Understanding Multiagent Systems: How AI Systems Coordinate and Collaborate

In a world increasingly reliant on automation and artificial intelligence, Multiagent Systems are becoming essential for building complex large language models (LLMs) or multimodal models. These systems are capable of tackling challenges that are beyond the scope of a single AI agent.

From coordinating fleets of autonomous vehicles to optimizing supply chains and enabling swarm robotics, these intelligent agents are transforming industries. This blog explores the core concepts, types, real-world applications, and best practices for developing effective multiagent systems, providing insights into how they enable smarter collaboration and decision-making.

What are Multiagent Systems?

Multiagent Systems(MAS) consist of multiple AI agents that interact within a shared environment. These systems are built to solve problems that are complex for a single agent to handle.

Example of a LLM based Multiagent system. Source

Core Components

• Agents: They are independent entities with specific objectives. They are able to understand their environment, make decisions, and execute actions to achieve their objective. E.g., software programs, or sensors.
• Environment: The environment is the dynamic space where agents operate. It can be physical like a factory floor or virtual like a simulation. The environment’s properties, such as accessibility and predictability, influence the agent's behavior.
• Communication: This allows the agents to share information and coordinate their actions. These mechanisms can be direct like message passing or indirect like modifying the environment, also known as stigmergy.

Key Concepts

Agent Autonomy

This refers to an agent’s ability to make decisions without any external control. It involves sensing the environment, processing information, and executing actions to achieve its specific objectives. Autonomous agents improve MAS by reducing the need for centralized oversight, improving adaptability and efficiency.

Decentralization

Each agent operates based on local information and interactions with other agents. This design enhances the system's scalability, as new agents can be added without requiring significant reconfiguration. It also improves fault tolerance, as the failure of one agent does not compromise the entire system.

Emergent Behavior

This occurs when interactions among simple agents lead to complex system-wide changes that are not explicitly programmed. For example, in swarm robotics, individual robots follow basic rules, such as maintaining a certain distance from neighbors, resulting in coordinated group behaviors like flocking or obstacle avoidance. Emergent behaviors are essential for problem-solving in dynamic and unpredictable environments.

Types of Multiagent AI Systems

Cooperative Systems

In this, agents come together to achieve a common goal. Each agent’s actions add to the collective outcome, with coordination mechanisms ensuring efficiency and conflict resolution. For example, search-and-rescue operations, where multiple drones work together to locate survivors.

Competitive Systems

In competitive MAS, agents have conflicting goals and aim to maximize individual outcomes, often at the expense of others. These systems are commonly seen in applications like stock trading, where agents compete for market advantage, or in adversarial game simulations.

Mixed Systems

Mixed MAS have both cooperation and competition. Agents might collaborate in some aspects while competing in others. For instance, autonomous vehicles may share traffic data to avoid congestion (cooperation) while simultaneously looking for optimal routes to reduce travel time (competition).

Hybrid Systems

This is a blend of traditional rule-based logic with adaptive learning methods. These systems allow agents to follow preprogrammed rules while using machine learning to improve the decision making process over time. For example, in a smart grid, agents may follow rules for energy distribution while learning user consumption patterns to optimize efficiency.

Real World Use Cases

Here are some of the multi agent-based applications in various domains:

• Autonomous Vehicles: Multiagent systems coordinate fleets of autonomous cars to manage traffic, optimize routes, and prevent accidents through real-time communication and decentralized decision-making.
• Robotics: Swarm robotics use MAS principles to deploy set of robots for tasks like warehouse automation, environmental monitoring, and disaster response.
• Healthcare Systems: MAS assist in patient monitoring, and resource allocation in hospitals for efficient scheduling and treatment delivery.
• Distributed Sensor Networks: MAS enhance environmental monitoring, surveillance, and disaster management by enabling sensors to collaborate and share data.
• Gaming: MAS are used in multiplayer games and simulations for realistic behavior modeling of non-player characters (NPCs) or for training purposes in defense and urban planning.
• Financial Systems: Automated trading platforms use multiagent systems for competitive interactions between AI agents to maximize profits and analyze market trends.
• Supply Chain Management: MAS optimize logistics by coordinating tasks such as inventory management, demand forecasting, and delivery scheduling across multiple AI agents.

Some generative AI applications of MAS. Source

Single Agent vs. Multiagent systems

Single Agent Systems

As the name suggests, these systems have one autonomous agent for a specific task. They are common where the environment is static and the objective is not complex and well defined. For example, recommendation systems.

Multiagent Systems

These distributed systems have more than one autonomous agent in a shared environment. Each agent can either have its own specific goal or work with other agents towards a collective goal. Example, drones working together to survey an area, or autonomous bidding agents in auctions.

Source

Challenges in Training Multiagent AI Systems

It can be tricky training multi-agent systems since there are different agents interacting with each other in the same environment. Here are some of the common challenges:

• Scalability: As the number of agents increases, the computational need for communication between agents also increases.
• Dynamic Environments: Each agent’s actions changes the ecosystem. Now these constant changes and external factors make it difficult to predict outcomes or develop consistent strategies.
• Credit Assignment: Each agent’s actions are accounted for. Determining which agent’s actions led to success or failure is challenging especially in cooperative tasks where contributions are added up.
• Communication Bottlenecks: Agents often rely on communication to coordinate, but limited bandwidth, high latency, or long and complex messages can slow down decision making.
• Evaluation Metrics: Measuring the performance of multi-agent systems is complex, as it must account for individual agent goals, overall system efficiency, and fairness among agents.

How Encord Supports Multiagent System Development

Encord is a data annotation platform designed to support the training of machine learning models and multiagent systems. It provides tools to manage and curate multimodal datasets. It helps with large-scale data annotation, designing workflows, and integrating it into machine learning pipelines.

Here are some of the key features of Encord that help in building MAS:

• High-Quality Annotated Data: With support for all modalities, features like ontology, and tools like Encord Active to visualize, and quality metrics to find labeling mistakes, this platform can handle complex data annotation while ensuring precision.
• Scalability and Efficiency: Training multiagent systems often requires managing large amounts of data. Encord is built to scale, allowing you to work with large datasets that are necessary for effective training. It also supports parallel annotation pipelines, allowing multiple tasks to run at once, which speeds up the process of preparing data for training.
• Effective Collaboration: With custom workflows, the platform makes it easy for distributed teams to work on data annotation. 

Practical Steps to Build Effective Multiagent Systems

Define Objective of Each Agent

For building a multiagent system, the first step is to assign each agent with specific goals and responsibilities. Whether agents are cooperating, competing, or performing independent tasks, their objectives should be clearly outlined. The goal of the overall system should also be defined in order to assign tasks to each agent and to calculate the number of agents required.

Design Environment and Interaction Rules

The ecosystem  where the agents are to interact should be created next. This includes defining how the agents interact with each other, the environment, and the set of rules that govern these interactions. 

Choose Learning Algorithm

Here, select the learning algorithm based on the objective of the system. If the agents need to collaborate, multi agent reinforcement learning or MARL algorithms like QMIX can be chosen. For competitive scenarios, consider algorithms that can handle adversarial behaviors like Nash equilibrium.

Annotate and Simulate

Cure and annotate the data for training that reflects the real world scenario in which the agents will operate. Using tools like Encord can help in data curation, management, and annotation of high quality training and testing data. This is important for building agents that can handle complex tasks and dynamic environments.

Train the Agents

Once the environment and data are set up, begin training the agents. Use AI to allow the agents to learn real-time decision making from their interactions and experiences. This is where the real learning happens, as agents adjust their behavior based on rewards and punishments.

Test and Iterate

Testing is important to evaluate how well the agents are performing. Simulate different scenarios close to real world scenarios to see how the agents respond, and adjust the rules, training data, or the learning algorithm.

Deploy and Monitor

After training and testing, deploy the MAS in a real-world or production ecosystem. Monitor the system’s performance regularly to ensure the agents are behaving as expected.

Popular Learning Algorithms Used in Multiagent Systems

Multiagent Reinforcement Learning(MARL)

MARL is a key approach in multiagent systems where agents learn by interacting with the environment and the other agents. Here, each agent receives feedback based on its actions and the environment like in RL. The objective of the overall system is to maximize individual or group rewards over the time by improving the interaction rules.

Common MARL Algorithms

• Independent Q-Learning (IQL): In this each agent treats other agents as part of the environment and learns independently using Q-learning. IQL struggles in environments with many agent interactions.
• Proximal Policy Optimization (PPO): It is a RL algorithm that focuses on policy or rule optimization. It works well in both cooperative and competitive environments and is used in training agents in multi-agent scenarios like games or robotics.
• QMIX: This is a centralized training approach for multi-agent systems where a global reward function is used to train the agents individually. QMIX is designed to handle environments where agents work together toward a shared objective.

Centralized Training with Decentralized Execution (CTDE)

CTDE is a strategy used to train agents in a cooperative environment while ensuring that each agent acts independently during execution. The main idea behind it is to have a centralized controller that overlooks the training and helps the systems learn the necessary agent behaviors. However, during actual operation, agents rely on their local observations to make decisions.

Common CTDE Algorithms

• Multi Agent Deep Deterministic Policy Gradient: In this algorithm, during training agents have access to the observations of all agents but during execution, each agent uses only its own observations to make decisions. This works well for a collaborative approach.
• Value Decomposition Networks(VDN): This approach decomposes the global value function into individual value functions, making it easier for agents to cooperate without requiring a complex global reward structure. It is particularly useful in environments where agents need to act as a team but do not have direct communication with each other during execution.

Game Theory Based Algorithms

Game theory is a mathematical framework for analyze interactions between agents with conflicting interests. In MAS, this algorithm helps agents to make strategic decisions when they are in adversarial conditions.

Common Game Theory Algorithms

• Nash Equilibrium: In competitive scenarios, a Nash equilibrium represents a set of strategies where no agent can improve its payoff by unilaterally changing its own strategy. The agents use this algorithm to predict how their competitors will behave and adjust their actions and rules accordingly.
• Fictitious Play: This iterative algorithm allows agents to learn and adapt to the strategies of other agents over time. In each iteration, agents update their strategies based on the belief about the opponent's strategy.

Swarm Intelligent Algorithms(SIA)

SIAs are a class of search algorithms that are inspired by the collective behaviour of decentralized systems, like birds flocking. These algorithms allow agents to collaborate in a distributed manner, and solving complex problems without a centralized control.

Common SIAs

• Particle Swarm Optimization(PSO): In this technique, the agents simulate the social behaviour of birds to achieve the adjective. Each agent adjusts its position based on its previous experience and the best solution found by the group. It is mostly used in route planning in traffic flow.

Best Practices for Building Multiagent Systems

Here are some of the tips to keep in mind when implementing multiagent systems:

Design a Realistic and Adaptable Environment

Make sure to build the environments which mimic the real world conditions the agents will use. This will help the agents to learn how to behave in unpredictable scenarios better. Platforms like Unity can be used to simulate complex environments for testing.

Use Scalable Communication Strategies

The agent communication methods should be efficient, minimal and scalable. Unnecessary communication protocols can cause computational overload when the number of agents are increased.

Robust Credit Assignment Mechanisms

Identify which agent actions lead to success or failure using credit assignment methods like Shapley Value. This ensures fair rewards and accountability in agent collaboration tasks.

Efficient Data Annotation Tools

Use annotated datasets that capture agent interactions and environment complexity. Tools like Encord streamline dataset preparation, improving training efficiency.

Prioritize Ethical and Safe Deployments

Ensure agents follow ethical and safety guidelines, especially in critical areas like healthcare or autonomous vehicles. Safeguards help prevent unintended or harmful behaviors.

Conclusion

Multiagent systems(MAS) offer powerful solutions for complex problems. They use autonomous agents to work together or independently in dynamic environments. Their applications span industries like robotics, healthcare, and transportation, showing their advancements in adaptability and scalability.

By defining clear objectives, designing realistic environments, and with tools like Encord for efficient data preparation, developers can create systems that are both effective and ethical.

Start building multiagent systems today and explore their potential in solving real-world challenges.