Artificial intelligence (AI) agents are reshaping business dynamics through improved efficiency, scalability and cost effectiveness. These agents are considered the future of organisational efficiency for good reason, as they can help solve complex business tasks such as software design and IT automation. In this blog post, we provide an overview of intelligent agents, focusing on their different types, benefits and contributions to an organisational context through use cases.

What are AI agents?

AI agents, formerly also referred to as intelligent agents, are Computer systems or programmes that are capable of performing tasks autonomously on behalf of the user or another systemby organising its workflow and using available tools. The functionalities of these agents go beyond traditional automation tools, as they not only follow a set of instructions, but can also think, adapt and act independently.

At the heart of the AI agents are Large Language Models (LLMs). While traditional intelligent agents generate their answers based on the data they have been trained on, they can include AI-driven agents - but they don't have to. Non-AI systems are subject to the limitations of knowledge and reasoning. On the other hand, agent technology can perform complex tasks by invoking tools in the backend to obtain timely information, optimise workflows and create substacks autonomously. The core components of an AI agent include the following:

1. Agent coreThis consists of the central processing unit (CPU), in which all functions are integrated.
2. Memory moduleIt stores and retrieves information to maintain context and continuity over time.
3. ToolsThis includes external resources and APIs that the agents can use to perform tasks.
4. Planning moduleIt helps intelligent agents to analyse problems and develop strategies to solve them.

The process helps the AI agent to adapt to the user's expectations over time. The intelligent agent can provide a personalised experience and comprehensive responses thanks to its ability to remember past interactions and plan future actions. This tool call is possible without any human intervention and improves the real-life applications of these applications of artificial intelligence.

Thanks to their human-like text generation, large language models improve technological efficiency in companies and are used in a wide range of applications in the business world.

An Introduction to Large Language Models

Types of AI agents

There are different types of intelligent agents, each with unique functions and applications. They can be developed with different levels of performance. Knowing the different types of AI agents is important to find the right agent for your business needs. In this section, five types of intelligent agents are presented in order from the most basic to the most advanced type.

1. Simple reflexive agentsThese AI agents base their actions on current perception and have no memory. They function on the basis of a series of so-called reflexes or rules, i.e. the AI agents are pre-programmed to perform actions when certain conditions are met. A well-known example of such an intelligent agent is a thermostat that switches on the heating at a certain time every evening. However, these AI agents cannot react to situations for which they are not prepared.
2. Model-based reflex agentsThese AI agents use their current perception and memory to create an internal model of the world. The model is updated when the agent receives new information. A robot hoover is an example of a model-based reflexive agent. The robot hoover cleans the dirty room while it perceives and adjusts the furniture. At the same time, it memorises a model of the areas it has already cleaned in order to avoid getting stuck in the loop of repeated cleaning. However, these intelligent agents are limited by their set of rules.
3. Goal-orientated agentsThese AI agents have an internal model of the world and a goal or a series of goals. They search for sequences of actions to achieve their goal and plan them before acting. An example of this is a navigation system that recommends the quickest route to the user's destination from a range of options. This makes them suitable for complex decision-making tasks.
4. Value-orientated agentsThese agents choose the sequence of actions that will help them achieve the goal while maximising utility, which is calculated using a utility function. The utility function assigns a utility value, a metric that measures the usefulness of an action for an agent. This utility value is assigned to each scenario based on a fixed set of criteria. The AI agent selects the action that maximises the expected utility. An example of this would be a navigation system that recommends the fastest route to the user while optimising fuel consumption. These intelligent agents are particularly valuable when there are multiple scenarios and the most optimal one needs to be selected.
5. Learning agentsThese AI agents improve their performance based on their experience. New experiences expand their knowledge base and improve their ability to act in unfamiliar environments. Learning agents can be either utility- or goal-orientated. An example of this is the personalised recommendation system on e-commerce websites. The user's activities are continuously stored for learning purposes and the agent uses them to improve its accuracy over time.

How LLM can be further developed through multi-agent systems: Limitations, challenges and developments

Why the future is agentic: An overview of Multi-Agent LLM Systems

Benefits and advantages of project integration

Intelligent agents offer several advantages to organisations as they provide efficiencies unmatched by any other technology. It is the integration of the technology with LLMs that increases the value of its functionalities. In this section, we look at the benefits that AI agents offer when used with LLMs:

1. Automation of tasksAI agents can optimise the workflow by automating complex tasks. This reduces over-reliance on human resources and makes the process of achieving business goals cost-effective. These advances can be achieved on a large scale without the need for humans to guide AI agents in creating and navigating tasks. For example, Canon has implemented 135 AI-enabled UiPath robots to extract data from invoices, validate it and enter it into accounting software.
2. Optimisation of resources: AI agents can be configured to collect data from various sources such as production logs, sales records and customer interactions and provide a comprehensive overview of the business landscape. In a call centre, for example, the intelligent agent can predict peak operating hours and schedule an appropriate number of support agents for this period to minimise customer waiting time.
3. Improving the customer experienceAI agents provide comprehensive, personalised and precise answers to user queries. Such user-friendly answers improve the customer experience. Bank of America, for example, uses a chatbot called Erica that can check account balances and advise customers on budgeting. This improves response times for customers and reduces the workload for bank employees.
4. Detection of fraudAI agents can detect and prevent fraudulent activities in key industries such as the financial sector. They are able to monitor transactions in real time, making them a valuable tool for advanced analytics and pattern recognition for companies. JP Morgan Chase, for example, uses an AI agent to analyse millions of transactions and detect anomalies in real time. This helps to recognise potential fraud and protect the bank and its customers from fraud.
5. Improved scalability and flexibilityAI agents provide scalability and flexibility for businesses by quickly analysing data sets, supporting decision making through accurate demand forecasting and helping teams across organisations make strategic decisions. Salesforce, for example, uses the Einstein bot for AI-powered analyses that enable the sales team to make better decisions.

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Application examples for AI agents

AI agents are versatile, and in this section we will explore their contributions to four different industries.

1. Emergency assistanceIntelligent agents can be used during natural disasters to retrieve information about users on social media sites who need to be rescued. Deep learning algorithms can help to localise users and provide life-saving services to more people in less time. This also ensures targeted allocation and utilisation of resources as vulnerable people can be identified more quickly.
2. HealthcareAI agents are helpful in the real world of healthcare for problem solving, treatment planning, diagnosing patients in the emergency department and managing the distribution of medication. This relieves the burden on healthcare staff and saves time and effort for more urgent tasks.
3. Energy industryAI agents can support the energy industry in managing and optimising energy distribution and consumption. These agents can help predict demand, optimise grid operations and detect potential system faults before they occur. Intelligent agents help to balance energy supply and demand by analysing data from different sources.
4. TransportIntelligent agents can analyse traffic data in real time and optimise traffic flow to reduce congestion. AI agents can also be used in logistics to optimise the supply chain by efficiently managing inventory through optimised delivery routes. They can also predict delays and suggest alternative routes to ensure on-time delivery.
5. FinanceAI agents can analyse financial data and give customers insights into their investments. They can recognise strange patterns in the data and warn banks to prevent massive fraudulent activity. In this way, intelligent agents strengthen security in the financial sector.

Large Language Models (LLMs) increase efficiency and productivity. Discover in our blog post how LLMs can optimise processes and offer your company real added value:

Large Language Models: Use Cases for Businesses

Intelligent agents: At the dawn of a technological disruption

Intelligent agents are transforming the industry, with large language models (LLMs) significantly increasing their effectiveness. Advances in generative AI have led to the development of a variety of AI agents tailored to different needs. These agents offer significant benefits by protecting organisations from inefficiencies and customers from serious problems such as fraud. AI agents will continue to play an important role in various sectors and it will be interesting to see what additional opportunities they will offer in the near future.