A multi-agent system is a decentralised AI system in which several autonomous AI agents interact with each other in order to solve tasks or achieve goals together.
This sounds relatively abstract at first, which is why we will take a closer look at the areas of application and the purpose of a multi-agent system in this blog article.
Table of Contents
Definition and basic concept
A multi-agent system initially consists of several individual AI agents. Each individual AI agent has a specific specialisation in which it is very good. Depending on the task to be solved, an AI agent is then selected in a multi-agent system to solve the task. However, several AI agents may also have to interact with each other to solve the task. Through interactions, AI agents can exchange information and act cooperatively or competitively.
The aim of a multi-agent system is to solve tasks as efficiently and cost-effectively as possible. There are multi-agent systems because individual AI agents are not flexible enough to solve all the tasks that are set for an generative AI system. This is why several AI agents are used, which divide up the tasks according to their respective skills. This also allows AI systems to be smaller and more efficient, which saves costs.
Example of a multi-agent system
The following is a possible example of a multi-agent system:
A company wants to implement an AI chatbot for its employees. This AI chatbot should solve various tasks. These include, for example:
- writing marketing texts, job advertisements and blog posts
- searching for specific information on a topic
- comparing certain documents
- as a training partner for employees
- as a support contact for customers
- …
A single LLM is basically able to solve the tasks with the help of a retrieval augmented generation system. But it will not be able to perform the respective tasks as well as specialised agents. In a multi-agent system, an AI agent would now decide which AI agent would be selected to solve the challenge.
The prompt “Find me all the information about our customer X!”, for example, would be solved by AI agent 2.
The prompt “Formulate a cold call email to persona Y” would be solved by AI agent 1.
And the prompt “Compare document A with document B” would be solved jointly by agents 2 and 3, as AI agent 2 would search for the two documents and AI agent 3 would then compare the documents found.
Properties of a multi-agent system
To be a multi-agent system, an AI system must be able to demonstrate various properties. These include
- Autonomy: Each agent acts independently and decides on its own what the right action is based on its perceptions and goals.
- Communication: Agents exchange information via protocols and communication mechanisms.
- Cooperation: Agents work together to achieve common goals, often through division of labour and coordination.
- Heterogeneity: Agents can have different skills, knowledge and roles.
Advantages of multi-agent systems
Multi-agent systems offer a number of advantages – also in comparison to single-agent systems:
- Flexibility and scalability: multi-agent systems can dynamically adapt to changing environments by adding, removing or changing agents. This makes them extremely scalable and enables complex problems to be solved efficiently.
- Robustness and reliability: Decentralised control means that the system remains functional even if individual components fail. This increases the robustness and fault tolerance of the system.
- Self-organisation and coordination: Agents can organise themselves on the basis of emergent behaviour patterns, which leads to an effective division of tasks, coordinated decision-making processes and conflict resolution.
- Real-time operation: Immediate reactions to current situations are possible without human monitoring, which enables applications such as disaster rescue and traffic control.
Areas of application
There are various use cases for such systems that do not have to be purely software-driven. Many use cases also work because physical elements, such as cars or robots, are controlled by AI agents. The following examples (in addition to the example given above) should therefore be seen as a small selection of use cases:
- Robotics: co-operative teams of robots working together to perform tasks such as search and rescue missions.
- Traffic control: Intelligent traffic management systems in which vehicles and traffic infrastructures work together.
- E-commerce: Recommendation systems and auctions in which autonomous agents act on behalf of users.
- Game theory and simulation: Models for analysing and predicting complex systems such as financial markets or social networks.
Challenges of multi-agent systems
As with all AI systems – shit in, shit out. A good data structure is therefore very important as the basis for such systems. The following challenges must then be taken into account when designing multi-agent systems:
- Coordination effort: The effective coordination of the activities of many autonomous agents requires complex algorithms and mechanisms to ensure that the agents work together harmoniously and achieve their goals efficiently.
- Conflict resolution: Since agents may have different goals and priorities, conflicts are bound to occur. It is a challenge to develop appropriate conflict resolution strategies that do not affect the overall system.
- Communication costs: Communication between agents can be costly, especially in large systems. It is important to develop efficient communication protocols to minimise data traffic while ensuring reliable information transfer.
- Security: Security in multi-agent systems is critical as malicious agents can disrupt or damage the system. Robust security measures must be implemented to ensure the integrity and confidentiality of data as well as protection against attacks.
The further development of multi-agent systems
In the age of AI, simple chatbots will no longer be sufficient in the medium term and more complex solutions will be needed in companies that can also take over tasks automatically. Realism is already setting in at many companies – a single AI chatbot with its own data will not take anyone’s job or solve tasks independently. Such systems are assistance systems.
The great advantage of such systems lies in their intelligent combination with each other and their integration into the company’s internal processes.