The evolution of artificial intelligence (AI) and GenAI in particular has opened doors to amazing possibilities, including multi-agent systems. These systems, inspired by the way humans and other organisms interact and coordinate their actions, offer a fascinating vision of how AI can operate in complex environments.
What are Multi-Agent Systems?
In the context of artificial intelligence (AI), the concept of “agent” is fundamental. An agent is an autonomous entity that perceives its environment, makes decisions, and takes actions to achieve its goals. Agents can be both physical (e.g. robots) and virtual (e.g. chatbots) and the same thing applies to the environment in which they operate.
Multi-Agent Systems (MAS) represent a new architectural pattern that involves multiple agents interacting with each other in a common environment. Each agent can possess autonomous knowledge, goals, and capabilities, and acts independently to achieve its goals while affecting its surroundings and other agents.
MAS are based on a distributed architecture, where each agent is an autonomous entity with the ability to perceive the environment, process information and make decisions. These agents can communicate with each other to exchange knowledge or coordinate actions.
How does a Multi-Agent system work?
To effectively design a multi-agent system requires an in-depth understanding of the technologies and methodologies involved. These systems can be classified into two categories: competitive and cooperative.
- In cooperative systems, agents work together to achieve a common goal or to maximize a collective benefit. Cooperation between agents is essential and is based on the sharing of information, resources and strategies.
- In competitive systems, agents have conflicting or independent goals. Each agent tries to maximize its own benefit, often to the detriment of others. Competition can be direct or indirect, and agents can use strategies to outcompete rivals.
A classic example of multi-agent architecture is an urban traffic management system where multiple agents collaborate to coordinate traffic lights based on information collected through traffic sensors.
Not necessarily all elements of a system must implement AI logic internally. It is in fact possible to create complex systems in which intelligent agents collaborate with other deterministic agents.
Multi-Agent systems and LLMs
The integration of Large Language Models into Multi-Agent Systems represents a significant step forward in the evolution of artificial intelligence. This synergy combines the flexibility and adaptability of MAS with the analytical and generative power of LLM, creating more effective, responsive and innovative intelligent systems.
Here is a series of cases in which it may be useful to combine MAS with an LLM:
- One of the main challenges of multi-agent systems is communication between agents. In these contexts, LLMs’ ability to use natural language is very useful for both generating and interpreting messages.
- Agents in MAS often have to make complex decisions based on complex, incomplete, or uncertain information. LLMs can support this process through their capacity for synthesis, understanding of context and prediction.
- The dynamism of the environments in which MAS must operate forces them to make real-time decisions and develop ever-changing strategies. Here too, adopting an LLM can help solve the problem.
What are the advantages of MAS?
Multi-agent systems have numerous advantages compared to classic AI systems:
- A distributed architecture is inherently more scalable than monolithic systems. It is in fact possible to assign resources in a targeted manner to the system components that need them most.
- Each agent can use the technology best suited to their purpose. This fits very well with the large number of LLMs available. It is in fact possible to choose the most suitable model based on the type of requests that the agent must manage as well as being able to use multiple models within the same system.
- Independent agents just easier to track.
In summary, multi-agent systems represent a way to model the interaction and cooperation between intelligent entities. MAS leads us towards a future where AI can not only solve individual problems, but also collaborate effectively to tackle increasingly complex challenges.