Coze 3.0 Multi-Agent Collaboration: A Complete Guide to Building Your AI Team

As AI application development becomes increasingly mainstream, organizing multiple agents to work together is emerging as a powerful way to boost team productivity. ByteDance's Coze platform introduced a highly valuable feature in version 3.0: multi-user collaboration + multi-Agent projects. This article walks through a hands-on demonstration, breaking down exactly how to build an efficient "AI team" in Coze 3.0.

What Is a Multi-Agent Collaboration Project?

Traditional agent applications tend to operate in isolation — a single Agent handles everything, from content generation to data processing. But as task complexity grows, the limitations of a single Agent quickly become apparent.

To understand this, it helps to first clarify the fundamental difference between an AI Agent and a traditional chatbot. A traditional chatbot is essentially a rule-based or retrieval-based Q&A system with no ability to proactively plan steps or call external tools. A modern AI Agent, by contrast, is defined by a closed-loop "Perceive → Plan → Act" capability: it can understand natural language instructions (perceive), autonomously break down tasks and map out execution paths (plan), and complete real operations by calling APIs, search engines, databases, and other tools (act). The technical foundation of this paradigm is the Function Calling capability of large language models, which allows the model to decide when and which tool to invoke based on context. On the Coze platform, each Agent is bound to a specific LLM, system prompt persona, knowledge base, and toolset, giving it specialized execution capabilities within a defined domain.

Multi-Agent Systems (MAS) take this further by decomposing complex tasks and distributing them across multiple autonomous agents — a classic research direction in AI, originally applied to robotic collaboration and distributed computing. With the rise of large language models, frameworks like AutoGPT, LangChain, and MetaGPT have validated the feasibility of multi-Agent collaboration at the application layer. Coze packages this technical paradigm in a low-code format, enabling everyday users to build multi-Agent workflows without deep programming expertise.

The "Project" feature in Coze 3.0 essentially integrates multiple specialized Agents into a single workspace. Each Agent plays a distinct role — like different positions on a real team: one handles writing, another handles analysis, another manages archiving. Users can orchestrate these Agents through a unified chat interface to collaboratively achieve complex goals.

Beyond that, projects also support multi-user collaboration — it's not just multiple AIs working together, but multiple human team members participating in the same project, sharing data and results. This creates a new "human-AI hybrid" work model.

How to Create a Collaboration Project

The entry point for creating a project is straightforward. In the Coze 3.0 workspace, click the "+" button to create a new project.

The first step is naming the project — for example, "Test". The next key step is selecting which Agents to include. You'll see a list of Agents you've previously created, and you can check the ones relevant to the project.

Once you've made your selections, the multi-user collaboration project is ready.

One detail worth noting: the Files area within a project plays an important role. When you chat with Agents in the project, the content they generate is automatically saved to files — you send a command, the Agent receives it, executes the task, and archives the output, creating a traceable work record. This design addresses a key pain point in human-AI collaboration: AI-generated content is often scattered and hard to track. Structured archiving allows team members to asynchronously review, revise, and build upon AI outputs, forming a sustainable knowledge base for the team.

Project Settings: Managing Members and Agents

Inside Project Settings, you'll find a rich set of management options — the backbone of the entire collaboration mechanism.

Adding Collaborators

Under the "Members" section in project info, you can add other human team members by searching for their UID or similar identifiers. Once added, multi-user collaboration is activated — everyone on the team can participate in the same project's conversations and tasks, sharing files and outputs. This is especially useful for content creation, research compilation, or any work that requires coordinated human effort.

Toggling Agents On and Off

Project Settings also includes a dedicated Agent management area. All Agents you've created are listed here, and each has an on/off toggle to control whether it participates in the current project.

Enable all four Agent toggles, and the project has four agents working in parallel. Disable one, and only three remain active. This "plug-and-play" design lets you dynamically adjust the size and composition of your AI team based on task requirements at any given stage — highly flexible.

Project Settings also includes supplementary features such as file authorization, pinning projects, sharing, background tasks, and conversation history search, rounding out a comprehensive project management experience.

How to Dispatch Agents During Conversations

Once the project is set up, its real value shows in day-to-day use. In the chat interface, users can:

• Upload attachments: Drop data or reference materials directly into the project
• Use slash commands: Type "/" to quickly reference relevant skills or files
• @ mention specific Agents: Use "@" to assign a specific task to a specific Agent

The @ mention mechanism is the core of multi-Agent coordination. This interaction design draws from the @mention convention in enterprise collaboration tools like Slack and Feishu — in essence, it functions as "intent routing." Technically, when a user types @AgentName in a conversation, the system prioritizes routing that message and its context to the specified Agent. It's worth noting this differs from the common "Orchestrator-Worker" architecture in multi-Agent frameworks, where a master Agent handles task decomposition and sub-Agent scheduling. Coze's @ mechanism instead returns scheduling control to the user, aligning more closely with how human teams collaborate naturally and reducing cognitive overhead. Just like @-ing a colleague in a team chat to assign work, you can @ a specific Agent to own a particular task — for example, @ an agent to write a beginner's guide on "how to learn quickly from scratch," and it will automatically execute and produce the content.

Through this precise dispatching, multiple Agents can divide and conquer within the same project, each handling what it does best, and together completing a comprehensive work objective.

Why This Represents a "New AI Team" Model

Coze 3.0's approach fundamentally redefines the working paradigm for AI applications. In the past, using AI was a "one-on-one" Q&A. Now it's "many-to-many" collaboration — multiple human members and multiple AI agents advancing a project together in a shared workspace.

This model reflects a broader industry trend: Human-AI Collaboration is becoming a central theme in enterprise digital transformation. Research from McKinsey, Gartner, and others consistently shows that AI's impact on work is less about wholesale replacement and more about task-level collaborative division of labor — humans handle creative decisions, emotional judgment, and exception handling, while AI takes on repetitive execution, data processing, and content generation. The hybrid team design in Coze projects — combining multiple human members with multiple AI agents — is a direct product manifestation of this trend.

The core value proposition comes down to:

1. Specialized division of labor: Different Agents handle different functions, avoiding the bottlenecks of a single model
2. Scalability: Flexibly add or remove Agents via toggles — team size adapts to demand
3. Traceable collaboration: All outputs are automatically archived to files, shared across the team
4. Human-AI fusion: Humans handle decisions and instructions; AI handles execution and output

For content creation teams, operations teams, or small businesses, an "AI team" like this can significantly lower the barrier to collaboration and genuinely embed AI into everyday workflows.

Conclusion

Coze 3.0's multi-user, multi-Agent project feature represents an important step forward for low-code AI platforms toward a "team-oriented" future. The core value of low-code platforms lies in packaging technical capabilities — prompt engineering, tool integration, knowledge base management, workflow orchestration — into visual, graphical modules accessible to operations staff, product managers, and non-technical users alike. Coze goes further by layering in multi-Agent collaboration and multi-user project features, moving the "teamification" of AI applications from concept to reality.

It takes what were once isolated agents and organizes them into a structured, schedulable team — then adds multi-user collaboration on top to create an entirely new mode of intelligent work.

For teams looking to level up their efficiency, start simple: create a project, select your Agents, invite your members, and use @ to assign tasks. Once you internalize the orchestration logic, you'll find that building a high-performing AI team is far more achievable than it sounds.

Key Takeaways