orchestration

To automate frontend and backend tasks from natural language prompts, the orchestration workflow extracts conversational intents, maps them to relevant rules in Context/ or .cursor/rules/, and executes workflows to implement the changes.

Intent orchestration for feature planning and bug fixes parses conversational requests, categorizes them into specific task types, loads necessary context files, and applies selected Cursor rules to execute the implementation.

Yes, you can implement roadmap tasks without manual context loading or @ mentions by describing work in natural language, which triggers automatic intent parsing and context retrieval from Context/ and .ai/context/ directories.

The best way to apply Cursor rules to conversational feature requests is using an orchestration workflow that extracts user intent, selects applicable rules from .cursor/rules/, and automatically reads required files into context to apply changes.

No, you do not need to manually select Cursor rules for backend work described in conversation, because the agent parses natural language intent and automatically selects applicable rules from your context directories.

You should not use natural language intent mapping for code changes if your task requires precise manual control over specific files, lacks conversational context, or needs direct rule applications outside standard Context/ and .cursor/rules/ workflows.