How Does Multi-Agent Collaboration Work?

1. Introduction: From “Individual AI” to “AI Expert Teams”

In the early stages of artificial intelligence development, we tended to view AI as an “all-rounder.” It could answer questions, write articles, create images, and generate code, seemingly capable of anything.

However, when applying AI to complex business scenarios—such as supply chain management, investment research analysis, intelligent customer service, and strategy optimization—you will find:

No matter how smart one AI is, it cannot compete with a group of AIs working collaboratively.

For simple tasks, a single AI can suffice; but when problems span multiple domains, require continuous reasoning, and involve conflicting objectives, an “AI expert team” often completes tasks more efficiently and robustly.

This team is known as a Multi-Agent System (MAS).

Its value lies in transforming AI from merely a “tool” into an entity that can function like a human organization—dividing labor, collaborating, communicating, making decisions, and optimizing.

This article will take you deep into understanding the core mechanisms and architectural logic of multi-agent collaboration, breaking down how it achieves “1+1>2.”

2. Why Do We Need “Multi-Agents”? The Ceiling of Individual Intelligence

A single agent can indeed accomplish many tasks, but its capabilities are not limitless. As task complexity increases, the bottlenecks of single-agent systems become apparent.

2.1 Limitations of Single Agents

(1) Physical Limitations of Context Windows

Large language models are powerful, but they are constrained by the “context window.” Once tasks involve lengthy materials, multiple stages, or cross-domain knowledge, a single agent often “forgets the preceding logic,” leading to fragmented results.

(2) Unidimensional Skills

Just as a programmer finds it difficult to excel simultaneously as a designer, data analyst, and lawyer, a single agent struggles to master text generation, structured data analysis, knowledge retrieval, and tool invocation all at once.

(3) Difficulty in Decomposing Complex Tasks

For example, generating a market research report involves multiple steps such as data collection, data analysis, writing, and formatting. A single agent often gets stuck in a “half-done” situation.

(4) Difficulty in Coordinating Conflicting Multiple Objectives

Real-world tasks often require balancing multiple objectives such as speed, accuracy, and cost. A single agent struggles to dynamically adjust strategies in the face of multiple objectives.

2.2 Advantages of Multi-Agents

The advantages of multi-agent systems can be summarized in one sentence:

They allow AI experts from different fields to form teams, each performing their roles and complementing each other.

① Division of Labor and Specialization

Each agent takes on different responsibilities: information retrieval, task planning, logical reasoning, content generation, result verification… forming an “expert collaboration chain.”

② Parallelism and Efficiency

Multiple agents can execute tasks simultaneously, no longer limited by the speed of serial execution of a single model.

③ Robustness and Fault Tolerance

If one agent makes a mistake, the system can automatically adjust strategies or have other agents take over, ensuring that the overall task is not interrupted.

④ Strong Scalability

Businesses can add new agent modules as they grow, such as integrating database agents, image recognition agents, report generation agents, etc., without needing to start from scratch.

This is akin to a company evolving from individual entrepreneurs to a mature organization with “departmental collaboration.”

3. Core Architecture: Two Main Collaboration Models

In multi-agent systems, how the team is “managed” is crucial. Currently, there are two main architectures in the industry: vertical (centralized) and horizontal (decentralized).

3.1 Vertical Architecture: Centralized “Manager-Employee” Model

In this architecture, the entire system has a core brain—the main agent (Manager).

It acts like a project manager, responsible for receiving tasks, breaking down objectives, scheduling subordinates, and integrating results.

🧠 Typical Workflow

1. User submits a request (e.g., “Help me generate a real estate market analysis report”);

2. Main Agent plans the task (dividing it into three stages: “data collection,” “trend analysis,” and “report writing”);

3. Task allocation:

• The search agent fetches the latest policy data;

• The analysis agent generates trend charts based on historical transaction records;

• The writing agent drafts the report content based on the outline;

Sub-agents execute tasks in parallel, returning results;

Main Agent integrates and optimizes the output;

Final results delivered to the user.

🌟 Advantages

• Clear architecture and strong controllability;

• Facilitates debugging and monitoring;

• Suitable for process-oriented, clearly defined tasks (e.g., report generation, automated office tasks).

⚠️ Limitations

The system heavily relies on the planning and instruction quality of the main agent; if the main agent makes a mistake, it may affect the overall output.

📍 Real-World Case

The Workflow Agent system of Ping An Yi Wallet adopts a vertical architecture.

It includes three core roles:

• Planner: Responsible for task decomposition and strategy design;

• Observer: Monitors task execution status;

• Decider: Evaluates and integrates results.

This “manager-employee” structure is very common in internal process automation within enterprises, effectively enhancing task controllability and accuracy.

3.2 Horizontal Architecture: Decentralized “Round Table” Model

Unlike the vertical architecture, in the horizontal model, there is no “central authority.” All agents are equal, and they make decisions collectively through negotiation, debate, or voting mechanisms.

⚙️ Operational Process

1. All agents receive a common task;

2. They share their understanding and viewpoints through communication protocols;

3. Consensus is reached through voting, argumentation, or game theory;

4. Final solutions are output.

🌟 Advantages

• High flexibility, capable of addressing open-ended problems;

• Failure of a single agent does not affect the overall system;

• The system can self-adjust, suitable for scenarios requiring creative output.

⚠️ Challenges

• High communication and consensus costs;

• Complex decision-making processes;

• Prone to “stalemates” or opinion splits.

📍 Real-World Case

The PEER model launched by Ant Group is a typical representative.

This system includes four parallel agents:

• Plan: Plans tasks;

• Execute: Executes tasks;

• Express: Outputs expressions;

• Review: Evaluates and provides feedback.

The four form a closed loop, with each agent being both independent and interdependent, allowing the system to continuously optimize and self-learn.

4. Mechanism Breakdown: How Multi-Agents Complete Complex Tasks

Taking the example of “generating a new energy vehicle industry analysis report,” let’s see how multi-agents work from order receipt to delivery.

Step 1: Task Reception and Analysis

• User submits a request.

• Proxy Agent acts as the system entry point, responsible for filtering illegal inputs, conducting security checks, and converting tasks into structured instructions.

Step 2: Task Planning and Decomposition

• Main Agent (or a consensus reached by multiple agents) is responsible for breaking down the large task into executable sub-tasks.

• For example:

• Information retrieval

• Data analysis

• Report writing

• Content review

• Result integration

Step 3: Role Assignment and Scheduling

The main agent calls different experts based on the type of sub-task:

• Search Agent calls APIs to fetch industry data;

• Analysis Agent processes structured information such as Excel and databases;

• Writing Agent generates logically coherent content;

• Review Agent checks for consistency and factual accuracy.

Step 4: Execution and Collaboration

During execution, agents may engage in “internal communication”:

• Writing Agent requests a trend chart from the data agent;

• Analysis Agent obtains the latest data sources from the search agent. This communication is typically completed through structured protocols (such as JSON) to avoid semantic confusion.

Step 5: Result Summary and Integration

The outputs of each sub-agent are sent back to the main agent. The main agent is responsible for:

• Logical linking

• Style unification

• Content correction and polishing

Step 6: Review and Iteration

The evaluation agent (Critic) reviews the integrated results:

• Does it meet the task requirements?

• Are there any logical flaws or data errors? If not up to standard, the main agent will trigger a new round of correction processes.

Step 7: Final Delivery

Once all stages pass review, the Proxy Agent outputs the final report, marking the completion of the task loop.

This mechanism may seem complex, but in an automated environment, the entire process can often be completed within minutes.

5. Core Technologies Supporting Collaboration: The “Code of Conduct” for AI Teams

An efficient multi-agent system relies on the following four key mechanisms.

5.1 Communication Protocol: How Agents “Communicate”

To enable different agents to understand each other, a unified language must be defined. Common methods include:

• Natural Language Communication (based on LLM prompt interactions);

• Structured Communication (using JSON or XML formats to clearly define tasks and feedback content);

• API Call Communication (exchanging data through function signatures).

5.2 Collaboration Mechanisms: How to Reach Consensus

When agents have disagreements, rules must be in place to resolve them. Mainstream methods include:

• Contract Net Protocol (CNP): The main agent publishes tasks, and sub-agents bid, with the system selecting the optimal solution.

• Voting Mechanism: Multiple agents independently generate solutions and then vote for the best one.

• Debate Mechanism: Achieving optimal conclusions through argumentation and rebuttal.

5.3 Knowledge Sharing: How Teams “Share Memory”

Information sharing among agents is achieved through a “Blackboard System.”

All agents can read and write to the same shared knowledge base, enabling:

• Historical task memory;

• Data cache reuse;

• Shared contextual understanding.

5.4 Conflict Resolution: How to Avoid “Fighting”

When two agents have conflicting goals, the system must arbitrate.

Common practices include:

• Establishing Priority Mechanisms (determining execution order by the system);

• Introducing Arbitration Agents (independently judging conflicting proposals);

• Using reinforcement learning to dynamically adjust agent weights.

6. Case Insights: How Big Companies Do It?

Case 1: Ant Group’s “Investment Research Assistant”

This system adopts the PEER model (horizontal architecture).

Four equal agents form a closed loop, executing everything from information collection, analysis, expression, to evaluation entirely by AI.

This system has improved investment research efficiency by over 60%, achieving AI analyst-level intelligent investment research.

Case 2: Lenovo’s Sales Efficiency System

Lenovo’s sales agent system adopts a vertical architecture.

The main agent acts as the sales manager, coordinating inventory query agents, profit calculation agents, and product recommendation agents.

The system can generate optimal product combination plans in real-time based on customer needs, transforming sales consultants from “asking customers for data” to “directly providing answers to customers.”

7. Conclusion and Outlook

The core logic of multi-agent collaboration can be summarized in four words:

Decomposition, Scheduling, Communication, Integration.

It allows AI to leap from “individual intelligence” to “collective intelligence.”

Vertical architecture emphasizes control and efficiency, suitable for clearly defined tasks;

Horizontal architecture emphasizes flexibility and innovation, suitable for exploratory problems.

In the future, multi-agent systems will evolve in the following directions:

• More Natural Communication: Direct dialogue in natural language, eliminating the need for complex protocols;

• Smarter Negotiation: Agents capable of automatic reasoning, concessions, and bargaining;

• More Autonomous Organization: Systems with self-learning and self-management capabilities.

At that time, AI will no longer just be our assistant but will become intelligent colleagues and strategic partners in enterprises.

When one agent can save you time, a group of agents can help you reconstruct the entire business system.