Building AI Agents with Coze: From Beginner to Multi-Agent Collaboration

What Is Coze? Getting Started with a Homegrown Agent-Building Platform

Coze (Chinese name "扣子") is an 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 create functional agents without deep coding skills.

In today's wave of AI application development, the Agent has become a core concept. An agent is essentially an AI system capable of autonomously perceiving tasks, invoking tools, executing multi-step operations, and providing feedback. Coze is a low-barrier platform built precisely for this kind of application, integrating 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 upgraded capabilities 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 "perception—planning—action—feedback." Its technical core is typically 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 tool's returned results, and reasons about the next step. This capability elevates AI from "passive response" to "active execution," enabling it to break down complex tasks, call APIs, query databases, execute code, and more. The industry widely regards the period 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 the form of AI applications from "model as product" to "agent as product."

For users in China, one of Coze's major advantages is localization: built on ByteDance's tech stack, it offers stable access and an active ecosystem, while also providing rich Chinese documentation and templates—making it an ideal first stop for learning agent development.

Additionally, Coze's positioning toward non-professional developers continues the software industry's trajectory of "Low-Code/No-Code" development. 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 building applications. In the AI era, this approach has been transplanted onto agent development: through graphical workflow editors, a preset plugin marketplace, and template libraries, users can orchestrate large model capabilities without writing any low-level code. This cost-reducing, efficiency-boosting approach greatly expands the boundaries of who can be an AI developer, allowing content creators, product managers, and even operations staff to participate in building AI applications.

Comparing Coze with the Mainstream Platform Dify

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

An overall comparison for everyone

Positioning Differences Between Dify and Coze

Dify is an open-source LLM application development platform that emphasizes private deployment and flexibility, making it more favored by technical teams and enterprise-level users. Its advantage 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 the implementation of enterprise-level AI applications. Private deployment refers to installing and running a software system on an enterprise's own servers or private cloud environment, with data never passing 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 value on this. The open-source model means the source code is publicly viewable, modifiable, and independently maintainable, avoiding technical Vendor Lock-in to a single provider. Dify's open-source-plus-private-deployment approach stands in sharp contrast to Coze's official hosting—the former sells autonomy and control, while the latter offers convenience and ease of use. The two correspond to entirely 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 learning curve. For individual developers, content creators, or users looking to quickly validate ideas, Coze offers a gentler learning curve.

In short: for control and private deployment, choose Dify; for quick onboarding and ecosystem convenience, choose Coze. The two are not entirely mutually exclusive—many developers alternate between them depending on specific project needs.

Coze's Cross-Platform Interoperability

Coze does a thoughtful job with the user experience, offering multiple access methods with fully interoperable data.

We can all use it and it's all interoperable

Specifically, Coze supports:

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

This cross-platform interoperability means that an agent you configure on the web can be seamlessly invoked on your phone, greatly reducing migration costs. This is especially useful for scenarios requiring cross-device collaboration or on-the-fly demos.

Agent Types and the Skill System

Coze comes with multiple types of built-in agents, which can be broadly categorized by source into three groups:

Official Agents: Provided by Coze officially, verified and reliable, suitable for direct use or as learning templates.
Local Agents: Agents built and run in the user's own local environment.
Cloud Agents: Agents hosted in the Coze cloud, callable anytime.

How the Skill Mechanism Extends Agent Capabilities

Beyond agents themselves, Coze provides a rich Skill system, allowing agents to invoke external capabilities to complete more complex tasks.

In other words, scale

Skills are an extension 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 automation assistant capable of actually "getting things done." This mechanism is technically implemented through "Function Calling," where the large model identifies during reasoning that it needs to invoke some external capability, then generates structured calling parameters. The platform executes the corresponding plugin and feeds the results 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 "acting entity."

It's worth mentioning that Coze's previously launched Agent World feature has, according to the blogger's observation, been under maintenance and unavailable for nearly half a month, with the specific reason still unclear. This reminds us that when using such rapidly iterating platforms, certain features may be in a state of adjustment or testing, requiring ongoing attention to official updates.

Building Agents and Workflows via Programming

For developers seeking greater freedom, Coze provides programming methods to build agents and workflows.

We'll use the Code programming approach

The Difference Between Agents and Workflows

Agents place more emphasis on autonomous decision-making—you give a goal, and the agent 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-world projects, the two are often combined: use workflows to ensure the stability of critical processes, and use agents to handle steps requiring 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 issue in current AI engineering practice: over-reliance on an agent's autonomy can lead to unstable execution results that are difficult to debug, while complete reliance on fixed workflows sacrifices flexibility. Therefore, mature AI applications often adopt workflows on critical paths to ensure reliability, while introducing agents in steps requiring creativity or uncertain judgment, forming a complementary hybrid architecture.

Multi-Agent Collaboration in Practice

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

A single agent's capabilities are ultimately limited, whereas the idea behind multi-agent collaboration is to have multiple specialized agents each do their own job and work together. 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). Within this theoretical framework, multiple autonomous agents solve problems that a single agent cannot through communication, negotiation, and collaboration. Entering the era of large models, this concept has been reactivated and put into practice, 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, and tester), they simulate the division of labor and collaboration workflows of human teams to complete complex tasks like software development and market research. The advantage of multi-agent architecture lies in its clear division of labor and strong scalability, with each agent able to 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 the way human teams divide labor and can handle complex business scenarios with long chains and multiple stages. The multi-agent capabilities Coze provides are precisely what allow 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 offers a low-barrier path for AI application development through its friendly interface, cross-platform interoperability, and complete skill ecosystem. From building a single agent, to orchestrating workflows, to multi-agent collaboration, it covers a complete learning path from beginner to advanced.

For learners looking to enter 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 advisable to make a choice based on your own technical background and project needs.