SEO Glossary: 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.

 

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.

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.