1 Introduction
Over the past decade, the software development field has widely adopted microservices architecture to achieve independent development, scalability, and resilience. This architecture ensures that each service focuses on specific functionalities, avoiding the challenges of managing, scaling, and maintaining large monolithic applications.
In the AI-driven era of agentic systems, we follow the same principle. We do not want AI agents to become overly large and complex, repeating the mistakes of monolithic applications, especially when facing different challenges. When a single agent takes on too many tasks and decision-making responsibilities, it can lead to performance degradation, lack of interpretability, and violations of core software principles such as SOLID, particularly the Single Responsibility Principle (SRP). This situation is akin to having a single operator handle multiple tasks, prompts, and models, resulting in inefficiency and unpredictable outcomes.
2 Understanding Multi-Agent Systems
To address these challenges, we turn to Multi-Agent Systems (MAS), which are structured similarly to distributed systems in software architecture:
- Single Agent
: Similar to a basic conversational chatbot, it interacts with large language models (LLMs) or calls specific tools to complete tasks and provide responses. - Supervisor Agent
: Similar to the SAGA orchestration pattern in microservices, the supervisor agent decides which agent to call next. This is known as agent delegation, where one agent manages tools that can further call other agents to complete tasks. - Hierarchy of Supervisors
: Just as organizations have multi-level management, multi-agent systems can also have supervisors overseeing other supervisors. This enables more complex control flows and decision structures. A real-world analogy is a corporate hierarchy:
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The CEO represents the top-level supervisor agent. -
Subordinate department heads (e.g., finance, marketing, human resources) act as intermediate supervisor agents. -
Each department has dedicated teams or units (individual agents) focused on achieving common organizational goals.
Figure 1: Components and Interaction Patterns of MAS
Figure 1 illustrates the concept of different components and interaction patterns in Multi-Agent Systems (MAS), specifically including the following parts:
- Conversable Agent
: The top left of the figure shows a conversable agent capable of interacting with users. This agent may possess the ability to handle text, voice, or other forms of interaction. - Agent Customization
: The bottom left illustrates the concept of agent customization, meaning agents can be tailored to specific needs or scenarios to adapt to different functions or services. - Multi-Agent Conversations
: The illustration in the top right shows conversations between multiple agents. This indicates that different agents within a system can communicate to accomplish tasks or solve problems together. - Flexible Conversation Patterns
: The bottom right displays flexible conversation patterns, including joint chat and hierarchical chat as different communication structures. These patterns allow agents to interact in various ways to meet complex communication needs.
Figure 1 emphasizes the flexibility and scalability of multi-agent systems during design and implementation.
3 Supervisor Agent
The supervisor agent plays a crucial role in Multi-Agent Systems (MAS), responsible for managing and coordinating the behavior and task allocation of other agents. In this architecture, agents are defined as nodes, with supervisory nodes added to determine the invocation of agents. Conditional edges decide the order and conditions for invoking agents, making the operation of the entire system more flexible and efficient. The supervisor agent can be seen as the SAGA orchestration pattern in microservices, where the supervisory agent decides which agent to call next. This is also known as agent delegation, where one agent manages tools that can, in turn, call another agent to complete tasks.
In Multi-Agent Systems (MAS), the supervisor agent plays a core management and coordination role. It is responsible for several key tasks:
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Task Allocation: The supervisor agent allocates tasks to one or more agents based on the system’s goals and current state. It ensures that tasks are allocated reasonably to optimize resource utilization and improve efficiency.
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Coordination and Collaboration: When multiple agents need to collaborate to complete tasks, the supervisor agent coordinates their actions, ensuring effective communication and cooperation between them. It may need to resolve conflicts or priority issues among agents.
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Monitoring and Evaluation: The supervisor agent monitors the execution of other agents, evaluates their performance, and intervenes when necessary. This includes monitoring whether agents are executing tasks as planned and whether they are achieving expected outcomes.
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Decision Support: In complex or uncertain environments, the supervisor agent provides decision support. It may need to dynamically adjust task allocation or strategies based on real-time data or changing environmental conditions.
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Learning and Adaptation: In some advanced multi-agent systems, the supervisor agent may also possess learning capabilities, enabling it to learn from experience and adapt to new environments or tasks. This allows the system to improve its performance over time.
The design and implementation of supervisor agents can adopt various strategies, such as rule-based systems, machine learning models, or hybrid approaches. In practical applications, the specific functions and complexity of supervisor agents depend on the system’s requirements and goals.
In some designs, such as LangGraph, there are five ways to create multi-agents, one of which is the supervisor mode, where each agent can communicate with a supervisor agent, which decides which agent to invoke next. Additionally, the AWS Bedrock team’s proposed framework adopts a hierarchical design that includes a top-level supervisory structure, where the supervisor agent serves as the decision core responsible for overall task planning and allocation, maintaining the unity of team collaboration. This hierarchical design allows each leaf node agent to focus on its area of expertise while also serving as a supervisor for other expert agents, forming a multi-level collaborative network. The advantage of this design is that each agent only needs to maintain context relevant to its role, avoiding context overload, facilitating independent development and testing of expert agents, and supporting distributed development processes.
4 Hierarchy of Supervisors
In Multi-Agent Systems (MAS), the hierarchy of supervisors refers to organizing supervisor agents into different levels to achieve more effective management and coordination. This hierarchical structure simulates the management hierarchy in human organizations, where each level has specific responsibilities and authorities. The following are key features of the hierarchy of supervisors:
- Hierarchical Structure
: The hierarchy of supervisors typically includes multiple levels, each responsible for different ranges or types of tasks. Top-level supervisors may be responsible for strategic planning and resource allocation for the entire system, while bottom-level supervisors may focus on managing specific tasks or subsystems. - Division of Responsibilities
: Each level of supervisors has clear responsibilities and authorities. High-level supervisors are generally responsible for formulating overall strategies and objectives, while low-level supervisors are responsible for executing specific tasks and operations. - Decision-Making Process
: In the hierarchy of supervisors, the decision-making process is typically top-down. High-level supervisors formulate overall strategies, which are then broken down into specific tasks and objectives executed by low-level supervisors. - Information Flow
: Information flows in the hierarchy of supervisors, reporting execution status and results from lower-level supervisors to higher ones, while high-level supervisors communicate strategies and directives downwards. - Flexibility and Scalability
: The hierarchy of supervisors provides flexibility and scalability, allowing the system to add or remove levels as needed to adapt to different tasks and environments. - Collaboration and Coordination
: In the hierarchy of supervisors, different levels of supervisors need to closely collaborate and coordinate to ensure the smooth execution of tasks and the achievement of objectives.
The hierarchy of supervisors has many advantages in multi-agent systems, such as improving management efficiency, enhancing decision-making capabilities, and promoting collaboration and coordination. However, it also faces challenges, such as excessive levels potentially leading to delays in information transmission and increased coordination costs. Therefore, when designing the hierarchy of supervisors, it is necessary to weigh its advantages and challenges to achieve optimal system performance.
5 Collaboration Among Agents
In Multi-Agent Systems (MAS), coordination and collaboration among agents are achieved through various mechanisms that ensure agents can work together to complete complex tasks. Here are some key coordination and collaboration strategies:
- Task Allocation
: Agents are assigned specific tasks based on their unique expertise and resources to achieve mutually beneficial outcomes. This collaboration is particularly important for tasks requiring collaborative problem-solving, collective decision-making, and complementary skills. - Collaboration Mechanisms
: Defining collaboration mechanisms among agents allows them to work together. This includes sharing information, task allocation, resource scheduling, and conflict resolution. - Decision-Making
: Decision-making among agents aims to achieve final objectives. This may involve negotiation among agents, shared planning, or other collaborative strategies. - Communication Structure
: Agents exchange information through specific communication structures, which can be point-to-point, centralized, or distributed. - Role-Based Protocols
: By assigning specific roles or divisions to each agent, agents can focus on sub-tasks within their areas of expertise. This strategy improves the efficiency and structure of the system. - Model-Based Protocols
: Provide flexibility in decision-making in environments where input perception is uncertain. Agents make probabilistic decisions based on their perception of the environment, shared goals, and inherent uncertainties. - Centralized Structure
: All agents are connected to a central agent responsible for managing and coordinating interactions among agents. - Distributed Structure
: Control and decision-making authority are distributed among multiple agents, with each agent operating based on local information and limited communication. - Hierarchical Structure
: In this structure, different levels of supervisor agents exist, with high-level agents managing and coordinating the activities of lower-level agents.
These strategies and structures work together to enable multi-agent systems to effectively coordinate and collaborate to achieve collective goals.
6 Architecture of Multi-Agent Systems (MAS)
The architecture design of Multi-Agent Systems (MAS) needs to consider the interactions, collaborations, and how to manage and coordinate these agents. An effective MAS architecture typically includes the following key components:
- Agents
: Agents are the basic units in MAS, which can be software agents, robots, or other computational entities. Each agent has a certain degree of autonomy, capable of perceiving the environment, making decisions, and executing actions. - Environment
: Agents interact with the environment. The environment can be physical or virtual, providing information to agents and receiving their actions. - Communication Mechanism
: Agents need a communication mechanism to exchange information. This can be direct messaging, shared databases, or other communication protocols. - Task Allocation
: In MAS, the task allocation mechanism determines which agent performs which task. This can be static or dynamic, depending on the system’s design and requirements. - Coordination and Collaboration
: Agents need to coordinate and collaborate to complete tasks together. This may involve negotiation, shared planning, or other collaborative strategies. - Hierarchy of Supervisors
: As mentioned earlier, the hierarchy of supervisors involves different levels of agents, with high-level agents managing and coordinating the activities of lower-level agents. - Learning Mechanism
: MAS may include learning mechanisms that allow agents to learn from experience and improve their behavior. - Fault Recovery and Fault Tolerance
: The system needs to handle failures of agents or communication links and ensure robustness and reliability. - User Interface
: For MAS requiring user interaction, the user interface allows users to interact with the system, monitor agent behavior, or provide input. - Evaluation and Monitoring
: The system needs to evaluate agent performance and monitor the operational status to ensure goal achievement and system optimization.
When designing MAS architecture, scalability, flexibility, and maintainability of the system must be considered. Additionally, the heterogeneity of agents should be taken into account, as the system may include different types of agents with varying capabilities, knowledge, and behavior patterns. With careful design, MAS can solve complex problems, enhance decision quality, and perform excellently in dynamic and uncertain environments.
7 Conclusion
In this discussion, I have delved into the concepts, architecture, coordination, and collaboration mechanisms of Multi-Agent Systems (MAS), as well as their applications in modern artificial intelligence. Multi-Agent Systems provide an effective way to solve complex problems by simulating group behaviors found in nature and society. Just as microservices have fundamentally changed software architecture, MAS can achieve tasks that individual agents cannot accomplish through coordination and collaboration, demonstrating powerful collective intelligence. As technology continues to advance, MAS will find applications in more fields, bringing greater convenience and value to human society.