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 an AI-powered software system that pursues a specified objective and, within defined limits, can independently plan tasks, process information, use tools and carry out actions.
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.
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.
How does an AI agent work?
Put simply, an AI agent operates in a loop consisting of understanding, planning, acting and checking.
Four distinct process steps can be identified:
- Understanding the objective and context: The agent receives an objective, along with information about the current task and relevant conditions.
- Planning the approach: It determines which intermediate steps are necessary and what data or tools it requires to carry them out.
- Using tools and carrying out actions: Via APIs or other interfaces, it can, for example, retrieve data, process documents or trigger actions in applications.
- Evaluate the result and determine the next step: The agent assesses the result of its action and decides whether the objective has been achieved or whether further steps are required.
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 to some extent and differ primarily in terms of the degree of autonomy. The decisive factor is which tasks, decisions and actions a system can carry out independently.
In practice, the boundaries between chatbots, AI assistants and AI agents are blurred. When classifying them, therefore, it is the system’s actual range of functions that matters more than the product name.
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.
How is an AI agent structured?
An AI agent consists of several components which, together, enable it to process information, make decisions and carry out actions. Which components are actually present depends on the specific system and its intended use.
- Decision logic: This processes information and determines which step should be carried out next. In modern AI agents, this function is often performed by an AI model or a large language model.
- Objective and instructions: These define the task the agent is to perform, the rules that apply and the limits within which it is permitted to act.
- Context and state: The agent requires information about the current task, its environment and steps already carried out in order to make decisions within the relevant context.
- Tools and interfaces: An AI agent can access external information and carry out actions in other systems via APIs, databases or other applications.
- Memory: A memory can retain information across multiple steps or interactions. However, not every AI agent requires a permanent memory.
- Authorisations and protection mechanisms: These determine which data and functions the agent is permitted to access and which actions are allowed.
Not every AI agent has all of these components. The specific architecture depends on the tasks the agent is to perform and the degree of autonomy it is required to operate with.
What types of AI agents are there?
There is no universally accepted classification of AI agents. Depending on the theoretical model, agents are distinguished, for example, by their decision-making logic, their memory or their degree of autonomy.
A distinction can be made, amongst other things, between the following types:
Reactive agents: Respond to the current state and make decisions without extensive long-term planning.
Goal-based agents: Align their actions with a defined goal and select appropriate steps to achieve it.
Planning agents: Break down more complex tasks into several steps and adapt their plan based on interim results.
Learning agents: Can utilise experience or feedback to adapt their future behaviour. However, not every AI agent learns automatically.
Multi-agent systems: Several specialised agents work together and take on different sub-tasks
The categories are not always clearly distinct: an AI agent may exhibit characteristics of several types simultaneously.
When does an AI agent make sense – and when is automation sufficient?
For clearly defined and recurring ‘if-then’ processes, traditional workflow automation is often sufficient. It is particularly well suited to workflows with fixed rules and predictable process steps.
An AI agent is particularly useful when workflows vary, information needs to be interpreted, or several possible courses of action can lead to the same outcome. Its use is particularly appropriate where flexible decisions are required within an automated process.
The key factor, therefore, is not whether a process can, in principle, be automated, but rather what degree of flexibility and independent decision-making capability is required.
Question | More like an AI agent | More like automation |
|---|---|---|
Do the input data and situations differ? | ✅ | ❌ |
Does information need to be interpreted? | ✅ | ❌ |
Are there different ways to reach the goal? | ✅ | ❌ |
Do we need to integrate multiple systems? | ✅ | in part |
Is every step in the process exactly predictable? | ❌ | ✅ |
Can the process be described entirely using if-then rules? | ❌ | ✅ |
The greater the need for interpretation, context and situation-dependent decisions, the more worthwhile an agent becomes. The more predictable the process is, the more often traditional automation is the better solution.
What are the limitations and risks associated with AI agents?
Depending on how they are designed, AI agents can not only process information, but also access systems and carry out actions. As a result, errors can have immediate consequences.
- Erroneous decisions: AI agents may misinterpret information or choose inappropriate courses of action. Their results are therefore not automatically correct.
- Excessive permissions: An agent should only be able to access the data and functions it needs to carry out its task.
- Data protection: When processing personal or confidential data, data protection requirements and access rights must be taken into account.
- Human oversight: For sensitive or irreversible actions, approvals and technical safeguards may be necessary.
The appropriate level of autonomy therefore always depends on the specific application and potential risks.
Conclusion: An AI agent should not just respond, but also carry out tasks
AI agents differ from purely dialogue-based AI systems primarily in that they can plan tasks, deploy tools and carry out actions within defined limits. The degree to which they act autonomously depends on their technical design and the permissions granted to them.
Whether an AI agent is appropriate depends on whether a task requires flexible decision-making, context processing and access to different tools or systems. For rigid and predictable processes, traditional automation is often the simpler solution.
Frequently Asked Questions about AI Agents
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.
ChatGPT is an AI product that, depending on its function, can perform both traditional assistant tasks and agent-based tasks. An AI agent, on the other hand, generally refers to an AI system that pursues a goal, plans multiple steps, uses tools or external systems, and can carry out actions independently within defined limits. What matters most, therefore, is not so much the product name as the degree of autonomy and capacity for action.
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.
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.
That depends on the specific use case, the data being processed, the service providers involved, and the access rights. Data protection must therefore be part of the design from the very beginning.
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.
