Multi-Agent: The Commander and Rational Division of Labor in the AI Era

Multi-Agent (多智能体) = Rational Division of Labor (合理分工)1In March 1776,Adam Smithfirst proposed the concept ofdivision of laborin his bookThe Wealth of Nations, explaining the significant role of division of labor in improving labor productivity.In the early 20th century, Henry Forddivided the production of a car into 8,772work hours, and the assembly line significantly reduced production costs, making it affordable for the working class at that time.The premise of division of labor is: there is an invisible “commander” coordinating all processes.A smartphone or a car contains thousands of components, with multiple suppliers responsible for production; the “supply chain manager” acts as the “commander” in this process.Specialized division of labor promotes social production efficiency, and the Agent model in the AI era is no different.Complex tasks are broken down into different Agents forming a Team, and Langchain defines several different architectures:

Source: https://langchain-ai.github.io/langgraph/concepts/multi_agent/#multi-agent-architectures

Among them, the Supervisor is a better approach; simply put, it is an AI Agent team for solving multiple tasks, where a “commander” is responsible for scheduling which Agent to use for which problem.The Multi-AgentSupervisor model = Wise Commander + Rational Division of LaborByte’s open-source Deep Research project belongs to this model.Trends in Agent Development: Solo, Symphony Orchestra, Open Organization

https://www.salesforce.com/blog/the-agentic-ai-era-after-the-dawn-heres-what-to-expect/

The Executive Vice President and Chief Scientist of Salesforce AI Research distilled three stages of development in “The Agentic AI Era: After the Dawn, Here’s What to Expect”:In the first stage, specialized agents excel at handling specific tasks in particular industries, bringing unprecedented efficiency and accuracy to routine but critical business operations.In the second stage: collaborative cooperation, working together to achieve common business goals. In this case, the “coordinating agent” coordinates multiple experts to work together, similar to a restaurant manager coordinating excellent waiters, servers, managers, chefs, prep cooks, and expeditors to win a coveted Michelin star.In the third stage: crossing organizational boundaries to achieve complex agent-to-agent (A2A) interactions, thereby creating new business relationship models. In addition to traditional B2B and B2C models, we are also seeing the emergence of B2A (Business to Agent) and even B2A2C interactions, where AI agents act as intermediaries for work and transactions.Enterprise-level Multi-Agent Architecture: Platform + Application + ToolsThe platform is the foundation.It is a generic generator that abstracts specific attributes, and the following can all be understood as “foundation”:

1. Component libraries similar to Ant Design for front-end development scenarios
2. aPaaS architecture for management systems
3. Model components in the industrial CAE software field

Applications are scenarios.AI Agents do not appear out of thin air; they are better solutions based on business scenario needs, and the following are all “applications”:

1. Opportunity Agents in the L2C process of CRM systems
2. Research Agents in the pharmaceutical R&D field
3. Post-processing Agents in the industrial software field

Tools are enhancersWhat is referred to as MCP, Plugin, etc., all belong to this category, as they are all tools, and enterprise-level tools will inevitably have “tools” based on enterprise-specific functions.

https://bailian.console.aliyun.com/?tab=mcp#/mcp-market

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