The demand for faster and more personalised services has made automation necessary for business. Failure to do so can result in companies lagging behind and losing customers to those who can meet their needs.
Artificial intelligence (AI) plays a key role in this endeavour as it can take over repetitive tasks and enable employees to make better decisions. At the heart of these technologies are AI agents, which are automated programs designed to adapt their actions to new environments.
There are five different types of AI agents, each with its functionalities, architectures, and learning methods. This article will give you a clear picture of how each AI agent works and the benefits they have towards your business.
What are AI agents?
An AI agent is an advanced autonomous program that can observe the environment, make decisions, and take actions to achieve specific goals.
AI agents typically include sensors, decision-making algorithms, and actuators that enable the technology to detect changes and make accurate decisions based on them.
For example, an eCommerce AI agent can manage inventory and analyse customer data to deliver personalised product recommendations.
Also read: What Is An AI Agent?
Key components of an AI agent
AI agents depend on certain components working together to create an intelligent and responsive system. These components include:
- Perception: Sensors, application programming interfaces (APIs), and databases enable an AI agent to gather information about the world around them. For instance, an AI-driven chatbot can interpret user input through text and voice interfaces.
- Reasoning: AI agents analyse information either through rule-based systems, machine learning (ML) algorithms, or neural networks. An example of this can be seen with recommendation systems using ML algorithms to interpret a user’s browser history.
- Action: After analysing the information, the AI agent acts on it using automation and communication tools. These actions can be physical, like commanding a robot to move, or digital, like having software send an automated email.
- Learning: AI agents rely on learning models to discover new situations and refine their decision-making processes. Depending on their type, these learning models learn through either labelled examples, unstructured data, or trial and error.
- Memory: Sophisticated memory systems enable AI agents to store information for future use, such as recalling past interactions or providing relevant products.
Different types of AI agents with examples
All businesses need to know about five key AI agents. Each type has its own characteristics and functionalities. By understanding how they work, companies can identify which AI agents can best supercharge their operations.
Simple reflex agents
Simple reflex agents are the most basic form of AI agents. They respond immediately to environmental stimuli without relying on memory or learning models.
Instead, their actions are based on condition-action rules, which specify what actions the agent needs to take for certain inputs. While they are not suited for complex operations, simple reflex agents have a fast reaction time and are easy to install.
An example of how simple reflex agents work can be seen with thermostats that are designed to turn on heating when the temperature drops below a certain threshold. Similarly, industrial sensors can immediately stop operations if they detect an obstruction in a work area.
Model-based reflex agents
These agents are designed to operate in partially observable environments. They use internal models to track changes and detect any aspects that simple reflex agents might not notice immediately. Although these models cannot “remember” past events, they are capable of making decisions about the current situation.
For instance, model-based reflex agents use data on routine household activities to help security systems distinguish between harmless events and threats. Similarly, self-driving cars use mapping data to navigate passengers safely to their destinations.
Goal-based agents
These agents use search and planning algorithms to decide on the best actions that will help them achieve specific objectives. This can be seen with a chess-playing AI that considers multiple moves to achieve a checkmate.
While goal-based agents excel at problem-solving scenarios, implementing them requires significant computing resources.
Utility-based agents
Utility-based agents assign numerical values to different outcomes, aiming to identify the best actions that can lead to maximum satisfaction. This is done by considering multiple factors and trade-offs. These agents are useful in situations involving competing objectives.
For instance, an AI-powered financial trading system weighs risk against potential returns to help users find the best investments.
Learning agents
Learning agents are designed to modify their behaviours over time based on past interactions, experience and feedback. This enables them to improve their performance and find new ways to achieve their goals.
Learning agents are well suited in situations where ideal methods are not known in advance and must be learned by interacting with the world around them.
For instance, by conversing with users, customer service chatbots can provide helpful answers to users’ queries. Similarly, energy management systems can reduce energy wastage by studying past power consumption patterns.
Applications of AI agents (Real-world examples)
While not obvious, we have already seen AI agents at work in various industries and fields. In this section, we will explore how AI agents have transformed operations and delivered better customer experiences.
- Streaming entertainment: Netflix and Amazon Prime Video use learning algorithms to recommend shows based on what their viewers are currently watching. For example, while a viewer might enjoy horror or thriller movies, the services can adapt their recommendations if they decide to watch a different genre, like romance.
- Human resources: Companies have implemented AI agents to evaluate candidates and obtain unbiased hiring recommendations. According to Mercer, 81% of businesses use AI agents for candidate screening, 60% for interviewing, and 50% for evaluating.
- IT support: Microsoft’s Azure Directory AI agent enables employees to reset their passwords through secured workflows without having to consult internal support teams. This feature has helped to reduce response times and has saved thousands of dollars annually in support costs.
- Finance: TurboTax uses AI-powered conversational interfaces to walk users through the tax filing process, answer questions, and suggest ways to maximise deductions. It allows users to fill in their tax details faster and more accurately.
For a deeper dive into how AI can benefit specific industries, read:
Future trends in AI agents (Looking ahead)
While businesses have already adopted AI agents, new advancements will enable the technology to tackle complex issues in 2025 and beyond.
One such innovation that businesses should keep an eye on is multi-agent systems (MAS). These systems bring together several AI agents to execute tasks that require multiple layers of decision-making. For instance, MAS can be utilised by manufacturing companies to optimise supply chains and predict periods of demand surges.
Another advancement to consider is embodied agents, which are designed to interact with the physical world. Robots and virtual assistants are just some examples of embodied agents. Because of their ability to interact with the environment, embodied agents can create more realistic and believable solutions.
Furthermore, embodied agents can learn from new experiences and apply them to new situations. This, in turn, makes the process of training AI systems faster and cheaper.
Finally, as AI becomes prevalent in the business landscape, ensuring transparency in agent behaviour is crucial to building trust and maintaining compliance. This will lead to the rise of explainable AI (XAI), which will ensure that all AI agents can provide a clear and reasonable justification for their actions. Those who are successful in implementing this technology can increase brand loyalty and avoid costly fines.
Conclusion
AI agents are poised to transform the way we interact with customers and make decisions. For businesses looking to compete in 2025 and beyond, it is crucial to adopt this technology. Choosing a suitable AI agent for your business needs can empower the workforce, make customers feel valued, and achieve the best outcomes that propel a company forward.
