How I Manage 400K Lines of Code with Claude Code: A Multi-Agent Development Workflow

After months of iteration and refinement, I’ve developed a workflow that transforms Claude Code from a single AI assistant into a full engineering team. This approach has allowed me to effectively manage a project approaching 400,000 lines of code and 150,000 lines of markdown planning documents.

Updated: Fixed to work seamlessly with blog update functionality. Server now handles content updates properly without foreign key constraint issues.

Here’s my battle-tested system that turns AI into a force multiplier for complex software development.

The Foundation: Project Structure is Everything

project-overview/
├── ai_docs/
│   ├── features/
│   ├── sprints/
│   ├── requirements/
│   └── architecture/
├── planning/
└── git-submodules/
    ├── frontend-app/
    ├── backend-api/
    └── shared-components/

Key Principle: Always start Claude Code from the planning directory, never from submodules. This ensures consistent context and prevents scope confusion.

Phase 1: Strategic Planning with AI Collaboration

Step 1: Document Architecture

I maintain all planning documents in ai_docs/, creating a single source of truth that both humans and AI can reference:

ai_docs/
├── features/
│   ├── feat1.md
│   ├── feat2.md
│   └── ...
├── architecture/
├── requirements/
└── sprints/

Step 2: Iterative Refinement

• Work with Claude to refine planning documents using @ ai_docs/features/feat1.md
• For deeper analysis, I zip planning docs and upload to ChatGPT o3/o3-pro for comprehensive review
• This dual-AI approach catches blind spots and ensures robust planning

Step 3: Detailed Implementation Planning

Once high-level plans are solid, I prompt Claude:

“Create a detailed implementation plan with goals, requirements, and considerations that may not have been captured in the high-level docs.”

Phase 2: Work Decomposition for Parallel Execution

This is where the magic happens. Instead of sequential development, I break work into parallel-safe tasks:

Sprint Design Process

Feature Requirements
        ↓
Task Decomposition
        ↓
Parallel Safety Analysis
        ↓
Sprint Assignment
        ↓
Agent Task Cards

Sprint Output:
├── sprint1/
│   ├── agent1-tasks.md
│   ├── agent2-tasks.md
│   ├── agent3-tasks.md
│   └── agent4-tasks.md
└── dependencies.md

I ask Claude to:

1. Break features into tasks suitable for parallel development
2. Identify dependencies and potential conflicts
3. Group tasks into sprints based on what’s safe to execute simultaneously
4. Create clear task cards for each “agent”

Quality Gates Before Execution

Before launching agents, I run this verification prompt:

“Have you reviewed these sprint documents against the codebase? Do these requirements make sense based on the documents @ path-to-feature-docs/? Are any changes required to ensure that the agents complete these tasks as if they were staff level engineers with 15+ years of experience?”

This catches ambiguities and ensures each agent has everything needed to work independently.

Phase 3: Multi-Agent Execution

The Launch Sequence

After clearing or compacting the conversation, I launch 4 Claude Code instances in split terminal windows:

# Terminal 1: Agent 1
# Terminal 2: Agent 2  
# Terminal 3: Agent 3
# Terminal 4: Agent 4

Agent Initialization Prompt

Each agent receives a personalized brief:

Please fully review @ sprint1/

You are agent 1, a staff level engineer with over 15 years of 
technical experience. Please get started on your task, ensuring to:
- Use our custom error handler for error handling
- Write tests for expected functionality, not implementation
- Ensure code accuracy and completeness

This is an important project and I know you can do it!

Final note: Run the linter, code analyzer, build and test your 
code before declaring completion.

Do you have any questions before beginning your agent 1 sprint 1 assignment?

Phase 4: Quality Assurance and Integration

Individual Agent Review

After task completion, each agent performs self-review:

“Does your work reflect the quality we’d expect from a staff engineer at OpenAI, Anthropic, or Google? Did you follow our coding guidelines? Did you update your agent document with progress? Are you proud of the work you’ve done?”

The Team Manager Promotion

Here’s where it gets interesting. The agent with the most context (and at least 30% context remaining) gets promoted:

You have been doing great work and have been promoted from staff 
level engineer to team manager. Your first assignment is to perform 
a deeply technical retrospective on this sprint by comparing the 
uncommitted changes to the sprint requirements.

Your goal is to determine if the requirements were met at the quality 
we'd expect from staff engineers, and ensure all requirements were 
fully implemented.

Results and Insights

This workflow has transformed my development velocity by:

1. Parallel Execution: 4x theoretical speedup on parallelizable tasks
2. Consistent Quality: Each “agent” operates at a staff engineer level
3. Comprehensive Coverage: Multiple perspectives catch edge cases
4. Built-in Review: The manager promotion creates natural quality gates

Key Success Factors

Lessons Learned

1. Context is King: Starting from the planning directory maintains coherent project understanding
2. Documentation Investment Pays Off: Time spent on clear specs saves 10x in agent iterations
3. Agents Need Boundaries: Well-defined sprint tasks prevent scope creep
4. Review Culture Matters: Even AI benefits from code review practices

What’s Next?

I’m exploring:

• Automated agent coordination for larger teams (8-12 agents)
• Integration with CI/CD for automatic validation
• Custom tooling for sprint visualization and tracking
• Performance metrics for agent efficiency

Try It Yourself

This workflow scales from small projects to massive codebases. Start with 2 agents on a simple feature and work your way up. The key is maintaining discipline around documentation and sprint planning.

Remember: The goal isn’t to replace human developers but to amplify what one developer can achieve. With this approach, you’re not just coding—you’re conducting an orchestra of AI engineers.

Have questions about implementing this workflow? Found improvements? Let’s connect and push the boundaries of what’s possible with AI-assisted development.