In a nutshell: What are AI agents (EN = AI Agents)?
"AI agents" is an umbrella term for software based on artificial intelligence (AI). Advanced AI agents are able to interact with multiple systems, make decisions completely independently and perform complex tasks.
Chatbots such as ChatGPT and Microsoft Copilot, which only perform individual tasks and deliver the results in the form of text, voice or images, are also AI agents.
What distinguishes AI agents from chatbots?
The chatbots known worldwide since 2022 at the latest (e.g. ChatGPT, Microsoft Copilot) are examples of general AI agents. They answer questions, provide information and help with less complex tasks.
In addition to these general AI agents, there are specialized AI agents that have a different range of functions:
With a specialized image processing AI such as DALL-E or MidJourney, better images can be created than with chatbots.
Some programs are specifically trained for customer service and differ from general AI in that they make decisions independently across several levels and solve complex, multi-level tasks.
Cross-system AI agents can work together with hardware, software and other networks, enabling the control of machines in industry or autonomous driving, among other things.
AI agents is therefore an umbrella term for software with artificial intelligence. However, it is currently (as of January 2025) apparent from internet sources that "AI agents" is hardly used as an umbrella term for AI software anymore, but rather as a kind of buzzword to describe a certain type of AI.
Timo Specht will go into this in more detail in the following sections.
AI agents as systems to make autonomous decisions and achieve goals
In January 2025, "AI agent" establishes itself as a buzzword for comprehensive systems that interact with other software and hardware. These AI systems receive an order from humans, which they then carry out independently.
The special feature compared to AI software such as ChatGPT and Google Gemini is the ability to handle complex tasks that require multiple actions and autonomous decision-making.
For example, Amazon Web Services and Microsoft define AI agents in their newsrooms as software that records physical and virtual interfaces (i.e. hardware and software) completely independently for machine learning and makes decisions based on the data received.
The goals set by the user serve as a guide for the AI to make target-oriented decisions and carry out efficient actions to achieve the goal. AI agents can also complete more complex tasks themselves over several stages thanks to their strong decision-making skills and interactions with hardware and software.
In order to clearly distinguish this interpretation of AI agents from chatbots, the following applies: While bots such as ChatGPT and Copilot have to divide complex and multi-stage tasks into several subtasks, the more autonomous AI agents can perform multi-stage tasks themselves.
Users formulate the goals they want to achieve with the AI agents, whereupon the AI agent takes all measures to do so.
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Example of application of AI technologies with a high degree of autonomy
Users of AI agents with a high degree of autonomy benefit from the fact that they can delegate a large number of more complex tasks to their AI assistant. Microsoft cites the example of an autonomous AI that knows a company's product catalog inside out.
The AI could be entrusted with the task of providing first and second-level support for customer inquiries in order to answer simple questions.
The AI could also automatically create and send product presentations.
It is also conceivable to delegate the creation of product descriptions on the website to the AI.
As a largely autonomous program, the AI agent in this example helps to increase efficiency and frees up employees so that they can focus on other tasks. All users have to do is tell the AI agent the goals they want to achieve.
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Special features and advantages of AI agents
If we assume advanced AI agents that exceed the capabilities of general bots such as ChatGPT and Claude 3.5 Sonnet, then the independent perception of data is a special functional feature.
AI agents extract the data independently from the connected software (e.g. CRM system, ERP system), perform a data analysis and use the data to make decisions.
In addition to the independent perception of data, autonomous decision-making is a special skill. Thanks to various principles, decisions can be derived from the collected data and data analysis, which contribute to the efficient completion of tasks.
Examples of decision-making principles are rule-based systems (use of predefined rules for decision-making) or heuristics (making decisions based on experience).
Advanced AI agents with a high degree of autonomy carry out actions independently to achieve their goals. The special feature of advanced AI agents is that, unlike chatbots, they can perform multiple actions.
They can even take breaks between actions in order to reflect on their actions by analyzing data and adapt them if necessary. This is the fourth special feature of AI agents: Learning and adaptation.
The benefits of such comprehensive AI agents surpass those of chatbots, making them a promising ChatGPT alternative. Instead of having individual tasks performed and re-prompting the AI model for each task, users access comprehensive systems with modern AI agents.
These AI systems interact with hardware and software and work together with them across interfaces . This significantly increases efficiency and productivity at work.
Conclusion: What are AI agents?
AI agents are any type of software that behaves in an artificially intelligent way - from chatbots like ChatGPT to self-driving cars. At the beginning of 2025, the term "AI agents" will be used less for chatbots and instead for a special form of AI software: for autonomous systems that perform complex and multi-step tasks and interact with different software and hardware.
Such comprehensive systems make it possible, for example, to have the computer operated completely automatically by the AI or to use the AI as a comprehensive project manager.
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We are already closer than ever to AI agents, as advances in artificial intelligence enable these systems to perform increasingly complex tasks autonomously and interact effectively with software and hardware.
How do you build AI agents?
To build AI agents, developers must create artificial intelligence algorithms that can analyze data and learn, as well as integrate interfaces for interacting with hardware and software.
What does an AI agent do?
An AI agent is capable of independently collecting and analyzing data and making decisions in order to perform complex tasks and achieve goals by interacting with various software and hardware systems.
What are the 5 types of AI agents?
The five types of AI agents are: rule-based agents, learning agents, autonomous agents, dialogue-oriented agents, and specialized image processing agents. Each type has specific functions and areas of application that are tailored to a wide variety of tasks.
What are AI agents?
AI agents are software solutions based on artificial intelligence that are capable of making autonomous decisions, performing complex tasks, and interacting with various systems.
What are they, the AI agents?
AI agents are software solutions based on artificial intelligence that can interact autonomously with various systems to perform complex tasks and make decisions. They go beyond simple chatbots and act independently.
What is an example of an intelligent agent?
An example of an intelligent agent is an autonomous vehicle that uses sensors and AI systems to make decisions independently in order to navigate safely in traffic.
Are AI agents the future?
The future belongs to AI agents. With their ability to make autonomous decisions and perform complex tasks, they are revolutionizing various industries and improving efficiency, making them an indispensable part of digital transformation.
Are AI agents artists?
AI agents are not artists in the traditional sense, as they generate creative works by using algorithms and existing data rather than their own intuition or emotions.
How are AI agents composed?
The composition of AI agents includes advanced algorithms, machine learning, data analysis systems, and interfaces that enable interaction with software and hardware to make autonomous decisions and perform complex tasks.
What capabilities do AI agents possess?
The capabilities of AI agents include autonomous data collection, complex decision-making, and the execution of multi-step tasks. They can interact with various software and hardware systems and independently perform actions to achieve goals.
How do AI agents function in everyday life?
AI agents function in everyday life by analyzing data, making decisions, and performing tasks independently. They interact with software and hardware to optimize processes, such as in customer service or the automation of routine tasks.
What are the advantages of AI agents?
The advantages of AI agents are manifold: they automate complex tasks, interact with various systems, make autonomous decisions, and thus significantly increase efficiency in companies by reducing the burden on human resources.
How do people interact with AI agents?
Interaction between humans and AI agents takes place through the formulation of goals and requests. Users communicate via text input or voice commands, whereupon the AI agents independently make decisions and perform tasks to achieve the desired results.
Where are AI agents used?
AI agents are used in numerous areas, including customer service, data analysis, industrial automation, and autonomous driving. They enable efficient processes by performing complex tasks independently and interactively with human systems and hardware.
What technologies support AI agents?
The technologies that support AI agents include machine learning, natural language processing, computer vision, and robotics. These technologies enable AI agents to analyze data, make decisions, and interact with various systems and hardware.
What are the challenges with AI agents?
The challenges associated with AI agents are manifold: They include data security, ethical issues, the complexity of interacting with existing systems, and the need to continuously optimize and monitor algorithms.
How do AI agents learn from experience?
AI agents learn from experience through continuous machine learning, analyzing data, recognizing patterns, and making adjustments to optimize their performance and decision-making.
Which industries benefit from AI agents?
The industries that benefit most from AI agents are primarily IT and automotive, healthcare, finance, retail, and marketing, as they can increase efficiency and innovation through automation and data-driven decisions.
How do AI agents optimize business processes?
AI agents optimize business processes by making automated decisions, analyzing data, and efficiently handling complex tasks. This increases efficiency, reduces human error, and enables faster response to business needs.
What is the difference between AI agents and humans?
The difference between AI agents and humans is that AI agents rely on algorithms and data to make decisions autonomously, while humans possess emotional intelligence and creative abilities that go beyond logical analysis.
How is the performance of AI agents measured?
The performance of AI agents is measured using various criteria, including accuracy, efficiency, decision quality, and interactivity with other systems. These factors help to evaluate the effectiveness and usefulness of the agents in real-world applications.
What knowledge do developers need for AI agents?
Developers of AI agents need comprehensive knowledge in the areas of machine learning, data analysis, programming, and specific AI frameworks and algorithms in order to effectively create and optimize autonomous systems.
How secure are AI agents in use?
The security of AI agents in application is a crucial aspect. Advanced AI agents have security mechanisms to protect data and minimize risks. Nevertheless, their security depends heavily on programming and the context of use.
What are some examples of AI agents?
Examples of AI agents include chatbots such as ChatGPT and Microsoft Copilot, as well as specialized systems such as DALL·E and MidJourney, which can generate images. Autonomous systems in industry that control machines also fall into this category.
How do AI agents influence employment?
AI agents have a wide range of effects on employment: they can automate jobs, thereby reducing demand for certain activities, but they can also create new jobs that require specialized skills in working with AI.
What are the ethical issues surrounding AI agents?
Ethical issues surrounding AI agents concern responsibility for decisions, transparency of algorithms, data protection, and potential discrimination. It is important to ensure that these systems are designed and used in a fair and responsible manner.
What does the future hold for AI agents?
The future of AI agents will be characterized by increasingly autonomous systems that take on complex tasks, interact efficiently with different software and hardware environments, and thus become more decisive and powerful.
How do we test the effectiveness of AI agents?
The effectiveness of AI agents is tested by monitoring and evaluating their performance in executing complex tasks, their ability to analyze data and make decisions, and their interaction with other systems.
Which algorithms do AI agents use most frequently?
AI agents often use algorithms such as neural networks, decision trees, and reinforcement learning. These methods enable them to recognize patterns, make decisions, and learn from experience in order to perform complex tasks autonomously.
