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Chatbots have changed how customer service is handled: efficiently and swiftly. However, chatbots can struggle when it comes to dealing with certain tasks. How do we overcome these issues and fulfill more use cases for businesses? Introducing AI Agents, the solution to all that and more.
Regarded as the next generation of chatbots, artificial intelligence agents can handle tasks autonomously, adjust to changes, and solve any problems according to individual needs. Thus, with their advanced capabilities, AI Agents can revolutionize not just customer service but entire business operations.
Thus, let’s talk about AI Agents, how they work, the different types, benefits, examples, and a lot more.
An AI Agent is a program that is able to complete tasks independently based on user needs. When configured, agents are built for a specific purpose, and they will complete that goal as needed based on instructions or prompts.
Thus, utilizing LLMs, APIs, databases, and more, AI Agents strive to complete the tasks at hand and learn from the experiences to improve their functionality. To carry out tasks, agents have their distinct components when they are built. So, let’s try to understand that a bit.
AI Agents vs. Chatbot is a recent topic, and there are some significant differences. Here they are.
These are some of the few differences that really help agents stand out. To learn more about AI Agents vs. Chatbot, please refer to this blog.
Each AI Agent is unique, but generally shares some components when it comes to the architecture. This is determined when an agent is built and also specifies what sort of actions it can do. Let’s take a look at these components.
Each AI Agent has an architecture that it’s built upon. This could be a physical architecture that interacts with an environment through a robot. Also, the architecture could be for a software agent, which will be implemented in a website or any other similar platform.
AI Agents need a way to observe and collect data, and interfaces let them do just that. Robotic agents make use of sensors, actuators, and the like. Meanwhile, software agents are connected to knowledge bases, databases, user data and other information through APIs and other protocols.
This also allows AI Agents to connect to storage to not only collect data but also store any new information that it receives. This is a process of improvement for the AI Agent in the long term.
To note, the interfacing is not just simply internal systems but can also be external sources like Wikipedia or Google searches and such.
Using data collected via interfacing, an AI Agent can plan through the use of a Large Language Model (LLM) or a Small Language Model (SLM). For both robotic and software systems, it makes use of different interfaces and uses either models to plan the actions.
Also, the functionality of an AI Agent will also consist of any feedback system (if implemented), knowledge base integrations, and such.
Next, we have an execution module that determines what kind of action the AI Agent can take. Using all the data collected and the plan of action established through an LLM or SLM, this module will carry out those tasks.
Now that we are up to speed on what an AI Agent is and the components inside one, let’s talk about how an agent works. To explain in a more contextual way, we will also use a situation through which it will be easier to understand.
The first step is to take an instruction or input from a user. This is then taken and analyzed by the AI Agent to understand the goal the user has in mind. Then the artificial intelligence agent starts working towards this goal to give the necessary output.
For example, a new user asks REVE chatbot about which pricing plan is best for their company. Our AI Agent takes that input and understands that it has to suggest the right plan for the user.
Next, the AI Agent will create a list of tasks it needs to complete in order to give the right answer. This creates a checklist that the agent will do and consists of different sorts of tasks like web searches, API calls, checking the knowledge base, and the like.
Using the same example, the AI Agent determines that it needs to. It will create tasks to collect company information, their requirements, pricing plan data, industry they are in, and so on. Then, it will also create tasks to compute all that information to generate an optimal solution for the user.
As the AI Agent has determined the tasks it needs to complete, it starts collecting the data. Through different data collection processes like web searches, databases, APIs, and more, the agent finds all the necessary data to complete the tasks.
Continuing the example, the AI Agent will scan through different sources and searches to find information about the company and gather our internal data on pricing plans. Concurrently, it will ask the user for more information about his or her requirements and company information as needed.
Through this process, the AI Agent checks its progress after each task it completes and adds more if needed. This iterative process creates a comprehensive result for the user and makes use of one or more LLMs to generate the right answer.
In the example’s case, the AI Agent starts cross-checking the company information and requirements with our pricing plan knowledge and creates some opinions. It will then check its generated results with external and internal sources and keep improving the answer as each task is completed. Then it will send the data to the user once a final result is computed.
After sending a result to the user, the AI Agent will use feedback to do further iterations on the result. Using external sources and internal databases, the agent will continuously improve the results.
To further reinforce this step, the AI can store all the data collected, and the result formulated in its knowledge base for future use.
For example, the user can say that the pricing plan suggested does not meet one or two of the requirements. Then, the AI Agent will go back and do more iterations on the result to accommodate the missing requirements and create a better response.
While the general process of an AI Agent is similar, there are many different kinds of agents available in the market. Each has its unique uses, and here they are.
This is the simplest AI Agent type that uses a set of conditions called reflexes to carry out its action. However, Simple Reflex Agents have no memory capabilities, thus only working in a fixed environment.
This means that the artificial intelligence agent will only take action when one or many conditions are satisfied.
Unlike Simple Reflex Agents, the Model-Based versions have memory capabilities that allow them to upgrade operations. However, these AI Agents are still reliant on those sets of reflexes assigned during configuration.
With Model-Based Agents, you can produce better solutions as they have the capability to learn from new information and can operate in a changing environment.
This type takes a different approach, as Goal-Based Agents are configured with goals or a set of goals. The goals can be as simple as checking the temperature or as massive as creating a new chatbot.
To carry this out, the Goal-Based Agents uses several different components, such as a knowledge base, reasoning module, planning, execution, and so on. Thus, these agents create a set of actions to complete the goal, collect the information as needed, and create solutions.
There are a few different kinds of Goal-Based Agents:
This type updates Goal-Based Agents by using utilities to create the best possible solution. These utilities are sets of criteria or preferences asked by users that can be used by AI Agents to create better solutions.
What Utility-Based Agents do is create multiple solutions for a goal and then use the criteria to select the most optimal answer. This is the type of agent that can really address user preferences while giving them a great solution.
This type of AI Agents takes a new approach as it prioritizes learning new information and improving results in the process. Taking the characteristics of either Goal-Based or Utility-Based Agents, Learning Agents improve their performance over time with more information.
There are four components to Learning Agents, and they are as follows:
Through these components, a Learning Agent constantly adapts and improves its operations and provides better responses.
This is the first type of AI Agents that makes use of a lot of them. Assigning agents in a hierarchy, this type divides a goal or problem into multiple tasks for each agent to handle.
Hierarchical Agents have agents that are either at a higher level or lower level. This can be made of two agents or 10 agents. The hierarchy depends on the purpose it is configured for, as well as how you want to structure the AI Agents.
The second type of AI Agent that makes use of multiples is Multi-Agent System or MAS in short. Unlike Hierarchical Agents, MAS makes use of artificial intelligence agents that are independent of one another but still collectively solve a problem.
Multi-agent systems can be categorized into two different types:
As AI Agents are a vast technology that can improve many industries, there are a lot of benefits to using them. Here are seven of the most important ones.
With AI Agents, there is no need to manually configure tasks, as agents can automatically start completing them once a request or query is received.
Not only that, but agents also don’t need to be told what tasks to complete for a goal or such. So, AI Agents provide a level of automation that other technologies have not been giving in the past.
The solutions received from AI Agents are at a higher level as they make use of multiple information sources as well as constantly iterating on their mistakes. So, with a deep learning capability as well as the ability to make use of varied sources of information, AI Agents can provide high-quality solutions.
AI Agents perform better than other technologies that are available at this moment. Thus, making use of the capability of performing higher performance while using fewer resources is an enticing offer.
As AI Agents have superior reasoning skills, this makes them capable of making better decisions. Also, artificial intelligence agents can make use of updated information as well as many different kinds of data, so their decisions are more informed.
This ensures high-quality solutions due to how AI Agents operate.
AI Agents are capable of handling multiple workflows and can be configured for multiple goals. Thus, an agent can be used for multiple use cases, leading to more scalability for any industry.
AI Agents reduce the need to have as many human personnel to do certain tasks. Thus, it reduces a need for resources and even finances in the long term.
Using AI Agents, which can refer to multiple different data sources, you can gain comprehensive insights as well as increase the accuracy of analytics.
This can be vital for any business in order to improve the efficiency of the company. Also, it gives companies a better view of how to improve themselves for more sales and revenue.
There are many use cases for a technology like AI Agents. Here they are as follows:.
One of the biggest use cases for AI Agents is in customer service. In current times, many businesses are still using live chat or chatbot solutions to operate their customer support department.
However, live chat requires human agents, and chatbots are fairly limited. In this regard, AI Agent solves two problems: it reduces the need for human agents and provides superior automation.
So, instead of using chatbots and live chat, you can use agents in order to automate your customer support solution and provide better service. There are many customer service apps like REVE Chat that will allow you to implement AI Agents for your website or any social media app. So, the support provided will not only be comprehensive and personalized, but each problem will be solved automatically without human intervention.
Example: A customer wants to learn more about your product, and an AI Agent can give them the comprehensive details, including recommendations automatically.
Financial institutions can really benefit from AI Agents, as they can provide some excellent support to such businesses. This extends to any BFSI company, as customers can get confused with the amount of plans and services available.
Using AI Agents, a financial institution can recommend services such as loans, credit cards, investments, etc. All of this will be personalized, as the agent will gather both company and customer information to make these suggestions. By using agents, BFSI companies can provide the most curated solutions to all their customers.
Also, you can serve employees in terms of getting information for benefits, salary information, and the like using artificial intelligence agents as well. Hence, artificial intelligence agents can help both employees and customers of financial institutions.
Example: A customer wants to apply for a credit card from Scotia Bank. Hence, the AI Agent will collect all the customer’s information and match his income with the available credit cards. By cross-checking, the agent will provide the right credit card to the user and start the application process for the customer.
Similar to financial institutions, AI Agents really can change how telecom companies operate. They have many roaming plans, prepaid packages, and more to offer that customers may get confused navigating through. Thus, an agent can provide all of this information in a more concise and user-friendly manner to provide assistance to users. This means any product or service recommendations can be made by an agent automatically by analysing customer data and the services information.
Also, AI Agents can help companies internally by detecting fraudulent activities, network issues, advanced data analysis and a lot more. Not to mention this will also help solve problems much quicker so that customers are not affected as much due to network disruptions.
Example: A user wants to get a prepaid package from stc Kuwait, and the AI Agent will start the process by collecting information about the customer and all the packages. This will help the Agent identify the usage rate of the customer and then suggest the right prepaid package based on that.
For both e-commerce and retail companies, artificial intelligence agents can be very crucial. Using a technology like this, such businesses can provide personalized product recommendations through customer info and preferences. Not just that, they will also take the item with the right size and color to the shopping cart and buy it for them automatically by taking contact and payment details.
Also, internally, you can carry out inventory management with ease and ensure the product stock does not run out. Thus, an agent can serve customers and businesses in an efficient manner.
Example: A user wants to order a pair of shoes from Le Reve. The AI Agent will gather information on the customer’s shoe size and design preferences to give some personalized recommendations. The user will like those suggestions and picks a product from the selection. After that, the AI will add it to the shopping cart and buy it for the consumer with the right specifications.
Another use case for AI Agents is in the healthcare industry. Agents can do the little tasks, like appointment scheduling, to the big tasks, like treatment planning and medicine recommendations.
An industry like healthcare can really benefit from the vast amount of knowledge you can train an AI Agent with to provide personalized solutions.
Using patient data and medicine or treatment information, an artificial intelligence agent will be capable of providing the best treatment for an individual patient and scheduling a doctor visit when needed.
Example: A patient has a stomach problem, so the AI Agent schedules a doctor appointment. After the patient meets the doctor, the doctor can input the patient’s problems, and the agent will provide a treatment plan that the doctor can cross-check and then give to the patient.
While everything we have said so far is great for you, there are some challenges you need to be aware of when using AI Agents. Here are some prominent ones.
AI Agents can handle a lot of different tasks at the same time, but that also means that the complexity of the technology is really high. Unlike chatbots, agents require more technical integrations and systems to function autonomously.
This can be a big hurdle to overcome at the initial stages of implementation, and everyone thinking of getting artificial intelligence agents for their business or software should keep it in mind.
Data bias is a serious issue considering AI Agents provide the solutions autonomously. If the data used to train the agents is biased, they will provide solutions according to those biases and not provide the best solution possible.
Thus, it is important to use a diverse amount of data and ensure that the information used is as unbiased as possible. Also, periodic bias checks can help weed out any preferences that the AI may develop. These checks allow developers to find and patch up issues quickly.
All in all, data bias is something to avoid, especially when you are dealing with a technology that is designed to operate by itself without much intervention.
For AI Agents to operate efficiently, it learns a massive amount of data by acquiring it through different sources. While more data ensures that the agent performs optimally, that also means they hold a lot of information that could be exploited.
In some cases, the artificial intelligence agent may malfunction and reveal sensitive information, or there can be malicious attacks on it to gain all that data by hackers.
So it is important for companies to implement data encryption, conduct adversarial training, carry out regular security checks, and so on.
An AI Agent’s greatest strength is operating autonomously and giving the best solutions to the users. However, it is important to ensure that the solutions given are in line with ethical considerations that do not cause harm to humans in any way.
Thus, it is important that agents are versed in societal norms and have information on the laws and regulations for where they are operating. Also, having a degree of human control can really help, as through feedback or internal checks, an AI Agent can be tuned to provide more ethical solutions.
While AI Agents are revolutionary, here are some suggestions that businesses can use to ensure better performance.
When implementing an AI Agent, have a goal in mind that you want to fulfill with the program. This reduces the complexity of implementing an agent, as you know what you need from it.
Using goals as an indicator, you can implement one or many artificial intelligence agents to cover all the necessities of your business.
As there are seven different types of AI Agents, you need to make sure that your business is using the right one for the goal. This makes implementation easier, as you can configure different types of agents for different purposes.
Thus, when you implement the right one, it saves you resources in the long term and serves your business better.
When training an AI Agent, it is important to ensure that the data is unbiased and of high quality. Without high-quality data, an agent will be incapable of providing the right solutions.
Thus, ensuring data quality and bias through verification is an important step when configuring an agent.
To ensure that AI Agent is not vulnerable to security issues, regular checks are required to plug the holes. Also, doing adversarial training and using the right encryption process protects your data even further.
This is highly important as your agent will be handling a significant amount of data, and your business should do their best to protect it.
While AI Agents can operate by themselves, it is important to monitor the actions they are taking and gather customer feedback. Through monitoring these logs, you can improve the agent further.
Also, if an artificial intelligence agent starts malfunctioning, having the option to intervene when needed is crucial. So, while AI Agents can be the best autonomous systems, human intervention and monitoring are necessary in the event something starts going wrong.
Whether it be a customer or employee experience with an AI Agent, it is important to prioritize that. At the end of the day, agents are serving a solution, and whoever receives the solution should have a good experience and be satisfied with the solution.
So, ensure that customer satisfaction and experience are monitored and improved. That way, your business increases its reputation while providing fast and efficient solutions through AI Agents.
As time passes, AI Agents will get even better at making informed decisions. They can already perform with minimal intervention, but there is always room for improvement.
Thus, as Large Language Models (LLMs) develop, so will the agent’s capabilities of learning new information. Also, other technologies like NLP and machine learning will certainly improve, thus making AI Agents more human-like and providing personalized solutions and responses. Hence, the future of AI Agents is bright.
Over the course of time, artificial intelligence agents will continuously improve and become more robust. Thus, you can expect that AI Agents will be more productive, provide better solutions, be more capable of handling complex tasks, and so on in the future.
That is what we at REVE Chat are striving to do, to provide the best possible AI Agents to our clients. To try out our agents for customer service, you can try out our solution to witness how our agents can help your business.
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Nur-Nabi Siddique is the CTO at REVE Chat. He is renowned for his deep proficiency in the Spring Framework, NLP, and Chatbot Development. He brings a wealth of knowledge and a forward-thinking approach to technological innovation.
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