AI Agent

An AI agent is an AI system that not only answers questions but also pursues a goal, plans tasks, processes information, uses tools and carries out actions. This is precisely what sets it apart from many traditional chatbots and AI assistants: an AI agent does not merely generate a response. It can take action.

For businesses, this opens up new possibilities for automation. AI agents can handle multi-step tasks, consolidate data from various systems and, within defined limits, independently initiate the next steps.

Key facts about AI agents at a glance

An AI agent pursues a goal and can plan and carry out several steps to achieve it.
It can access data, software, APIs and other tools.
Typical areas of application include sales, customer service, marketing, analytics and administration.
Not every process requires an agent: rigid workflows are often better automated using traditional methods.
Clear authorisations, data protection, monitoring and human-in-the-loop are crucial.

What is an AI agent?

An AI agent is a software system that is given a goal and independently decides what steps are necessary to achieve that goal. To do this, it can understand context, gather information, evaluate results and use external tools or systems.

For example: An agent is tasked with reviewing open sales opportunities and identifying leads with follow-up potential. To do this, it can retrieve CRM data, evaluate contacts and – with the appropriate authorisations – prepare the next step in the process.

This marks a shift in AI from information processing to task completion. This is particularly interesting for processes that do not follow a completely rigid sequence.

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“An AI agent becomes interesting when AI is intended not just to provide answers, but to drive a process forward.”

What an AI agent is not

A chatbot typically focuses on communicating with a user. An agent, on the other hand, can also plan further steps and utilise systems.

Predefined workflows follow fixed rules. An AI agent can decide, within defined limits and depending on the situation, what the next logical step should be.

An agent possesses neither human judgement nor, by default, the necessary understanding of business implications.

Even modern AI systems can misinterpret information, produce incomplete results or select inappropriate actions.

A poorly defined or unclear process does not automatically improve simply because an AI agent is used. The clearer the objective, the data set and the decision-making logic, the more effectively an agent can be utilised.

The more data, applications and permissions an AI agent is given, the more important guardrails, monitoring and approval processes become. Autonomy can only work within a business if there are clearly defined boundaries.

How does an AI agent work?

Put simply, an AI agent operates in a loop comprising understanding, planning, acting and evaluating. First, it interprets the objective and context. It then breaks the task down into meaningful steps and decides what information or tools are required.

Via interfaces, the agent can, for example, access databases, CRM systems, documents, calendars or other applications. It then carries out an authorised action and evaluates the result. If the objective has not yet been achieved, the next step follows.

So the language model alone does not make an agent. What is crucial is the combination of the AI model, context, tools, decision-making logic and authorisations.

AI Agent vs. Chatbot, AI Assistant and Traditional Automation

Characteristic	AI Agent	Chatbot	AI Assistant	Automation
Main Function	Set Goals and Take Action	Answer questions	Support Tasks	carry out set procedures
Autonomy	higher	low	mostly moderate	rule-based
Tool Access	central	optional	frequently	clearly defined
Planning	dynamically possible	usually no	in part	specified
Ideal for	variable tasks	Dialogue	Support	stable processes

The terms overlap, but differ primarily in terms of the degree of autonomy. What matters is not how well a system formulates, but what it is permitted to do independently.

In practice, the boundaries are blurred. For businesses, therefore, the product label is less important than the specific question: what decisions and actions can the system carry out independently?

What specific tasks can an AI agent take on?

AI agents can research leads, analyse CRM data, prioritise contacts and prepare follow-ups.

You can categorise enquiries, access information, check customer details, initiate actions and escalate complex cases to staff members.

Checking documents, collating information or processing data records: information-intensive routine processes often offer significant potential.

An AI agent can carry out research, prepare reports, link data from various sources or assist with individual campaign steps. Business-critical decisions should, however, continue to be subject to oversight.

What are the building blocks of an AI agent?

At the heart of the system is usually an AI or language model that interprets information and prepares decisions. Data and context provide the necessary knowledge base, whilst tools and APIs enable access to other applications.

Added to this are objectives, instructions and, where applicable, a memory for information from previous steps. Equally important are authorisations, guardrails and human-in-the-loop mechanisms. These determine what an agent is permitted to do independently and when a human must intervene.

When does an AI agent make sense – and when is automation sufficient?

For stable ‘if-then’ processes, traditional workflow automation is often simpler and easier to control. An AI agent becomes particularly useful when processes vary, information needs to be interpreted, or there may be different paths to the goal.

The question is therefore not: ‘Where can we deploy an agent?’ The better question is: where do we actually need flexible decision-making within an automated process?

AI agents can not only formulate errors – but also carry them out

PLEASE NOTE: As soon as AI gains access to real-world systems, the risk changes. An incorrect response is quite different from a data record that has been incorrectly altered, an unintended message or a faulty system action.

AI agents therefore require clearly defined access rights, monitoring and approval processes. Data protection and sensitive company data must also be taken into account right from the design stage.

What do AI agents mean for SEO and GEO?

AI agents do not replace either SEO or GEO (Generative Engine Optimisation). What is more important is that AI systems are increasingly able to research and combine information independently and use it to inform further decisions.

This means that clearly structured and factually sound content is becoming even more important. Search and AI systems must be able to clearly understand which topics, services and areas of expertise a brand stands for. For GEO, factors such as entity consistency, grounding, brand mentions, digital authority and extractability all play a role.

SEO remains the foundation. GEO broadens the perspective to consider whether brands and content are also recognised as relevant sources and entities within AI-generated responses.

5 steps to your first AI agent use case

Select process

Identify a recurring task that currently requires a great deal of manual information processing or coordination.

Define the objective

Identify what needs to be improved – such as speed, costs or the quality of processing.

Data and systems determine

Check what information and applications the agent requires.

Setting boundaries

Define which decisions may be taken autonomously and when approval is required.

Measure the pilot

Test the agent initially in a limited use case and assess its quality, faults and economic benefits.

Conclusion: An AI agent should not just respond, but also carry out tasks

AI agents are shifting AI from individual responses towards multi-stage processes involving data access, decision-making and actions. This increases the potential for automation – but also the responsibility for control, authorisation and data quality.

Not every workflow requires an agent. The most useful AI agent is not the most autonomous one, but the one that measurably improves a clearly defined business process.

Frequently Asked Questions about AI Agents

What is an AI agent, explained simply?

An AI agent is an AI system that is given a goal and can independently carry out multiple steps. To do so, it can process information, use tools, and perform actions within defined limits.

What is the difference between an AI agent and ChatGPT?

A chat system first generates responses. An AI agent combines AI with goals, tools, and a decision-making logic to handle tasks that involve multiple steps.

Can AI agents act independently?

Yes, within the scope of their technical permissions. In companies, however, this scope of action should be deliberately limited and supplemented by human approval for critical actions.

What tasks can AI agents handle?

These tasks are particularly well-suited for variable, information-intensive, and multi-step tasks in areas such as sales, customer service, administration, marketing, or analysis.

What is the difference between Agentic AI and an AI agent?

An AI agent is a specific system that pursues goals and performs actions. Agentic AI is a more general term describing the approach of having AI plan and perform tasks with increasing autonomy.