qwen-native-agents

Qwen models often fail with generic prompts because they expect specific native dialect conventions. Using the exact ChatML templates, XML-based tool-calling formats, and concise system prompt structure Qwen was trained on ensures reliable agent behavior and accurate tool execution.

The correct tool-calling format depends on the specific Qwen model family and includes NOUS, coder-XML, or VL-JSON formats. Applying the exact XML-based tool-calling conventions configured for your specific Qwen model ensures proper function execution and parsing.

To enable thinking mode for Qwen models, apply precise configuration instructions that dictate when and how the mode activates. Properly enabling thinking mode maximizes tool-calling accuracy and improves instruction following for complex task-automation agents.

Yes, Qwen3.6-35b requires a native dialect system prompt structure featuring a concise, direct, GitHub-flavored Markdown tone. Applying this specific structure ensures the coding agent processes instructions correctly and avoids overly verbose or unpredictable outputs.

Generic prompt-engineering best practices cause Qwen models to behave unpredictably, resulting in overly verbose outputs and incorrect tool-calling. Limitations arise from ignoring the native ChatML templates and XML-based tool-calling formats Qwen models were specifically trained on.