AI Development Team: An AI Development Team Integrated in the Editor

AI Development Team is an innovative AI-assisted development system integrated into the editor, simulating the structure of a real development team. It includes about 48 specialized AI agents and provides a structured development experience through enforced workflows and approval mechanisms. This project is open-source with no lock-in, supporting editors like Claude Code, Cursor, Kiro, and VS Code. Its core philosophy is "Process, not Prompts", aiming to solve the instability issues of traditional AI code tools.

The core design philosophy of the project is "Process, not Prompts". Traditional AI code completion tools rely on developers writing complex prompts, leading to unstable results; whereas this system enables AI agents to collaborate through standardized workflows, with the following advantages:

• Predictability: More stable and consistent results
• Reusability: Workflows can be reused across projects
• Maintainability: Workflows can be version-controlled and optimized
• Collaboration: Multiple agents collaborate according to the workflow This philosophy aims to provide a structured and predictable AI-assisted development experience.

Team Structure

It includes about 48 professional agents covering teams such as requirement analysis (product managers, business analysts, etc.), design (architects, UI/UX, etc.), development (front-end, back-end, DevOps, etc.), QA (testing, code review, etc.), operation and maintenance, and documentation.

Workflow Design

1. Enforced Workflow: Each phase (requirements/design/development/testing) has fixed steps that cannot be skipped (e.g., the requirements phase includes collection → analysis → feasibility assessment → documentation → review).
2. Proportional Workflow: Adjust the number of agents based on task complexity (1-2 for simple tasks, 6-10 for complex ones, and the full team for major projects).
3. Approval Nodes: Key nodes require manual approval (requirements confirmation, design finalization, code merging, launch).

Supported Editors

• Claude Code: Native integration, full workflow + real-time collaboration
• Cursor: Plugin form, chat window extension + code editing enhancement
• Kiro: Deep integration, workflow visualization + team collaboration
• VS Code: Extension plugin, command palette integration + status bar display

Usage Examples

• Start a new project: After the user proposes requirements, agents execute the requirements → design process, requiring user approval at nodes.
• Add a feature: For example, a payment feature—agents sequentially analyze requirements → design a solution → implement → test, waiting for approval.
• Code review: Agents check PR issues and security vulnerabilities, provide suggestions, then wait for the user's merge decision.

Configuration Customization

Agents (e.g., the architect's professional field, front-end development framework) and workflows (custom steps and approval nodes) can be configured via YAML.

Core Advantages

1. Structured development: Enforced workflows reduce errors and improve code quality
2. Specialized division of labor: Each agent focuses on a specific field, delivering high-quality outputs
3. Auditability: Complete execution records, traceable decisions
4. No lock-in: Open-source project, supports multiple editors, configurable to export
5. Progressive adoption: Enable part of the agents or workflows as needed

Tool Comparison

Feature	AI Dev Team	GitHub Copilot	Cursor AI
Multi-agent collaboration	Yes	No	Limited
Workflow management	Complete	None	Basic
Approval mechanism	Yes	No	No
Specialized agents	48	General	Limited
Open-source	Yes	No	Partial

Limitations

1. Initial configuration: Time is needed to customize agents and workflows
2. Learning curve: Developers need to adapt to the new working method
3. Flexibility: Strict workflows may reduce flexibility
4. Cost: Running multiple agents increases computational costs

Summary

AI Development Team represents the evolutionary direction of AI-assisted programming—from code completion to team collaboration. It is suitable for enterprise-level, complex projects or scenarios requiring strict process control. Despite existing challenges, its structured solution provides value for high-quality development, and it is expected to drive the emergence of more systematic AI development tools in the future.