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layout default
title Chapter 6: Application Patterns and Safety Boundaries
nav_order 6
parent Qwen-Agent Tutorial

Chapter 6: Application Patterns and Safety Boundaries

Welcome to Chapter 6: Application Patterns and Safety Boundaries. In this part of Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows, you will build an intuitive mental model first, then move into concrete implementation details and practical production tradeoffs.

This chapter maps application-level patterns and operational caveats.

Learning Goals

  • explore app patterns like BrowserQwen and code-interpreter flows
  • identify safe vs unsafe execution assumptions
  • define environment boundaries for production use
  • document risk controls for tool-executing agents

Safety Notes

  • code-interpreter workflows need sandbox hardening
  • browser and external-tool integrations need explicit trust boundaries
  • production use requires stronger controls than local demos

Source References

Summary

You now have a safer application-design lens for Qwen-Agent deployments.

Next: Chapter 7: Benchmarking and DeepPlanning Evaluation

Source Code Walkthrough

examples/assistant_qwen3.5.py

The test function in examples/assistant_qwen3.5.py handles a key part of this chapter's functionality:

def test(query: str = 'What time is it?'):
    # Define the agent
    bot = init_agent_service()

    # Chat
    messages = [{'role': 'user', 'content': query}]
    response_plain_text = ''
    for response in bot.run(messages=messages):
        response_plain_text = typewriter_print(response, response_plain_text)


def app_tui():
    # Define the agent
    bot = init_agent_service()

    # Chat
    messages = []
    while True:
        query = input('user question: ')
        messages.append({'role': 'user', 'content': query})
        response = []
        response_plain_text = ''
        for response in bot.run(messages=messages):
            response_plain_text = typewriter_print(response, response_plain_text)
        messages.extend(response)


def app_gui():
    # Define the agent
    bot = init_agent_service()

This function is important because it defines how Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows implements the patterns covered in this chapter.

examples/assistant_qwen3.5.py

The app_tui function in examples/assistant_qwen3.5.py handles a key part of this chapter's functionality:

def app_tui():
    # Define the agent
    bot = init_agent_service()

    # Chat
    messages = []
    while True:
        query = input('user question: ')
        messages.append({'role': 'user', 'content': query})
        response = []
        response_plain_text = ''
        for response in bot.run(messages=messages):
            response_plain_text = typewriter_print(response, response_plain_text)
        messages.extend(response)


def app_gui():
    # Define the agent
    bot = init_agent_service()
    chatbot_config = {
        'prompt.suggestions': [
            'Help me organize my desktop.',
            'Develop a dog website and save it on the desktop',
        ]
    }
    WebUI(
        bot,
        chatbot_config=chatbot_config,
    ).run()

This function is important because it defines how Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows implements the patterns covered in this chapter.

examples/assistant_qwen3.5.py

The app_gui function in examples/assistant_qwen3.5.py handles a key part of this chapter's functionality:

def app_gui():
    # Define the agent
    bot = init_agent_service()
    chatbot_config = {
        'prompt.suggestions': [
            'Help me organize my desktop.',
            'Develop a dog website and save it on the desktop',
        ]
    }
    WebUI(
        bot,
        chatbot_config=chatbot_config,
    ).run()


if __name__ == '__main__':
    # test()
    # app_tui()
    app_gui()

This function is important because it defines how Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows implements the patterns covered in this chapter.

examples/visual_storytelling.py

The VisualStorytelling class in examples/visual_storytelling.py handles a key part of this chapter's functionality:

class VisualStorytelling(Agent):
    """Customize an agent for writing story from pictures"""

    def __init__(self,
                 function_list: Optional[List[Union[str, Dict, BaseTool]]] = None,
                 llm: Optional[Union[Dict, BaseChatModel]] = None):
        super().__init__(llm=llm)

        # Nest one vl assistant for image understanding
        self.image_agent = Assistant(llm={'model': 'qwen-vl-max'})

        # Nest one assistant for article writing
        self.writing_agent = Assistant(llm=self.llm,
                                       function_list=function_list,
                                       system_message='你扮演一个想象力丰富的学生，你需要先理解图片内容，根据描述图片信息以后，' +
                                       '参考知识库中教你的写作技巧，发挥你的想象力，写一篇800字的记叙文',
                                       files=['https://www.jianshu.com/p/cdf82ff33ef8'])

    def _run(self, messages: List[Message], lang: str = 'zh', **kwargs) -> Iterator[List[Message]]:
        """Define the workflow"""

        assert isinstance(messages[-1]['content'], list)
        assert any([item.image for item in messages[-1]['content']]), 'This agent requires input of images'

        # Image understanding
        new_messages = copy.deepcopy(messages)
        new_messages[-1]['content'].append(ContentItem(text='请详细描述这张图片的所有细节内容'))
        response = []
        for rsp in self.image_agent.run(new_messages):
            yield response + rsp

This class is important because it defines how Qwen-Agent Tutorial: Tool-Enabled Agent Framework with MCP, RAG, and Multi-Modal Workflows implements the patterns covered in this chapter.

How These Components Connect

flowchart TD
    A[test]
    B[app_tui]
    C[app_gui]
    D[VisualStorytelling]
    E[test]
    A --> B
    B --> C
    C --> D
    D --> E
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