Types of AI Agents: Exploring the Different Roles of Artificial Intelligence in Modern Technology
Artificial Intelligence has all the makings of a revolutionary technology and people in business it's important to understand and know about the different types of AI agents. Now, let’s address something though when you think about “AI agents” what is the first thought that comes to your mind? For most people, it is probably ChatGPT or maybe Gemini and all the wonderful things you can do with them. However, did you know that AI agents can do much more than just answer questions? Sure, you can do things like generating images and content and what not but there’s a whole world of different possibilities for AI agents. And, that’s what this blog is all about we’re going to take a look at the different types of AI agents and how you can use them in real world scenarios.
We’re going to learn about what AI agents actually are, how they work in general, their real world applications, the challenges you may face while using or building an AI agent and of course what the future has in store for AI agents. Building an AI agent for your business can be quite rewarding when you look at how they allow you to automate tasks. However, AI agents are not about replacing human beings with robots but more about creating a great user experience for your customers.
Let’s jump right in.
A Quick Introduction to Types of AI Agents
Many people don’t know about the different types of AI agents because a lot of these AI agents are integrated seamlessly into larger platforms. The telecom and finance industries have used some sort of automated system for interacting with customers for a long time. And you probably have used these systems at some point in your life because IVR or Interactive Voice Response is an earlier form of an AI agent.
IVR was a revolution for customer service operations because before this technology existed businesses had to employ human customer service personnel to attend customer calls. When IVR came about which you can classify into the types of AI agents this process became much simpler and basic user queries could be resolved without having to speak with a human executive. Additionally, even early IVR systems had voice recognition which could understand simple user voice inputs like “check balance” or “yes/no.” While in today’s standards this may be primitive but these systems paved the way for conversational AI that we know of today.
As time went on these systems became more capable and were able to handle more complex user inputs. While there are many types of AI agents these conversational agents are perhaps the most used when businesses want to automate their customer interaction process and today these AI agents are so advanced that they can even handle vocal conversations without seeming like an AI is talking. In later parts of the blog we will explore what these AI agents can do and how they came to be but for now let's talk about what AI agents are.
A Deeper Look Into What Are AI Agents
An AI agent is a piece of software that can carry out tasks such as answering questions, taking certain actions and providing suggestions based on data that it is trained on. There are many types of AI agents and the advancements in technologies like Artificial Intelligence, Natural Language Processing, Machine Learning and Deep Learning have made these agents quite smart and usable in everyday situations.
Earlier chatbots were exactly what they sound like, chatbots. Because you couldn't do anything more than answer questions. You had to manually feed the questions users might ask and the acceptable responses into these chatbots and they could match the user’s inputs which are essentially questions with the response that you already fed into the database. These types of AI agents are not smart and are not even AI in the sense that they don’t mimic human-like conversations. Modern AI agents use artificial intelligence and algorithms to mimic human-like conversations.
But the earlier chatbots are important in the history of AI agents because that was the first time you could really automate customer engagement where the customers have an interactive interface to connect with your business. With that said, modern chatbots work through technologies like NLP, ML and DL to understand user queries better, comprehend their context and respond in meaningful ways. These types of AI agents can do more than answer questions and businesses in some industries like travel and hospitality have used AI agents to even allow users to book entire holidays.
But now that we’ve talked about what AI agents are, let's talk about how they work because it involves a lot of technological grunt behind the scenes.
Let’s Learn More About How Do AI Agents Work
There are many ways to use AI agents for business but to work properly we need data and lots of it. Because data is what drives these systems regardless of which types of AI agents you use. If you think about it, data is what humans work off of too since we act in our everyday lives based on what we have learned over the years. AI agents are no different and have to be trained if you want them to respond in ways that make sense for your business. The technology as you can imagine is pretty complex but I’ll try to simplify it and explain so that you can understand better.
AI agents work through a combination of 3 different technologies or subsets of artificial intelligence; natural language processing or NLP, machine learning or ML and Deep Learning or DL. Regardless of the types of AI agents you come across, these technologies are running the show under the hood. We’re now going to take a jump into the rabbit hole and see how these technologies work together to create the AI that we know of today.
Natural Language Processing or NLP
What do you need your AI agent to be able to do primarily? Understand you. I think we can agree that to have a proper conversation with anyone we first have to understand each other. More specifically, understand the context of what the other person is saying. Even with different types of AI agents the major task we need the chatbot to do is understand what we are saying.
In reality, our conversations are nuanced and we use things like metaphors and expressions to make our conversations engaging. This may not be a conscious choice because our conversational skills depend on what experiences we’ve had in the past. But this is not something you would expect a machine to understand, right? Well it wasn't something machines could do back in the day and chatbots in the early days relied heavily on predefined data.
The types of AI agents we have today are quite different in that regard because they are capable of understanding the context of more nuanced conversations through “Natural Language” which is where Natural Language Processing comes in. This involves multiple layers of analysis where you have to think about the syntax, semantics and context to convert raw data into a structured form that an AI can understand. If you think about it, we do all of this in our minds where we absorb abstract data, think about it and deduce the context from the data quickly.
All types of AI agents work on neural networks which are programs that mimic human neural networks. In the earlier days of NLP things were not this smart though because while chatbots could understand longer conversations they did not quite understand the context of conversations. For example, if you type a question as a long sentence into the chat box the NLP system could only pick up keywords from your sentence and respond with answers related to those keywords. If you say “this product is defective” then the chatbot could easily understand what the problem is. However, if you give these types of AI agents inputs like “my phone feels like it's been put into an oven” the chatbot wouldn’t be able to understand that you are actually talking about the phone’s heating issues.
Modern NLP systems rely on transformer based models like BERT and GPT. These transformer models are great when it comes to understanding the context of text and work by analyzing the entire sentence of a conversation rather than just specific keywords in between. There are other techniques used here too like sentiment analysis and intent recognition so that these types of AI agents could understand the user’s emotions and purpose also.
Machine Learning or ML
The word machine learning is pretty much self explanatory; it allows AI agents to learn and improve their performance overtime. If you think about the human experience, our interaction with the world and people around us is a direct result of the things we have learned in the past and continue to learn everyday. Let’s take customer support for example—even human customer service agents improve their performance over time as they interact with more customers everyday. Today, we have multi-agent systems to handle all types of user queries but in the past the types of AI agents we had available couldn’t do these tasks and we had to use human agents if the customer had diverse needs.
Machine learning has leveled the playing field in that regard because we can now create conversational AI agents that could be trained using real world data and the more data you can provide the AI model the more accurate it will get. However, machine learning can do more than just make your chatbots smarter. That part is called supervised learning where you train the AI model on labeled data to improve its abilities in some areas. There are other types of AI agents apart from chatbot development like recommendation engines on platforms like Amazon for example. These agents can give users recommendations on products and offers and things like that and the great thing about it is that you can also implement them inside chatbots while also being used in a more hands off way through the website’s recommendations section.
Deep Learning or DL
Deep learning and machine learning are similar in some regard and DL is actually a subset of ML. However, deep learning has the power to further advance the capabilities of an AI agent for business. Deep learning is used by types of AI agents that require a lot of depth to understand complex patterns which is great for unstructured data like text, images and audio. We’ve already established that AI is basically artificial neural networks right? Deep learning is just a system with multiple layers of neural networks and can understand inputs much more deeply.
In situations where you need NLP and DL together like to build a chatbot for instance DL takes the form of recurrent neural networks or RNNs as they are also called. These types of AI agents that use RNN can understand sequential data quite effectively which means that they are great for understanding complete conversations where your users’ needs can be addressed in a much more profound way. BERT and GPT have made great strides in this area because these models could process all the words in a sentence simultaneously and provide responses that are more coherent and relevant in terms of the context.
These 3 technologies work together to create the highly capable AI agents we know of today.
Let’s Discover The Key Types of AI Agents
Now, there are a few types of AI agents and these agents can perform different kinds of tasks for different industries. Learning about these different types will give you some insight into how you can use them in different industries. But we will cover some great industry use cases also later in the blog so that you can easily explore your options for your own business.
1. Reactive AI Agents
Reactive AI agents you could say are a simple form of AI agents and you can use them with present data which means that they can’t learn from past human interactions. These types of AI agents work by following predefined rules and they can respond to specific inputs with specific outputs. This is great if you want predictability but you can’t really use them if you want advanced functionalities. They have no memory or learning capabilities, they can work in real time but they can only respond to current inputs and they are really good for static and well defined tasks.
2. Limited Memory AI Agents
Limited memory AI agents are much more capable than reactive agents and are able to learn from past interactions and use this data to improve their capabilities in decision making. A great feature with these types of AI agents is that they have a temporary memory of recent inputs that they have received and they can use it to provide context to their responses which also makes them dynamic and adaptive. However, although they can store and recall information they have received from [ast user interactions they do not have a persistent memory across long timeframes.
3. Theory of Mind AI Agents
The name might be a bit ambitious but these agents really are advanced and are able to understand emotions, beliefs and intentions of users. These are the types of AI agents that can infer the mental state of its human users and are capable of more nuanced interactions. For now these are experimental agents and they aim to mimic human empathy and social intelligence in interactions. Since they can simulate psychological and emotional understanding they are dynamic enough to respond to users based on their intent. However, these agents often require complex NLP and cognitive AI models to work.
4. Self-Aware AI Agents
You could say that self aware agents are one of the most advanced AI agents and they are capable of very advanced things like understanding their own existence, goals and actions. These types of AI agents can amazingly make decisions based on their own self perception and they are autonomous in adapting their behavior over time. That is not to say that we are in the era of Skynet with terminators on the horizon but self aware AI is an aspirational concept and we do not have a truly self aware AI agent just yet.
5. Tasks Specific AI Agents (Narrow AI)
Now, we are in the territory of specialist AI agents and you can use these agents if you want to do specific tasks. Although these AI agents cannot perform tasks that are outside of their predefined capabilities they are very good at single or a small range of tasks well. These types of AI agents are great for business use cases like customer support because you can train these agents for specific tasks and they can carry out these tasks efficiently and 24/7. When it comes to using an AI agent for business these are the agents you want.
Now that we have covered the major types of AI agents let's dive into the real world applications of these AI agents.
A Brief Look Into Real-World Applications of AI Agents
It is needless to say that AI agents are changing businesses all around the world and if you’re looking to understand how to build AI agents then you probably know how powerful they are. We’re going to cover a few types of AI agents and how they can improve operations in real world use cases.
1. Customer Support and Virtual Assistants
Customer support is perhaps the most obvious use of AI agents and they are capable of providing services to customers 24/7. Their capabilities include handling customer inquiries, resolving issues and guiding the users through different processes in real time which reduces wait times and helps improve user satisfaction. Many businesses have already implemented AI agents for their customer support especially in the telecom and finance industries.
2. Healthcare and Medical Diagnostics
Healthcare is a great sector for AI agents and these types of AI agents can assist healthcare businesses with diagnostics, patient care and administrative tasks. On a smaller scale there are businesses out there that have implemented AI chatbots for preliminary diagnosis where the patient can tell the chatbot their symptoms and the chatbot can send them information on what illness they might have. While these AI agents cannot provide you with a professional diagnosis they can give you insights into what your illness could be and how to care for yourself until you can consult a human doctor. In the real world Babylon Health is an entity that uses these types of AI agents for virtual consultations and suggest potential diagnosis.
3. Autonomous Vehicles
I bet you guessed this one already because we have had a lot of buzz around autonomous vehicles over the past few years. The way AI agents work in autonomous driving systems is a bit different because they work by processing data from sensors, cameras and GPS to make decisions in real time. These kinds of AI agents are used for safe navigation, lane assistance and collision avoidance in self-driving vehicles.
4. Retail and E-Commerce Personalization
If you’ve ever used an e-commerce website before then you are probably no stranger to recommendation systems, personalized offers and all that other goodness. These types of AI agents work by analyzing user preferences, their purchase history and browsing behavior to tailor your experience on the platform. The online buying experience is exactly that, an experience and AI agents can dramatically improve the customer experience and urge customers to spend more time on the platform with personalization.
5. Financial Services and Fraud Detection
Many financial institutions are using AI in general for many different tasks including trading and portfolio management. Apart from that AI is also a great tool in the fight against frauds and scams. AI agents in finance can help businesses automate tasks like credit scoring, managing portfolio and detecting fraudulent transactions. These types of AI agents can analyze financial transactions, find patterns in these transactions and identify anomalies. When coupled with chatbots these can even be a tool on the customers’ side where they can get real time alerts and notifications for their financial activities.
6. Education and Virtual Learning
Personalization is one of the major benefits of AI agents and in the education industry it is especially relevant because AI agents can easily tutor learners, assess their course work and provide suggestions to improve their performance. If you think about it, this is a huge advantage because these types of AI agents can work 24/7, work with students across the world in multiple languages and there is no need to train personnel. AI agents in the education sector are revolutionary and can especially help learners in financially unstable economies or remote regions gain access to good quality education.
7. Logistics and Supply Chain Management
AI agents shine in the logistics and supply chain industry because they can optimize logistics through better inventory management, demand forecasting and route planning. Additionally, they have the capability to enhance the efficiency of the supply chain because they can predict trends and manage delivery schedules in an automated manner. E-commerce companies use these types of AI agents for shipment tracking and to streamline last mile delivery operations. UPS’s ORION is a system that they have already implemented and it can calculate optimal delivery routes and in turn reduce costs and improve efficiency.
8. Gaming and Entertainment
The gaming industry has been using AI for a long time for their non player characters or NCPs. Popular games like The SIms, Grand Theft Auto and Left 4 Dead are just a few examples but AI in gaming has been around for years. AI agents can also be a great addition to the entertainment industry because like e-commerce media consumers also want personalized recommendations and similar features. Netflix has been using these types of AI agents for a few years now with their recommendation system that gives users content suggestions based on their watch history.
9. Smart Homes and IoT Devices
Home automation was a big trend a few years ago and now it has turned into an industry in itself with major improvements like smart lights, smart plugs, and much more. The most simplest example perhaps is Amazon’s Alexa which can easily connect with the smart devices in your home and carry out tasks from voice inputs. On the other hand, since these types of AI agents can communicate with your smart devices which are essentially IoT devices you can even improve home security systems with smart AI agents monitoring your home and alerting you in case of threats.
10. Legal and Compliance Assistance
The world of legal affairs is vast and there's a lot of information to know about and learn. AI agents are a great advancement here because it takes a lot of the grunt work out of the mix and provides law professionals with timesavings. An AI agent can simplify the process of legal research dramatically by analyzing documents and fetching the information you need quickly. These types of AI agents can identify relevant case law from large text books and documents and ensure that you are complying with legal regulations. Traditionally, even smaller litigations involve extensive research, going through past cases surrounding the same issue and some of these cases may be in different jurisdictions. AI agents are a great blessing for professionals working in this industry and can improve the efficiency of legal interactions dramatically.
Evaluating Benefits and Challenges of Using AI Agents For Business
As you can probably imagine AI agents have completely transformed how we do business and interact with customers across industries. Different types of AI agents are available today for pretty much any task that you can throw at them and these agents empower businesses with benefits like automation, enhanced decision making and personalized customer experiences. However, since we are dealing with technology there may be challenges along the way that you may need to manage if you want good results with these AI agents. Let’s look at a few benefits and challenges you may have to face when using AI agents.
The Benefits:
1. Automation and Efficiency
One of the benefits you get with these types of AI agents is that you can easily automate repetitive tasks which can free up your time for focusing on other parts of your business like strategy and culture. And AI agents of today are very advanced which means that they can handle a wide range of routine processes which can go from managing customer inquiries to processing large sets of data. In a nutshell, you can use AI agents for customer service and provide your customers with instant responses. This can help you reduce wait times dramatically and improve user satisfaction. Workflow automation is another great advantage with these types of AI agents where you can streamline tasks such as invoice processing, appointment scheduling and generating reports.
2. Personalized Customer Experiences
In 2024, we already know how AI agents can improve customer experience and provide customers with a much more personalized service. Businesses have realized that being customer centric is the only way to go if you want to truly succeed and AI agents are a big part of that in today’s day and age. AI agents are highly data driven and can use insights from large sets of data to provide your customers with a personalized experience. E-commerce websites are a great example for how these types of AI agents can improve customer experience with recommendations tailored to users, personalized offers and other solutions. And another great thing about them is that they improve over time as customers interact with your business more and the AI agent can learn from all the new data.
3. Scalability
Scalability is a concern for any business and most of the time scaling your business can involve scaling your teams, acquiring new tools and so on. All of these cost a significant amount of money and for smaller businesses this could mean that scaling can take time. With the types of AI agents available today you can easily handle large volumes of tasks simultaneously and this makes AI agents a scalable solution for businesses that are growing. In situations where your demand is high you have no choice but to hire extra sets of hands to meet the increasing demand. However, AI can easily handle big spikes in demand without you having to handle additional costs.
4. Enhanced Decision-Making
Did you know that there are AI agents available just for the purpose of improving the decision making process? Yes, since AI agents can process large amounts of data in real time you can get actionable insights quickly so that you can make decisions more efficiently. These types of AI agents are popular with financial institutions for tasks like risk assessment and fraud detection. With these AI agents you can identify patterns, trends and anomalies that a human analyst may miss and these data driven decisions allow you to reduce risks and maximize your returns. Additionally, you also get predictive analysis which can prove your strategic planning significantly.
5. 24/7 Availability
While human employees need to rest, AI agents can provide their services round the clock and this is one of the reasons AI agents are so popular with customer support. Uninterrupted customer service did exist in the past and still exists today but it involved the companies having to spend a fortune hiring call centers and training personnel. Now with the types of AI agents we have today all you have to do is train the AI model and the agent can perform consistently for as long as you need. On the other hand, AI agents also help in 24/7 monitoring of cybersecurity threats and anomalies which can help you ensure that your customers are having a safe, secure and optimized experience when they are interacting with your company.
6. Cost Reduction
Since you have the power to automate many tasks, AI agents can help you reduce your overhead costs significantly. You can also use AI agents to optimize your resource availability and operational efficiency. These types of AI agents are a great tool for businesses especially if you’re a small business that’s planning to expand globally or if you are a large business that wants to streamline your customer experience.
The Challenges:
1. Data Privacy and Security
When you’re using AI agents you need data and lots of it. This data could be sensitive data related to your customers or business which may raise some concerns over privacy. If this kind of data is leaked or misused then it could affect the reputation of your business. However, following good security practices when building these types of AI agents can mitigate these issues so that you can enjoy the benefits of AI agents for business.
2. High Initial Investment
Now, this could be a bit subjective depending on what path you choose to take and in the long run you can save a lot of money but training AI models can be expensive because you need powerful GPUs or TPUs for this. While there are some free options out there, those are mostly for researchers and students. Google Colab for example provides free GPUs/TPUs for training AI models for building different types of AI agents but for scalable operations that is just not enough. However, if you choose to integrate something like ChatGPT APIs for your AI agent this overhead can be greatly reduced.
3. Lack of Domain-Specific Expertise
Needless to say that if you don’t know what you’re doing you probably won’t get the results you need. If you want to deploy AI agents effectively you need domain specific knowledge to fine tune models and to ensure that you’re getting relevant outputs. Building these types of AI agents takes professionals and knowledge so make sure that you have great developers working on your AI agent. Many businesses for this reason hire agencies for their AI agent development process.
4. Ethical and Bias Concerns
The major thing here also is data because AI agents only perform according to the data they are turned on. Which is why you have to make sure that your training data does not have biases because if it does you may get responses that are biased and the AI agent can even amplify these biases as an unintended consequence.
5. Integrating with Existing Systems
When building these types of AI agents it is important that you are able to integrate them with existing systems because it is not practical to rebuild everything you currently use to implement the AI agent. So when you’re building your AI agent make sure that it can be integrated with your legacy systems to avoid any hiccups. Most of these problems can be solved by creating easy to use APIs or you could consider slowly modernizing your infrastructure to support AI before you implement your AI agent.
Analyzing The Future Trends in AI Agents
In the future, we are likely to have many types of AI agents for our business operations. Multi-agent systems for example are something that we probably see more of. As you may have already figured out, AI works best when you train it for specific tasks. For this reason building an AI agent that does multiple things may not be as accurate as a specialized AI agent. The good thing here is that you can easily make your AI agent interoperable with APIs and middleware. ChatGPT is a great example for this because you can easily integrate ChatGPT APIs into your current systems and enjoy the wonderful benefits of AI systems without having to worry about huge overheads.
These are the types of AI agents that can improve efficiency of your business to a great extent and provide your customers with a great experience. The other trend which in many ways already exists is the use of voice to interact with AI agents. Currently, although there are apps out there with voice input as an option these experiences are not very seamless. An AI agent that can truly understand the context and emotion if human voice inputs can really enhance the customer experience.
Some Final Words Before We Finish
Creating different types of AI agents for different industry use cases can enhance the everyday operations of your business dramatically. As AI gets more advanced we can expect to use it more frequently for business purposes. From customer support to creating personalized buying guides, AI is at the forefront of innovation and it creates many opportunities especially for solo entrepreneurs and small businesses.
For larger businesses AI is the right solution to handle large user bases because properly serving customers across the board is something larger businesses have always struggled with. If you look at the telecom or finance industries these types of AI agents have revolutionized how these businesses interact with their customers.
If you’re a business owner and are wondering how to build an AI agent for your business operations, book a call with our expert consultant today to get started.