What Are AI Agents?
AI agents are intelligent systems based on artificial intelligence and machine learning (ML). These technologies enable AI agents to react flexibly to changes and learn continuously. In contrast to simple language models (LLMs) such as GPT, AI agents are designed to carry out specific tasks and some even set themselves follow-up tasks independently.
Once equipped with clear goals and guidelines from humans, they can complete tasks autonomously without constantly needing new instructions. They plan their processes themselves and use various tools such as APIs, databases, search engines or communication platforms to solve problems or answer questions.
To understand natural language and respond specifically to queries, many AI agents use large language models (LLMs) - which is why they are also called LLM agents. Others use rule-based systems or special ML models. Whether as software in customer service or built into devices such as robot hoovers, AI agents make their own decisions and work autonomously.
How Do AI Agents Work?
The exact functionality of an AI agent depends on the respective implementation. Therefore, here is a more detailed description of how moinAI AI agents work:
.png)
moinAI's AI agents work efficiently and intelligently. They combine AI-supported searches with real-time data from the customer system in order to respond quickly and precisely to enquiries. In doing so, they always take into account the context and previous interactions with the customer. Depending on the enquiry, they decide whether to display content such as forms or topics or retrieve answers directly from a knowledge database. The process can be simplified as follows:
Example enquiry: ‘I need to have my credit card blocked’
1. Request is received:
The standard AI agent receives the customer's enquiry and analyses the content in order to understand the request. It immediately recognises that there is a problem with the credit card
2. Forwarding to special agents:
Since it is a specific concern, the standard AI agent forwards the request to the appropriate special agent - the ‘credit card problems’ agent who specialises in such cases.
3. Real-time data is retrieved:
The special AI agent retrieves real-time data from the customer system via an interface, such as the current credit card status and possible blocking options.
4. Responses are generated:
Based on the retrieved data, the special AI agent creates a precise response. It could:
- Trigger the blocking of the card directly (external process).
- Provide information on further steps, such as ordering a replacement card or contacting customer service.
5. Tools support the process:
To trigger external processes such as card blocking or to collect additional information such as checking for suspicious activity, the AI agent uses various tools such as webhooks or data extraction.
6. Customisation:
The AI agent adapts the response to the right tone through customised instructions – depending on factors such as:
- Urgency of the enquiry (e.g. suspected fraud).
- Customer's previous interaction to ensure consistent communication.
In the end, the customer receives a fast, accurate and personalised response - without long waits or manual intervention.
Do AI Agents Work Completely Without Human Assistance?
No, AI agents cannot work completely without human assistance. Although they are able to perform many tasks autonomously, make decisions and learn from experience, they still need human input to work effectively.
Clear goals and objectives: Humans define the goals and tasks that AI agents should fulfil. Without these clear guidelines, AI agents cannot act in a meaningful way.
Monitoring and control: To ensure that AI agents do not make any undesirable decisions, they must be regularly monitored and controlled.
Training and optimisation: AI agents learn from data and experience. Humans are responsible for providing this data, monitoring training and continuously improving the intelligent agents.
Help with difficult cases: If AI agents reach their limits or are unable to solve complex problems, they must be able to forward the requests to human employees.
Guardrails: To ensure that AI agents act ethically and safely, humans implement so-called guardrails. These protective mechanisms guarantee that the AI agents always remain within predefined, safe and ethical boundaries and do not perform any harmful or unwanted actions.
AI agents are therefore powerful tools that can automate many tasks, but they do not replace human intuition, creativity and judgement. As is so often the case, close collaboration between humans and machines is the key to achieving optimal results.
What Is the Difference Between an AI Chatbot and an AI Agent?
AI chatbots are designed primarily to respond to user enquiries. They use natural language processing (NLP) and generative AI (at least in the case of moinAI) to dynamically organise conversations and respond to various requests - for example in customer service or digital product consultations. However, chatbots are programmed to fulfill specific tasks and follow a clearly defined framework.
They often act as generalists who ensure that the right specialised AI agents are used. They could be described as meta-agents that take over the distribution of tasks in order to achieve the best possible result. At moinAI, for example, the chatbot ensures that specialised AI agents work together perfectly to provide customers with the best possible service.
Although AI agents are also specialised in certain tasks and follow human-defined parameters, they can act much more autonomously. They learn independently from their interactions and thus continuously expand their knowledge. This ability to learn enables them to become more dynamic and also solve more complex, individual problems.
To do this, they use various tools and data sources to expand their knowledge and work more efficiently.
Example: An AI chatbot on a travel website that receives user enquiries, such as information on flight availability or hotel bookings, and then uses the appropriate specialised AI agent to provide detailed information or complete the booking.
The transition between AI chatbots and AI agents is therefore rather fluid and should not be seen as a clear separation - chatbots can use specialised AI agents to complete tasks and AI agents can also work without a chatbot to control complex, dynamic processes.
Practical Examples and Use Cases for AI Agents
AI agents are true all-rounders and therefore have exciting applications in many areas. Here are some examples of how they are used in practice:
- Healthcare: As virtual healthcare assistants, AI agents support symptom checking, patient record management and appointment scheduling, making it easier to access medical care.
- Surgery: An AI agent can even provide support in the operating theatre - for example as a robot such as the da Vinci Surgical System, which enables precise, minimally invasive procedures. Here, the AI agent does not operate itself, but helps with the planning and thus improves the surgeon's accuracy and control.
- Finance: AI agents analyse transactions in real time, identify suspicious activity and assist with risk assessment and portfolio optimisation.
- E-commerce: As personalised shopping advisors, AI agents independently suggest products to customers that match their previous purchasing habits. For example, if a customer often buys sportswear, the AI agent can recommend similar products or new collections that match their shopping behaviour. This makes the shopping experience more personalised and targeted.
- Manufacturing: In industry, AI agents monitor other machines to predict maintenance needs and minimise downtime.
- Education: As virtual tutors, AI agents autonomously adapt learning content to the progress and needs of students.
- Insurance industry: AI agents take over the complete processing of claims. They analyse submitted documents, assess the amount of damage and make independent decisions about payouts.
- Marketing: AI agents can independently plan, execute and optimise marketing campaigns by reacting to data in real time and making adjustments to maximise effectiveness.
What Are the Benefits of AI Agents?
AI agents offer numerous advantages that can significantly improve not only customer service, but also overall company management. For example, AI agents are true multitasking professionals as they can process several enquiries at the same time. This shortens response times and frees up employees, leaving more time for more important tasks. And this also has a direct impact on customer satisfaction: Nobody likes waiting for answers, but AI agents respond quickly, accurately and can even provide personalized answers which helps to keep customers coming back.
Another plus point is round-the-clock availability. Day or night, AI agents are always there to help. This means that customers receive support at any time, no matter where they are or what time of day they have a problem. AI agents also collect data-based insights. They analyse valuable data that helps companies to better understand customer behaviour and adapt offers in a targeted manner without the need for extra effort.
AI agents also take over the automation of tasks. They take care of routine tasks such as sorting enquiries or searching through data - and that saves a lot of time. This leaves more room for the really important things. Less manual work also means fewer errors and lower costs.
Another major advantage is scalability. AI agents adapt easily to increasing workloads and support companies as they grow - without any additional resources. Last but not least, AI agents are easy to integrate. They can be seamlessly integrated into existing systems and databases, which makes implementation simple and cost-effective and further increases efficiency.
What Are the Disadvantages of AI Agents?
Despite all their capabilities and advantages, AI agents are not infallible - what technology is? That's why it's important to be aware of their disadvantages:
Dependence on multiple agents: For more complex tasks, AI agents often require the knowledge and skills of multiple agents. If one of these agents fails, the entire system can falter. It is therefore important that the agents are well trained and regularly checked for their functionality.
Infinite feedback loops: Some AI agents can get into an infinite loop if they don't have clear objectives or reflection on their results. Without human control, they may run the same process over and over again, wasting not only time but also resources.
Complexity and costs: Depending on the technology used and the use case, the development of powerful AI agents can be complex and expensive - especially if a lot of training time and computing power is required. However, this is not always the case. At moinAI, for example, the focus is on particularly efficient AI agents that require little maintenance and hardly any computing power.
Susceptibility to errors: Despite their capabilities, AI agents are not perfect - especially with complex tasks or nuances that are easily recognisable to humans, errors can occur that are difficult for the AI to comprehend.
Need for human supervision: Even if AI agents can complete many tasks independently, they still need a human eye in some cases. Especially for more complex or delicate tasks, it is important that humans continue to keep an eye on the process to avoid errors.
Conclusion: AI Agents as Efficient Helpers
To summarise: AI agents are real all-rounders and have the potential to make work easier for companies - in more and more areas. They have already become indispensable, particularly in customer service and product advice. And with every further development, AI agents are becoming smarter and smarter and will soon be taking on even more complex tasks. They may soon be able to support processes that we cannot even imagine today, opening up new creative possibilities. The future with AI agents looks exciting – and we can look forward to seeing where the journey takes us.
