Coze (Chinese name "kouzi") is a zero-code/low-code AI agent development platform for all scenarios developed by ByteDance's Flow division, supporting ordinary people to quickly build conversational agents, functional applications, etc. by dragging and dropping components through the visual interface, and can access knowledge bases, plug-ins and other extended functions
The platform can be accessed in the following ways:
Official website:
International version: www.coze.com
Domestic version: www.coze.cn
Mobile APP:
At present, there is no independent mobile APP, but the developed agent can be published to mobile applications on WeChat, Feishu and other platforms with one click. There are also "kouzi space" mobile related functions, which support the generation of PPT solutions, audio, web pages and other content.
It has the following core functions:
Start Agent professional development from scratch:
coze provides the core technology required for professional AI Agent development, and its security and reliability can meet enterprise-level security needs, and he also provides various AI models out of the box, such as: deepseek capability enhancement version, after-sales customer service, intelligent teaching assistants, etc.
Agent Development Tuning:
coze provides the debugging tool kouzi compass, which can help professional developers quickly build AI Agents and efficiently complete observation, evaluation, prompt development and debugging work.
Prompt Development:
The prompt function provides developers with an efficient debugging experience, supporting intelligent optimization, multi-mode comparison, and version management. After debugging, it can be integrated into the business code through the SDK-chain to achieve seamless connection to the development process.
Evaluation:
Coze provides out-of-the-box evaluation capabilities, built-in expert evaluator, supports multi-object evaluation, and provides out-of-the-box result insight analysis, multi-experiment comparison, and observation capabilities to achieve transparency in the evaluation process.
Observation:
Visualize the application development process to help you gain insight into the details of the entire link
The observation function provides visual support for the entire life cycle of large model applications, covering the entire process of development and debugging and online operation, helping developers quickly locate the root causes of problems and performance bottlenecks, and control the full link status of applications in real time.
Coze also offers an AI Agent open-source development framework:
Eino is an open source development framework for Al Agent based on the Go language, providing a wealth of capabilities such as atomic components, integration components, component orchestration, and facet expansion to assist in Al Agent development, helping developers develop Al Agents and various AI applications with clear architecture, easy maintenance, and high availability.
Practical operation
Tutorial core: Take the lesson plan as an example to learn to build a workflow with Coze
(This tutorial is based on the Coze platform, and automates the entire process from document content disassembly, AI generation to final document output by building an Agent Workflow)
Preliminary preparation: sort out the document structure
Before setting up a workflow, you need to clarify the components of the lesson plan, such as:
- Textbook analysis and teaching objectives (knowledge objectives, ability objectives, quality objectives)
- Teaching strategies, teaching priorities and difficulties
- Pre-class preview, in-class activities (introduction of new lessons, knowledge lectures, classroom exercises, etc.), and after-class consolidation
- Teaching evaluation and reflection
These modules will serve as the "instruction basis" for subsequent AI generation.
Steps: From blank workflow to complete system
Create a conversation flow workflow:
In the Coze platform, select Workflows" in the resource, enter a footnote (in English), and create a blank workflow as a base.
Split the modules and break them one by one:
Due to the rich content of the lesson plan, if it is generated by a single agent, it is easy to have the problem of insufficient length, so the method of multi-agent division of labor is used to split the lesson plan into multiple modules to generate them separately:
- Basic information module: including the selection of teaching materials, textbook content analysis, teaching objectives, etc., through the large model node, enter the command You are an expert in writing lesson plans, write and select teaching materials (50-word description), knowledge objectives (50-word description), etc. according to the provided content" to let the AI generate the corresponding content.
- Student situation modules: such as knowledge base, cognitive ability, learning characteristics, etc., are also generated through large model nodes, driven by instructions such as "describe students' cognitive abilities in 100 words".
- Teaching implementation module: Covers teaching strategies, teaching methods, key teaching difficulties and solutions, etc., and sets word count requirements for different parts (such as 100 words for key teaching solutions) to ensure detailed content.
- In-class modules: Detailed to sub-links such as new lesson introduction, new lesson teaching, classroom exercises, and classroom summaries, each sub-link is then divided into teacher activities, student activities, and design intentions, so that the logic of AI generation is clearer.
- After-school module: including after-class homework, teaching evaluation and reflection, etc., in which teaching reflection needs to analyze the shortcomings of teaching effect and improvement measures.
Document reading and content integration:
If you need to generate a lesson plan based on an existing document, you can add a "File Read" node to read the content of the Word document, and then pass the content to the large model to generate the corresponding lesson plan module based on the prompt.
Output Documents: From Fragmented Content to Complete Lesson Plans:
When the content of each module is generated, the scattered content is integrated into the Word document through the "Text Processing" and "Doc Maker" nodes, and finally the lesson plan file that can be used directly (you can also choose to output PDF or HTML format).
Details: Make workflows more efficient
Prompt optimization
In each large model node, it is necessary to carefully design prompts to clarify roles (such as You are a senior lesson plan writing expert"), tasks (such as "write pre-class preview content, which must be closely related to class content"), and format (such as "point point instructions") to ensure that the AI-generated content meets expectations.
Resource points and token consumption
When using the Coze platform, you need to pay attention to resource points and token consumption. Generally speaking, the longer the content and the more modules, the more tokens and resource points are consumed. You can adjust the length and complexity of the generated content according to the resource point quota of your account (such as 500 or 1000 resource points per day).
Model selection and replacement
If you need to improve the effect, you can replace the model with "Deep Seek v3024", etc., and adjust the prompt words and model parameters according to the quality feedback of the generated document
Try out existing agents
we try to actually use an existing agent - "intelligent customer service assistant", which is based on COZE dialogue flow and knowledge base capabilities, and provides templates such as sparring, dialogue, and copilot from three stages: pre-sales, in-sales, and after-sales, helping enterprises quickly build AI intelligent customer service applications, which can realize automatic responses to user inquiries, problem classification, work order generation and other processes. The specific demonstration is as follows:
Functional experience:
The agent can realize automatic response, problem classification, work order generation and other processes of user consultation. After the user enters a question, the agent first identifies the type of question through semantic understanding, and if it is a regular question, it triggers a general answer, and if it is an unconventional question, it transfers to human customer service. Coze's own intelligent customer service can only answer questions about itself, and the functionality is not strong.
Operation process:
- Go to the agent demo environment of the COZE platform, find the "Intelligent Customer Service Assistant" and launch it.
- Type in the consulting question How do I make a workflow?" The agent immediately returns the step description and gives the corresponding link jump interface.
- Click Workflow", and the agent will immediately jump to the development interface of kouzi.
Summary and analysis
advantage
- High development efficiency: Low-code features and rich template components greatly shorten the development cycle of agents and software, and non-technical personnel can quickly get started to build simple applications.
- Strong scalability: Supports custom functions and API docking, which can meet the needs of complex business scenarios and can be deeply integrated with existing enterprise systems.
- High degree of intelligence: Built-in natural language processing and intelligent decision-making capabilities enable the developed agents and software to have good interactivity and autonomy.
suggestion
- For developers: Start with templates and gradually try custom workflows and function modules to dig deeper into COZE's advanced features (such as multi-agent collaborative algorithms, complex logic orchestration).
- For enterprises: Priority can be given to introducing COZE-based agent applications in customer service, task management, data analysis, and other scenarios to improve operational efficiency, and then explore more complex business process automation.
