Coze Agent Development in Practice: From Beginner to Multi-Agent Collaboration

What is Coze? An Introduction to a Homegrown Agent-Building Platform

Coze (Chinese name 「扣子」) is an intelligent agent-building platform launched by ByteDance. Unlike traditional programming frameworks, it targets developers and everyday users who want to quickly build AI applications, with the goal of enabling people to build usable agents without deep coding skills.

In the current wave of AI application development, the Agent has become a core concept. An agent is essentially an AI system that can autonomously perceive tasks, invoke tools, execute multi-step operations, and provide feedback. Coze was built precisely for this kind of application—a low-barrier platform that integrates large model capabilities, workflow orchestration, and a plugin ecosystem into a unified interface.

It's worth understanding in depth that the rise of the agent concept is closely tied to the capability upgrades of large language models. Traditional conversational AI could only handle single-turn or multi-turn Q&A, whereas agents introduce a closed-loop mechanism of “perceive—plan—act—feedback.” Its technical core is usually built on paradigms such as ReAct (Reasoning + Acting), where the model first reasons and makes judgments, then decides which external tools to invoke, observes the results returned by the tools, and then proceeds to the next round of reasoning. This capability allows AI to evolve from “passive responding” to “active execution”—able to break down complex tasks, invoke APIs, query databases, execute code, and more. The industry widely regards around 2024 as the “Year of the Agent,” with OpenAI's GPTs, Microsoft's Copilot, and various domestic agent platforms emerging one after another, marking a shift in AI application form from “model as product” to “agent as product.”

For domestic users, one major advantage of Coze is localization: built on ByteDance's tech stack, it offers stable access and an active ecosystem, along with abundant Chinese documentation and templates—making it an ideal first stop for learning agent development.

In addition, Coze's positioning toward non-professional developers continues the software industry's Low-Code/No-Code development trajectory. This concept first became popular in enterprise application development, with representative products such as Salesforce and Microsoft Power Platform, whose core goal is to lower the development barrier through visual drag-and-drop so that business staff can also participate in application building. In the AI era, this approach has been transplanted to agent development: through graphical workflow editors, preset plugin marketplaces, and template libraries, users can orchestrate large model capabilities without writing low-level code. This cost-reducing, efficiency-boosting approach greatly expands the boundaries of who can be an AI developer, enabling content creators, product managers, and even operations staff to participate in building AI applications.

Coze vs. the Mainstream Platform Dify

In the space of rapidly building agents, there are several competing products on the market. The two most frequently compared are Dify and Coze.

Positioning Differences Between Dify and Coze

Dify is an open-source LLM application development platform that emphasizes private deployment and flexibility, and is more favored by technical teams and enterprise-level users. Its strength lies in complete autonomous control over data and model integration, making it suitable for teams with a certain level of operations capability.

Private deployment here is a key consideration for enterprise-level AI application implementation. So-called private deployment refers to installing and running the software system on an enterprise's own servers or in a private cloud environment, where data does not pass through third parties, thereby meeting requirements for data compliance, security auditing, and customization. Industries sensitive to data—such as finance, healthcare, and government—place particular importance on this. The open-source model means the source code is publicly viewable, modifiable, and self-maintainable, avoiding technical lock-in to a single vendor (Vendor Lock-in). The open-source-plus-private-deployment route adopted by Dify stands in stark contrast to Coze's official hosting—the former sells on autonomy and control, the latter on convenience and ease of use, each corresponding to distinctly different user demands in the AI tools market.

Coze, on the other hand, leans more toward “out-of-the-box” usability, with official hosting, a friendly interface, and a fast onboarding experience. For individual developers, content creators, or users who want to quickly validate ideas, Coze has a gentler learning curve.

In short: if you pursue controllability and private deployment, choose Dify; if you pursue quick onboarding and ecosystem convenience, choose Coze. The two are not entirely opposed—many developers alternate between them depending on specific project needs.

Coze's Multi-Platform, Interconnected Usage

Coze does a thoughtful job on the usage experience, offering multiple access methods with fully interconnected data.

Specifically, Coze supports:

• Web version: No installation needed—just open a browser to build and debug agents, suitable for desktop office scenarios.
• App version: View and use your built agents anytime on mobile.
• Local installation version: Get a more stable experience by installing a client.

This interconnected multi-platform design means an agent you configure on the web can be seamlessly invoked on your phone, greatly reducing the migration cost of usage. This is especially practical for scenarios requiring cross-device collaboration or on-the-fly demo presentations.

Agent Types and the Skill System

Coze comes with multiple built-in types of Agents, which can be roughly categorized into three types by source:

• Official Agents: Provided by Coze officially, verified, stable, and reliable—suitable for direct use or as learning templates.
• Local Agents: Agents built by users and running in local environments.
• Cloud Agents: Agents hosted on the Coze cloud, callable at any time.

How the Skill Mechanism Extends Agent Capabilities

Beyond the Agents themselves, Coze also provides a rich Skill system, enabling agents to invoke external capabilities to complete more complex tasks.

Skills are extensions of an Agent's capabilities—by attaching different skills (such as web search, image processing, data query, and other plugins), an agent that could originally only converse can become an automated assistant that can truly “get things done.” This mechanism is technically usually implemented through Function Calling, where the large model identifies during reasoning that it needs to invoke a certain external capability, then generates structured invocation parameters, and the platform executes the corresponding plugin and feeds the result back to the model. It is precisely this capability that forms the key bridge for an agent to leap from a “language model” to an “actor.”

Worth mentioning is the Agent World feature that Coze previously launched. According to the blogger's observations, it has remained unopened for nearly half a month under maintenance, and the specific reason is still unclear. This also reminds us that when using such rapidly iterating platforms, some features may be in a state of adjustment or testing, requiring ongoing attention to official updates.

Building Agents and Workflows Through Programming

For developers who want greater freedom, Coze offers programming-based methods to build agents and workflows.

The Difference Between Agents and Workflows

Agents place more emphasis on autonomous decision-making—you give a goal, and it decides on its own which tools to invoke and which steps to execute.

Workflows, on the other hand, are pre-orchestrated processes—every node and every branch is explicitly defined by the developer, with controllable execution paths and predictable results.

In real projects, the two are often used together: use workflows to ensure the stability of critical processes, and use agents to handle segments that require flexible judgment. Through programming, developers can finely control data flow, node logic, and exception handling to build more robust AI applications. This trade-off between “deterministic processes” and “autonomous decision-making” is also a core topic in current AI engineering practice: over-reliance on an agent's autonomy may lead to unstable, hard-to-debug execution results, while complete reliance on fixed workflows sacrifices flexibility. Therefore, mature AI applications often adopt workflows on critical paths to ensure reliability, and introduce agents in segments requiring creativity or uncertain judgment, forming a complementary hybrid architecture.

Multi-Agent Collaboration in Practice

Finally, the focus of this practical session—the Multi-Agent mode.

The capability of a single agent is ultimately limited, whereas the idea of multi-agent collaboration is to have multiple specialized agents each perform their own duties and cooperate with one another. For example, one handles information retrieval, one handles content generation, and one handles result review, collaborating to complete complex tasks that a single agent would struggle with.

In fact, multi-agent collaboration is not an entirely new concept—its ideas can be traced back to last century's research on Distributed Artificial Intelligence (DAI) and Multi-Agent Systems (MAS). In this theoretical framework, multiple autonomous agents solve problems that a single agent cannot through communication, negotiation, and collaboration. In the large-model era, this idea has been reactivated and implemented, with representative frameworks including Microsoft's AutoGen, as well as MetaGPT, CrewAI, and others. By assigning roles to different agents (such as product manager, programmer, tester), they simulate the division-of-labor collaboration process of a human team to complete complex tasks such as software development and market research. The advantage of multi-agent architecture lies in clear division of labor and strong scalability, and each agent can be optimized for a specific subtask, thereby breaking through the capability ceiling of a monolithic model as a whole.

This mode represents an important trend in current agent development: moving from “monolithic intelligence” to “collective collaboration.” It more closely resembles how human teams divide labor, and can handle complex business scenarios with long chains and many stages. The multi-agent capability provided by Coze is precisely what allows developers to experience and practice this cutting-edge concept on a low-barrier platform.

Conclusion

As a homegrown agent-building platform launched by ByteDance, Coze provides a low-barrier path for AI application development thanks to its friendly interface, interconnected multi-platform experience, and complete skill ecosystem. From building a single agent, to orchestrating workflows, to multi-agent collaboration, it covers a complete learning chain from beginner to advanced.

For learners looking to enter the field of AI Agent development, Coze is a starting point worth trying first. Of course, it and platforms like Dify each have their own focus, so it's recommended to make a choice based on your own technical background and project needs.

Key Takeaways