate and plan actions to meet their goals. What sets them apart from other types of intelligent agents is their ability to combine foresight and strategic planning to navigate toward specific outcomes.
a. Roomba
Robot vacuums — like the beloved Roomba — are designed with a specific purpose in mind: to clean all accessible floor space. These goal-based agents have a simple goal, and they do it well. All decisions made by these goal-based agents (like when to rotate) are made to achieve this lofty goal. The cat sitting on them is just a bonus.
b. Project Management Software
While it’s possible to use utility-based agents, project management software typically focuses on achieving a specific project goal. These AI agents will schedule tasks and allocate resources so that teams can be optimized to complete projects on time. These agents evaluate the most likely path to success and act on it on behalf of the team.
c. Video Game AI
In strategy and role-playing games, AI characters act as goal-based agents — their goals can range from defending a location to defeating an opponent. These puppet AI agents consider various strategies and resources — which attacks to use, which powers to burn — so that they can achieve their goals.
3. Model-Based Reflex Agents
When you need to adapt to information that isn’t always visible or predictable, model-based reflex agents are the right tool to use. Unlike simple reflex agents that react solely based on current perception, model-based reflex agents maintain an internal state that allows them to predict parts of the observable environment. This is an internal model of the parts of the world that are relevant to their task.
This model is constantly updated with incoming data from their environment, allowing AI agents to make inferences about unseen parts of the environment and anticipate future conditions.
They assess the potential outcomes of their actions before making decisions, allowing them to deal with complications. This is especially useful when performing complex tasks, such as driving a car through a city, or managing an automated smart home system.
Because of their ability to combine past knowledge and real-time data, model-based reflex agents can optimize their performance, regardless of the task. Like humans, they can make context-aware decisions, even when conditions are unpredictable.
a. Autonomous Vehicles
While these cars span a wide range of intelligent agent types, they are a good example of model-based reflex agents. Complex systems like traffic and pedestrian movement are the kinds of challenges that model-based reflex agents are designed for.
Their internal models are used to make real-time decisions on the road, such as braking when another car runs a red light, or slowing down quickly when the car in front does the same. Their internal systems are constantly updated based on input from their surroundings: other cars, activity at crosswalks, and the weather.
b. Modern irrigation systems
Model-based reflex agents are the powerhouse behind modern irrigation systems. Their ability to respond to unpredictable environmental feedback is perfect for weather and soil moisture levels.
The AI agent’s internal model represents and predicts a variety of environmental factors, such as soil moisture levels, weather conditions, and crop water needs. These agents continuously collect data from sensors in the field, including real-time information on humidity, temperature, and rainfall.
By analyzing this data, model-based reflex agents can make informed decisions about when to water, how much water to apply, and which areas of the field need more attention. This predictive ability allows irrigation systems to optimize water use, ensuring that crops receive what they need to thrive (without wasting water).
c. Home automation systems
The internal model here is a model of the home environment – these systems are constantly updated with data from sensors, and use this information to inform their decisions.
Thermostats will detect changes in temperature and configure accordingly. Or a lighting system can detect darkness outside and adjust accordingly - because this darkness may be due to nighttime, or from an unexpected thunderstorm, an intelligent agent is required to anticipate and react to the differences.
4. Learning Agents
Learning agents stand out because of their ability to adapt and improve over time based on their experience. Unlike more static AI agents that operate solely based on pre-programmed rules or models, learning agents can evolve their behavior and strategies. Because of this learning element, learning agents are most often used in changing environments.
a. Fraud Detection
Fraud detection systems operate by continuously collecting data and then adapting themselves to recognize fraudulent patterns more effectively. Because fraudsters are always changing their tactics, fraud detection agents must also constantly adapt.
b. Content Recommendations
Platforms like Netflix and Amazon use systems equipped with learning agents to improve their recommendations for movies, shows, and products. Even if your profile says you like horror and thriller movies, if you suddenly switch to romantic comedies, your recommendations will adapt. Just like us, we are always learning.
c. Speech Recognition Software
Apps like Google Assistant and Siri leverage learning agents to better understand incoherent attempts to speak to them. Thanks to learning agents, these systems are getting better at understanding accents and slang — so we can ask Siri things like, “Oh, Siri, can you find me the nearest snack for dinner? I’m really hungry!”
d. Adaptive Thermostats
Even smart thermostats — like Nest — learn from user behavior, such as when they tend to be home or away, and what temperature they prefer. This information is likely to change all the time, so the thermostat needs to be able to adapt over time — making it another example of a learning agent.
5. Hierarchical Agents
Hierarchical agents differ from other types of AI agents in their structured, multi-layered approach to problems. Hierarchical agents are similar to complex organizational structures, with different levels of decision-making. Different agents in the system will have different areas of specialization, making them more efficient at handling complex, multi-step tasks.
Hierarchical agents are one of the more complex ways to use AI agents, as they are made up of multiple smaller AI agents. In a sentence: Hierarchical agent structure is about structured decision-making processes at different levels of the system.
a. Manufacturing Robots
In advanced manufacturing systems, hierarchical agents manage the production line. Higher-level agents plan and allocate tasks across the system, while lower-level agents control specific machines such as robotic arms for assembly tasks. Each can communicate with each other to ensure a smooth flow of production – that’s multi-level decision-making at work.
b. Air Traffic Control Systems
This system uses hierarchical agents to manage the safe and efficient flow of air traffic. Since this is a complex task that encompasses a variety of functions, a hierarchical agent system is required for proper execution. Higher-level agents handle broader regional traffic management, while lower-level agents focus on specific tasks such as takeoffs, landings, and taxiing at individual airports.
c. Autonomous Warehouse Robots
Hierarchical agents are those that manage inventory and package handling in a warehouse enhanced with machine learning. High-level agents optimize warehouse layout and inventory distribution, while low-level agents operate robotic forklifts and individual sorters to perform the physical tasks of moving and arranging goods.
6. Robotic Agents
These are exactly what we think of when we think of intelligent agents: robotic agents. With the added element of performance, robotic agents are the offspring of artificial intelligence agents. These intelligent agents operate in a physical environment, rather than just as software agents.
The physical embodiment of these AI agents is usually equipped with sensors such as cameras or touch sensors. These types of AI agents are particularly useful in dangerous or highly repetitive tasks - it would be more efficient and cost-effective to have an AI agent perform these tasks. This type of AI agent is combined with other types of artificial intelligence, so it can physically perform utility tasks or purpose tasks, sometimes in multi-agent or hierarchical systems.
a. Assembly Line Robots
There are many robots on an assembly line. These AI agents perform tasks like welding, painting, and assembling parts, all with high precision and speed. Because they are intelligent agents, they can optimize production time while maintaining a consistent performance standard.
b. Surgical Robots
Surgery is high-stakes and high-precision, making it ideal for AI agents. Robotic agents like the da Vinci Surgical System assist surgeons as they perform precise, minimally invasive procedures. These AI agents do not perform surgery themselves, but they do extend the surgeon’s capabilities.
c. Agricultural Robots
Robots are commonly used in the agricultural cycle, from planting seeds, harvesting crops, to monitoring field conditions. These AI agents help increase productivity, as it’s easier for a machine to plant 10,000 carrot seeds than to get a human to do it.
d. Service Robots
The most famous robot waiter of all - yes, that’s WALL-E. The next runner-up is a restaurant robot that brings an all-you-can-eat sushi order right to your table. We use service robots everywhere: they vacuum, provide information to guests in hotels, and deliver goods to customers in all kinds of businesses.
7. Virtual Assistants
Virtual assistants are powered by natural language processing and artificial intelligence - and they’re probably the most familiar example of an AI agent to the general public. These intelligent personal assistants understand and process human language (using natural language processing) to perform tasks, such as setting reminders and managing emails. This type of AI agent also has a learning element: they can learn from user interactions, becoming more personal and effective over time.
a. Siri
One of the first mainstream virtual assistants, Siri is integrated into most Apple devices, including the iPhone, iPad, Mac, and Apple Watch. Siri helps with a variety of tasks, such as making calls, sending texts, setting reminders, providing directions, and answering general knowledge questions.
b. Alexa
Available on Amazon Echo devices and other Alexa-enabled products, this virtual assistant plays music, controls smart home devices, creates shopping lists, and provides news updates. And it’s ruining the human name ‘Alexa.’
c. Google Assistant
You’re familiar with this agent program from Android phones and Google Home devices. Google Assistant excels at pulling information from the web, scheduling events, managing smart home products, and facilitating real-time translation. Its deep integration with Google services makes it especially powerful for tasks involving maps, YouTube, and search functions.
8. Multi-Agent Systems
The beauty of multi-agent systems lies in the diversity and richness of their interactions. The agents in these systems often vary widely, from simple software agents that sift through data to complex entities that manage critical functions in a smart grid or transportation network.
Each agent operates semi-autonomously but is designed to interact with other agents, forming a dynamic ecosystem where collective behavior emerges from individual actions. For this type of agent program, collaboration is key.
a. Traffic Management Systems
You can find these intelligent agents in traffic management, with multiple agents representing different traffic signals, surveillance cameras, and information systems.
These AI agents collaborate to optimize traffic flow, reduce congestion, and respond to real-time conditions such as accidents or roadworks. Each agent handles data from its region and communicates with others to adjust traffic signals accordingly - so teamwork is a must.
b. Smart Grids for Energy Management
Smart grids also involve multiple AI agents, each controlling different aspects of electricity distribution, from generating stations to individual smart meters in homes.
These AI agents work together to efficiently balance energy supply and demand, integrate renewable energy sources, and maintain grid stability. The coordination of the multi-agent system ensures optimal energy distribution and cost efficiency across the grid.
c. Supply Chain and Logistics
In supply chain management, agents represent various stakeholders such as suppliers, manufacturers, distributors, and retailers. These agents coordinate to optimize the supply chain process, from procurement to delivery, ensuring efficiency and reducing costs.
d. Autonomous Swarm Robotics
Sometimes during exploration or rescue missions, swarms of robots are deployed. Each robotic agent operates semi-independently, but coordinates with other AI agents to cover a wider area, share sensory data, or collaboratively move objects. This is especially useful in challenging environments – such as a collapsing building or the surface of a planet – where teamwork among large AI systems can accomplish more than individual AI agents.
9. Simple Reflex Agents
Simple reflex agents are the runt of the litter. They have very limited intelligence and operate based on straightforward condition-action rules. These rule-based agents are not well-suited for complex tasks. However, they are very good at the specific tasks they are designed for.
Simple reflex agents are well-suited for easy tasks in predictable environments. The actions of such agents affect the world around them, but only in specific tasks.
a. Thermostat
It’s 6 p.m. in the winter? Crank up the heat. It’s noon in the summer? This simple reflex agent, with its limited intelligence, will turn on the air conditioner.
b. Automatic doors
Despite their low perceived intelligence, automatic doors are often an example of a simple reflex agent. This AI agent detects a human at the door, and the door opens. Very simple.
c. Smoke detector
This AI agent operates from your kitchen ceiling. Yes, it is a simple reflex agent. It detects smoke and sounds the alarm.
d. Basic spam filter
Some agents in artificial intelligence have been helping us every day for years. Email spam filter is one of them. The basic version does not use natural language processing, but rather keywords or sender reputation.
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The presence of AI has a positive impact on various aspects of life, from time efficiency to improving the quality of service. However, it is important to use this technology wisely, consider privacy, and ensure its development is carried out ethically. By utilizing AI optimally, our lives can become easier, safer, and more productive.
