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Agent systems leverage large language models (LLMs) to determine application control flow. However, as system complexity increases, managing and scaling these systems can become challenging. For instance, you may encounter the following issues:
  • Tool selection complexity: The abundance of tools available to agents makes it inefficient to determine which tool to invoke next.
  • Context management difficulties: A single agent may struggle to handle complex contextual information effectively.
  • Specialization requirements: The system may require expertise in various specialized domains, such as planning, research, or mathematics.
To address these challenges, applications can be decomposed into smaller, independent agents and integrated into a multi-agent system. These individual agents can range from simple setups with just a single prompt and an LLM call to advanced architectures like ReAct agents. Key advantages of adopting a multi-agent system include:
  • Modularity: Breaking functionalities into independent agents simplifies development, testing, and maintenance, enhancing system flexibility.
  • Specialization: Creating domain-specific expert agents improves overall system performance and efficiency.
  • Controllability: Communication between agents can be explicitly managed without relying on implicit mechanisms like function calls.
Multi-agent systems empower businesses to address complex scenarios, enhance scalability, and achieve specialized capabilities, thereby better supporting business objectives.

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Multi-Agent Architectures

Set as sensitive In a multi-agent system, agents can be connected and coordinated in various ways, with the choice of architecture depending on business needs and application scenarios. Common multi-agent architecture patterns include:
  1. Network Architecture
    • Each agent can communicate with all other agents.
    • Any agent can autonomously determine the target agent to invoke next.
    • Suitable for decentralized, high-flexibility scenarios.
  2. Supervisor Architecture
    • Each agent communicates only with a supervisor agent.
    • The supervisor agent makes decisions and determines the next agent to invoke.
    • Ideal for centralized management and clear decision-making logic.
  3. Hierarchical Architecture
    • Builds on the supervisor architecture by introducing multi-level supervisor agents, creating a hierarchy.
    • Supports complex control flows and modular management for multi-level business logic.
  4. Custom Multi-Agent Workflows
    • Each agent communicates only with specific subsets of other agents.
    • Some workflows are deterministic, while some agents autonomously decide their next targets.
    • Highly flexible, suitable for specific logic or complex business rules.
By selecting the appropriate architecture, multi-agent systems can optimize coordination efficiency and execution performance, addressing the demands of complex business scenarios while improving scalability and reliability.

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Digital Expert Systems

Our digital expert system employs a hierarchical architecture, creating an efficient and scalable multi-agent system. Each digital expert within this system is itself a hierarchical multi-agent system. Here are the core reasons we chose this architecture:

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1. Adaptability to Complex Business Needs

The hierarchical architecture introduces multi-level supervisor agents to handle multi-layered logic requirements in complex business scenarios. Each level focuses on specific tasks, with higher-level supervisors coordinating the overall system, enabling flexible handling of task decomposition, resource allocation, and workflow management.

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2. Enhanced Modularity and Maintainability

The system is divided into clear hierarchical levels, with well-defined functions and responsibilities. This modular design simplifies development, testing, and maintenance, supporting rapid iteration and feature expansion.

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3. Improved Decision Efficiency and Control

By incorporating supervisor agents and multi-level control, the system coordinates agent behavior across levels, reducing unnecessary communication and redundant decisions. This boosts decision efficiency and ensures system actions align with global objectives.

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4. Support for Specialization and Collaboration

In the hierarchical architecture, each level can specialize in specific domains or tasks—for example, one level might handle data processing while another focuses on business logic optimization. Collaboration across levels enhances overall system performance and ensures domain-specific expertise.

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5. Scalability to Meet Business Growth

As business requirements evolve, the hierarchical architecture can easily scale horizontally or vertically by adding or adjusting supervisor agents, meeting diverse future needs. Our choice of a hierarchical architecture reflects our deep understanding of complex business scenarios and organizational requirements. This architecture enables our digital expert system to effectively solve complex problems, offering flexibility, stability, and scalability in real-world applications, ultimately driving greater value for businesses.