Coze 3.0 Launched: A Focus on AI Agent Collaboration to Simplify Workflow Automation
Launched on June 3, 2026, the Coze 3.0 platform brings a new solution to the field of artificial intelligence applications. The platform’s core identity is a Multi-Agent collaboration platform, designed to significantly lower the technical barriers for building and managing AI teams, enabling users to integrate various AI models with different functions into a unified workflow.
Core Features: A Unified Environment for Multi-Agent Integration and Collaboration
Before Coze 3.0, enabling collaboration between different AI agents—such as OpenAI’s Codex and Anthropic’s Claude Code—typically required developers to have professional programming skills and complex environment configuration knowledge. Traditional methods involved command-line operations, building API call chains, and service registration, which posed significant barriers for non-technical users.
Coze 3.0 simplifies this process through the following designs:
- Visual Workflow: The platform provides a graphical interface, allowing users to connect different agents to a project by dragging, dropping, and configuring, replacing tedious backend coding.
- Unified Context Sharing: All agents integrated into the same project share a single context environment. This means task information and execution status are transparent within the team, eliminating the need for manual synchronization and ensuring collaborative continuity.
- Cross-Platform Support: Coze 3.0 supports web, desktop, and mobile applications, allowing users to monitor project progress and assign tasks from anywhere. Cloud agents run 24/7 on Coze’s cloud servers, independent of local devices.
- Flexible Integration Methods: The platform supports two agent integration modes. First, Cloud Integration, where users can directly select well-known agents like Codex and Claude Code from a provided list to run them in the cloud. Second, Local Integration, where the platform can automatically detect and integrate agents that are already installed and configured on the user’s local machine.
The Three-Step Method for Configuring AI Teams: Model, Skills, and Mode
To efficiently organize AI teams, Coze 3.0 proposes a three-step configuration process to help users customize their AI collaboration system based on task requirements.
- Base Model Selection: The platform includes a built-in master agent named Coze Claw. Users can flexibly select or switch its underlying Large Language Model (LLM), with support for various models including Doubao, Kimi, Zhipu GLM, and Minimax, while also allowing users to connect their own private models.
- Loading Professional Skills: Coze 3.0 features a “Skill Market” that offers pre-built “skill packs” covering various industries such as finance, law, healthcare, and the internet. Users can one-click install these skill packs onto specific agents, quickly equipping them with domain-specific knowledge and capabilities.
- Defining Collaboration Mode: A typical collaboration mode is the “Master-Expert” model. Coze Claw acts as the project controller and coordinator, assigning specific tasks to specialized sub-agents (e.g., for coding, data analysis, content generation) using the
@ command. All agents work together towards the same goal, ensuring focus and efficiency in task execution.
Use Cases: From Content Generation to Complex Application Development
The platform’s capabilities have been demonstrated in several real-world scenarios, showcasing its potential to handle tasks ranging from simple to complex.
- Automated Video Generation: In one case, a user simply provided a link to a viral TikTok video and a product image. The platform then invoked its built-in seedance 2.0 video model to automatically analyze the original video’s scenes, camera language, and rhythm, and then generated a marketing short with a similar style but featuring the new product.
- Simulated Investment Research Meeting: Users can create an “investment research team” composed of multiple AI agents, such as a “Fundamental Analyst,” a “Technical Analyst,” and a “Market Research Specialist.” After equipping them with finance skill packs and setting an agenda, the AI team can automatically analyze and debate a specific stock from multiple perspectives, ultimately generating a comprehensive report that includes decision rationale and risk assessment.
- Interactive Web Application Development: In a more complex project, the platform was used to develop a tarot card reading website with gesture interaction support. The master agent, Coze Claw, broke down the task and assigned it to different AI roles: a content operations expert designed the divination logic and prompts, a front-end expert wrote the interactive interface, a back-end expert handled API development and model integration, and another execution expert used the platform’s Canvas image generation feature to batch-produce 78 tarot cards with a consistent style.
Industry Trend: From Single Models to a Collaborative Agent Ecosystem
The launch of Coze 3.0 reflects a significant trend in the artificial intelligence field: a shift from pursuing single, all-powerful “super models” to building an ecosystem where multiple specialized agents collaborate. As discussed in courses like Stanford’s CS146S, modern software development is becoming deeply integrated with AI, and agents—as entities capable of autonomous planning and execution—are key to achieving high levels of automation and solving complex problems.
In the past, different models (e.g., Codex excels at rapid coding, while Claude Code performs better on deep logic) operated in silos, requiring users to manually switch between them and transfer information. The value of platforms like Coze 3.0 lies in providing an “agent operating system” or “AI IDE” that encapsulates the underlying complex technology. This allows users to focus on the task itself, orchestrating an AI team with simple commands. This model not only boosts productivity but also signals a future where human-computer collaboration will focus more on high-level strategy rather than low-level technical execution.
