Cursor Agent Workflow: A Modular Multi-Agent Workflow Built for Cursor IDE

This article introduces Cursor Agent Workflow, a modular multi-agent collaboration framework built for the Cursor IDE. Through three core sub-agents (orchestrator, coder, code reviewer), combined with a skill library, CLI toolchain, and GitHub/Vercel integration, this framework enables end-to-end automated development workflows from requirement analysis to code merging, solving the problem of systematic organization for multi-AI agent collaboration.

With the popularity of AI-assisted programming tools, Cursor has become one of the preferred IDEs for developers due to its powerful Agent features. However, developers face the problem of how to systematically organize multi-AI agents to collaborate on complex tasks. The cursor-agent-workflow was born to address this—it is not a preset prompt template but a complete multi-agent workflow framework that enhances Cursor Agent capabilities through role division and state transition mechanisms.

The workflow adopts a three-tier architecture:

1. Orchestrator: As the entry point, it is responsible for requirement parsing, task decomposition, resource evaluation, and plan generation; manual confirmation is required at key decision points.
2. Coder: Takes over implementation tasks, completes code generation, file operations, dependency management, and unit testing, with deep integration of Cursor's file editing capabilities.
3. Code Reviewer: Performs quality checks, style consistency verification, and best practice validation, generates review reports, and enters the merging phase after manual confirmation.

Skill Library: Modular design, including Task Agents (e.g., GitHub integration agent), Utility Skills (general tools), and Domain Skills (domain-specific skills), dynamically loaded via cwf add. CLI Toolchain: The cwf command system, core commands include cwf init (initialization), cwf add (add module), cwf sync (synchronize updates), etc., with module status tracked via .env files.

Integration Practices:

• GitHub: Create a private repository via cwf github --private, and manage sensitive information with gistenv;
• Vercel: cwf add vercel generates deployment configurations, environment variable templates, and guidelines. Working Modes:
• Normal mode: Single directory structure, suitable for individuals/small teams;
• Bare mode: Separated structure (.bare/, main/, development/), suitable for medium to large teams with strict branch management.

Application Scenarios: Feature iteration development, code refactoring, tech stack migration, rapid prototype validation. Summary: This framework represents the direction of AI-assisted development evolving toward team collaboration automation. Through reasonable architecture and role division, AI agents become powerful assistants in development. For Cursor users, after getting familiar with it, they can achieve a qualitative efficiency change from "writing code" to "directing code to be written well".