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Claude Code Workflow: A JSON-Driven Multi-Agent Development Orchestration Framework

Claude Code Workflow is a JSON configuration-based multi-agent development framework that supports various AI models such as Gemini, Qwen, and Codex, enabling intelligent workflow orchestration through a context-first architecture.

Claude Code多智能体工作流编排AI辅助开发JSON配置GeminiCodex软件工程
Published 2026-03-29 21:46Recent activity 2026-03-29 21:54Estimated read 6 min
Claude Code Workflow: A JSON-Driven Multi-Agent Development Orchestration Framework
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Section 01

Introduction / Main Floor: Claude Code Workflow: A JSON-Driven Multi-Agent Development Orchestration Framework

Claude Code Workflow is a JSON configuration-based multi-agent development framework that supports various AI models such as Gemini, Qwen, and Codex, enabling intelligent workflow orchestration through a context-first architecture.

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Section 02

New Paradigm for Modern Software Development

With the popularity of AI coding assistants (such as Claude Code, GitHub Copilot, Cursor, etc.), the way software development is done is undergoing fundamental changes. The Claude Code Workflow project represents the evolutionary direction of this trend—from a single AI assistant to a collaborative team of multiple agents, from ad-hoc interactions to structured workflows.

The core idea of this framework is to treat AI as a member of the development team, and through a well-designed orchestration mechanism, enable multiple AI agents to collaborate like a human team to complete complex development tasks.

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Section 03

JSON-Driven Workflow Definition

Claude Code Workflow uses JSON as the configuration language, allowing developers to define the entire development process in a declarative way. The advantages of this approach are:

  • Version Controllable: Workflow definitions can be version-managed like code
  • Easy to Understand: The JSON structure is clear, enabling team members to quickly grasp the process
  • Flexible Expansion: New steps can be easily added or existing logic modified
  • Tool-Friendly: Facilitates integration with other DevOps tools
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Section 04

Multi-Model Intelligent Orchestration

The framework's uniqueness lies in its support for simultaneous invocation of multiple different AI models:

  • Gemini: Suitable for long-context understanding and code generation
  • Qwen: Excels in Chinese scenarios and multilingual processing
  • Codex: Focuses on code understanding and generation

Through intelligent routing, the framework can automatically select the most appropriate model based on task type, or allow multiple models to collaborate to complete complex tasks.

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Section 05

Context-First Architecture

Traditional AI-assisted development often suffers from context loss issues. Claude Code Workflow adopts a context-first design philosophy:

  • Project-Level Context: Maintains the structure and dependency relationships of the entire project
  • Task-Level Context: Tracks the progress of current tasks and historical decisions
  • Code-Level Context: Understands the interaction relationships of related code modules

This layered context management ensures that AI agents always have sufficient information to make correct decisions.

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Section 06

Workflow Types and Usage Scenarios

The framework supports multiple predefined workflow modes:

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Section 07

Cadence Team Development

This is the core workflow mode of the framework, simulating the iterative rhythm in agile development:

  • Planning Phase: AI agents analyze requirements and formulate implementation plans
  • Implementation Phase: Multiple agents develop different modules in parallel
  • Review Phase: Specialized review agents check code quality
  • Integration Phase: Coordinate the integration and testing of various modules
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Section 08

Intelligent CLI Orchestration

The framework provides a powerful command-line interface (CLI) that supports:

  • One-click launch of a complete workflow
  • Real-time viewing of the execution status of each agent
  • Interactive intervention and adjustment of the process
  • Collection and analysis of execution logs