Getting Started with Coze: Build AI Agents from Scratch

What is Coze

Coze is a one-stop AI Bot development platform created by ByteDance—essentially a low-code platform. Its greatest feature is this: whether or not you know how to code, you can develop AI-powered Q&A agents based on large language models right on the platform, solving complex problems through natural conversation.

The industry context of low-code platforms: Low-code platforms have been a major trend in software development over the past decade. Traditional software development requires engineers to master programming languages, databases, server architecture, and other specialized knowledge, resulting in long development cycles and high costs. Low-code platforms encapsulate technical complexity at the underlying level through visual drag-and-drop interfaces, prebuilt components, and templates, allowing business professionals to participate in application building. Gartner predicts that by 2026, low-code platforms will handle more than 65% of global application development work. It's worth noting that low-code does not equal "no-code"—for complex logic, developers can still insert custom scripts beyond the visual interface, balancing flexibility with ease of use. Coze extends the low-code concept into the realm of AI agents, representing the natural evolution of this trend in the AI era.

More importantly, the Bots you build can be published to various chat platforms and social platforms—virtually any scenario you want to integrate into is achievable. AI customer service agents, product Q&A assistants, AI knowledge bases, travel itinerary generation, image recognition and description... all of these features can be realized on this single platform.

Understanding AI Agents

For OpenAI, the form of an agent is GPTs; on the Coze platform, the Bot store gathers all kinds of agents—role-playing, image processing, short-video production, logo design, essay writing, and more, everything you could want.

The technical essence of AI Agents: AI agents differ from ordinary conversational AI in that they possess a closed-loop capability of "perceive—plan—act." Simply put, traditional large language models can only "answer questions," while agents can "complete tasks"—they can autonomously invoke external tools, break down multi-step goals, and dynamically adjust strategies based on intermediate results. GPTs, Coze Bots, and LangChain Agents are all concrete implementations of this concept. The core components of an agent typically span four dimensions: the foundation large model (responsible for reasoning and language generation), the memory module (storing short-term conversational context and long-term user information), tool invocation (acquiring external capabilities through plugins or APIs), and workflow orchestration (breaking complex tasks into multiple sub-steps and coordinating the execution order). These four dimensions work together to give agents task-execution capabilities that go beyond simple Q&A. Understanding these four dimensions is an important prerequisite for studying Coze in depth.

A vivid analogy: you can think of each AI Bot as an app. Apps on your phone come in endless varieties, each serving its own purpose; AI Bots are applications powered by AI technology behind the scenes. Traditionally, developing an app has an extremely high barrier for ordinary people, but now, as long as you can hold a normal conversation and possess basic logical thinking, a short period of learning will let you build your own AI applications.

Key Differences Between the Domestic and International Versions

Due to regional regulations and differences in network environments, Coze is divided into a domestic (China) version and an international version. In terms of features, the international version is slightly superior, but for beginners, the domestic version's all-Chinese interface makes it easier to get started. Either version works fine for entering the field.

Model Capability Comparison

This is the most core difference between the two versions:

• Domestic version: Uses ByteDance's self-developed Chinese large model "Yunque" (Skylark), with stable Chinese comprehension
• International version: Offers free access to OpenAI's GPT-4 128K model, with far more powerful capabilities

Here's a detail worth noting: GPT-4 is a paid model. Using it officially through OpenAI requires a Plus membership at $20/month (roughly 160+ RMB), and calling the API for additional development incurs extra costs. Coze offers this model for free—which is quite generous.

The significance of GPT-4's 128K context window: "128K" refers to the size of GPT-4's context window—the maximum amount of text the model can process at once. 1K roughly equals 750 English words or 500 Chinese characters, so 128K means the model can process about 64,000 Chinese characters in a single conversation—equivalent to a novella. The size of the context window directly determines the complexity of tasks the AI can handle: the larger the window, the more conversation history the model can "remember," the longer the documents it can analyze, and the more complex the multi-turn tasks it can handle. Early GPT-4 supported only an 8K context, so the 128K version is a major upgrade, offering tremendous value for professional users who need to process long documents and complex projects. This also means that when building Bots on Coze that need to analyze complete contracts, lengthy reports, or multi-round in-depth consultations, the model won't lose critical context due to "memory overflow."

Plugins and Output Platforms

Plugins provide large language models with functional extensions beyond their native capabilities. Take real-time flight lookup as an example: GPT-4 itself cannot obtain flight information, but with the corresponding plugin integrated, it can easily do so.

The technical principle of plugin mechanisms and tool invocation: Large language models are essentially a "static knowledge base"—their knowledge is cut off at the point in time of their training data, and they cannot actively retrieve external information. The plugin mechanism solves this limitation through Function Calling technology: when a user makes a request requiring real-time data, the model recognizes the intent, generates a structured API call instruction, which is executed by the platform backend and returns the result to the model, which then integrates it into a natural-language answer. This mechanism was first officially introduced by OpenAI in March 2023 and subsequently became a standard capability across major AI platforms. Coze's plugin ecosystem is essentially a productized encapsulation of this mechanism, allowing developers to grant Bots the ability to access the internet in real time, query databases, and control third-party services—all without writing API-call code. Currently, Coze's plugin store already has hundreds of prebuilt plugins covering mainstream scenarios like search, weather, finance, and e-commerce, and developers can also integrate their own APIs through standardized interfaces. The domestic version's plugins are better tailored to Chinese use cases; the international version can integrate with platforms like YouTube, Twitter, and GitHub, offering a richer variety.

Output platforms are the channels through which a Bot is ultimately published. The domestic version supports customer service accounts, WeChat Official Accounts, Juejin, and more; the international version supports Discord, Telegram, and others. In addition, the international version offers a multi-agent mode and more flexible workflow customization capabilities, but these are advanced topics that don't affect getting started.

Why Coze Is Worth Learning

The value of learning Coze is mainly reflected in the following four aspects.

Zero barrier—anyone can pick it up

Learning Coze requires no programming language whatsoever. Whether you're a workplace newcomer, a parent, or a professional developer, you can get up to speed quickly. Its core logic output relies on text—you describe the architecture and build the internal structure of an application by typing. For those who want to enter the AI field but lack a coding foundation, Coze is the lowest-barrier entry point.

Currently the most powerful LLM orchestration platform

After hands-on testing, Coze is one of the most powerful large language model orchestration platforms available today.

The competitive landscape of LLM orchestration platforms: LLM orchestration refers to the technical framework of connecting large language models with external tools, data sources, and multiple AI models—via visual or code-based methods—to accomplish complex tasks. Mainstream orchestration platforms can be divided into two categories: the developer-facing framework layer (such as LangChain and LlamaIndex, which require a Python programming foundation) and the business-user-facing product layer (such as Coze, Dify, and Flowise, which provide visual operation interfaces). Dify is another well-known open-source orchestration platform in China, supporting private deployment and offering greater technical depth, but its learning curve is noticeably steeper, making it more suitable for users with some technical background; Flowise is an open-source visual tool based on LangChain, with an active community but relatively lower feature completeness. Internationally, automation platforms like n8n and Make (formerly Integromat) have also begun deeply integrating AI capabilities, forming indirect competition with Coze in workflow-automation scenarios. Compared with the above peer products, Coze encapsulates complex workflows into visual operations and provides them directly to developers, truly achieving a balance between powerful functionality and beginner-friendliness.

Currently free—the window is limited

This is a very practical point. Coze is currently in the early stages of user growth, offering powerful models like GPT-4 for free. Keep in mind that every AI model call consumes real computing power, so ByteDance is actually making continuous investments—exactly the same logic as the heavy subsidies of early ride-hailing and food-delivery platforms.

Platform subsidy strategy and monetization path: ByteDance's free strategy for Coze is a replay, at the AI infrastructure layer, of the classic internet-industry playbook of "trading traffic for market share." The API costs generated by each GPT-4 call are borne by ByteDance—essentially subsidizing user growth and building a developer ecosystem with hard cash. Similar strategies have appeared many times in history: AWS grabbed market share early on with low-priced cloud services, Stripe cultivated developer habits with a free sandbox environment, and domestic cloud providers attracted startups with free tiers to lock in their tech stacks. For Coze, possible monetization paths include: an enterprise version charged by usage volume, subscription plans for advanced models and features, a revenue-sharing mechanism in the Bot store, and private deployment services for enterprises. Understanding this logic helps in judging the platform's long-term value—once the developer ecosystem reaches scale, the platform's bargaining power will greatly increase, and the free window will narrow accordingly. Understanding this logic also helps in judging the best time window for investing in learning.

Once a sufficient scale is accumulated, the platform will inevitably move toward monetization, at which point the cost of learning will rise significantly. Getting in now is the most cost-effective timing.

Enormous monetization potential

As Coze continues to open up more Bot deployment channels, developers will be able to integrate AI Bots into any scenario, and the commercial value is immeasurable.

Whether you're a professional Bot developer, a sales consultant selling excellent Bots to enterprises, or an internal technical staffer using AI Bots to boost your company's efficiency, you can find an opportunity that fits you. It's foreseeable that in the future, school websites, corporate homepages, OA systems, mobile apps, and even NPC characters in games may all be handled by AI Bots.

The underlying logic of AI monetization is actually quite clear: find an existing money-making workflow and replace the manual-labor steps within it with AI Bots, completing the same tasks at lower cost and higher efficiency.

What You'll Gain After Learning

A deeper understanding of AI models

Through repeatedly building AI agents in practice, you'll develop a more solid understanding of the overall operating logic of large language models, as well as fine-tuning and orchestration techniques. Compared with introductory content that only teaches you to "use AI to write copy or resumes," the understanding accumulated through hands-on practice has greater depth and more transferable value.

A large collection of your own AI Bots

This is a learning path that combines education with enjoyment and is driven by practice. If you follow along at your own pace, you'll accumulate at least several AI Bots that meet your own needs, and master the methods to integrate them into any platform.

Free access to GPT-4 128K

If you're already a heavy user of large AI models, the mere fact that you can freely call GPT-4 128K, DALL·E 3, and the GPT-4V multimodal model is equivalent to saving the cost of an entire OpenAI Plus account.

The capability boundaries of multimodal models: DALL·E 3 is an image-generation model released by OpenAI that can generate high-quality images from natural-language descriptions, and on Coze it can be used to build Bots for automated design and text-and-image creation; GPT-4V (Vision) is a version of GPT-4 with visual understanding capabilities, able to analyze image content, recognize chart data, and interpret product screenshots. The integration of multimodal capabilities means that the Bots you build on Coze are no longer limited to pure text interaction, but can handle needs closer to real business scenarios such as "answer based on an image" and "analyze an uploaded screenshot."

The ability to independently break down problems

This point especially deserves separate emphasis. In the process of building AI agents, users with no foundation will inevitably encounter challenges involving code or unfamiliar domains. True capability lies not in avoiding difficulties, but in learning to solve problems indirectly through AI conversation.

In an age of information overload, knowledge itself is not scarce—as long as you want to learn, resources are everywhere. What's truly scarce is the ability to break down and apply knowledge, and this is the key factor that sets people apart. When facing any new technology in the future, how you view it, understand it, and conquer it is the underlying capability most worth cultivating deeply.

Conclusion

Coze represents an important direction in the democratization of AI application development: making the technical barrier no longer an obstacle for ordinary people to embrace AI. While the platform is free and the ecosystem is still growing, now is the best window to get into Coze—whether out of learning interest or commercial strategy. The real value lies not just in how many Bots you can build, but in cultivating a deep understanding of AI and the ability to independently break down unknown problems.

Key Takeaways

• Coze is a low-code AI Bot development platform from ByteDance that requires no programming foundation to get started
• AI agents possess a closed-loop capability of "perceive—plan—act," with core components spanning four dimensions: the large model, memory, tool invocation, and workflow orchestration
• The international version offers flagship models like GPT-4 128K (supporting ~64,000 Chinese characters of context), DALL·E 3, and GPT-4V for free
• The plugin mechanism grants Bots real-time internet access and third-party service invocation through function calling technology
• The current free strategy is ByteDance's strategic investment in building a developer ecosystem, and the time before the monetization window narrows is the best time to learn
• The platform's core value lies in balancing powerful functionality with beginner-friendliness, giving it a differentiated advantage in the competitive landscape of LLM orchestration platforms