Build production AI systems 3-5x faster with specialized agents that handle requirements, architecture, coding, and deployment automatically.

Status: ⚠️ Alpha - Untested in production. Use at your own risk.
Version: See .repo-metadata.json for current version and agent counts.

15 minutes: Custom GPTs, Claude assistants, Cursor IDE modes
2 hours: Complete architecture + cost estimates
2-5 days: Working prototypes with tests

Complete lifecycle: Requirements → Architecture → Code → Deployment

# 1. Clone git clone https://github.com/paulpham157/multi-agent-ai-development-framework cd multi-agent-ai-development-framework # 2. Deploy (Cursor → Settings → Chat → Custom Modes) # Create "Supervisor Agent" # Paste: supervisor_agent.system.prompt.md # Enable: "All tools" → Save # 3. Use # Open AI Pane (Ctrl+Shift+L) # Try: "Build a Streamlit chatbot with Claude"

Done! Supervisor routes to specialized agents who generate code for you to review.

Other platforms: See docs/getting-started.md for Claude Projects and GitHub Copilot setup.

The Problem: Every AI project starts from scratch

• Requirements: 2 days → should be 2 hours
• Architecture: 2 weeks → should be 4 hours
• Prototypes: 2 months → should be 1 week

The Solution: Specialized agents automate 70% of repetitive work

Result: 3-5x faster delivery, 60% less rework, consistent quality

Main Supervisor → Routes your requests

Top-Level Domain Agents (5):

• Requirements: Discovers what you need (15-90 min workshops)
• Architecture: Designs system + estimates costs (AWS Well-Architected)
• Deployment: Creates platform-specific deployment guides
• Optimization: Analyzes and improves existing systems
• Prompt Engineering: Creates production-quality prompts

Engineering Supervisor (1): Coordinates 16 technology specialists

Engineering Specialists (16):

• Anthropic Claude (5): Code, Workspaces, SDK, MCP, Projects
• AWS Bedrock (2): AgentCore, Strands
• Other (9): Streamlit, LangChain, data, AWS infra/security, testing, GitHub, Cursor

See: docs/engineering-agents-guide.md for complete specialist reference

You: "Build a document summarization app with Streamlit and Claude" Engineering Supervisor routes to specialists: → Streamlit UI Agent: Chat interface + file upload → Claude Code Agent: Claude SDK integration + streaming → Testing Agent: pytest suite with mocks Output: ✅ streamlit_app.py, claude_client.py, tests/ ✅ requirements.txt, README.md, .env.example ✅ 80% test coverage, production-ready YOU: Review → Run locally → Deploy 

Result: Working prototype same day

You: "Optimize my Claude chatbot at outputs/my-chatbot/" Optimization Agent: → Discovers system structure → Assesses Well-Architected compliance (score: 6.2/10) → Proposes improvements (reduce tokens 30%, add caching) → Implements (with your approval) → Validates improvements Result: ✅ Monthly cost: $120 → $78 (-35%) ✅ Response time: 3.2s → 2.4s (-25%) ✅ Well-Architected: 6.2 → 8.1 (+30%) 

Result: Measurable improvements backed by data

More examples: See docs/workflow_guide.md and docs/examples/

Every architecture evaluated against 6 pillars + GenAI Lens:

• Operational Excellence • Security • Reliability
• Performance • Cost • Sustainability
• Model Selection • Prompt Engineering • RAG • Multi-Agent • Responsible AI

Test-Time Recursive Majority ensures quality:

• Generate → Validate → Improve → Re-validate
• Only present outputs meeting quality benchmarks
• Code coverage ≥80%, type hints ≥90%, 0 critical security issues

17 improvement prompts for continuous enhancement

• System-wide optimization
• Individual agent improvements
• 2-3 iterations per session (practical recursion prevention)

knowledge_base/system_config.json contains:

• 150+ technical documentation URLs
• AWS Well-Architected definitions
• Research papers (TRM, MetaGPT)
• Design patterns
• Quality benchmarks

✅ Junior Engineers: Learn from generated code, ship without years of experience
✅ Senior Engineers: Eliminate boilerplate, focus on complex decisions, 3-5x faster
✅ Consultants: Professional proposals in hours, accurate estimates
✅ Managers: Standardize processes, 5x faster onboarding
✅ Architects: Systematic Well-Architected designs, evidence-based recommendations
✅ CTOs: De-risk AI investments, scale without proportional hiring

Cursor IDE (Recommended - 5 min):

1. Settings → Chat → Custom Modes 2. Create "Supervisor Agent", paste supervisor_agent.system.prompt.md 3. Enable "All tools" → Save 

Claude Projects (10 min):

1. Create project at https://claude.ai/projects (requires Anthropic account) 2. Upload knowledge_base/*.json 3. Custom Instructions: supervisor_agent.system.prompt.md 

GitHub Copilot (15 min):

1. Create .github/copilot-instructions.md 2. Paste supervisor_agent.system.prompt.md 3. Use @workspace in VS Code 

Full guide: docs/deployment-guide.md

Core: Python 3.12+ • Streamlit • Anthropic Claude • AWS Bedrock • MCP • LangChain

16 Engineering Specialists cover:

• 5 Anthropic Claude specialists
• 2 AWS Bedrock specialists
• 9 Other specialists (UI, orchestration, data, AWS infra/security, testing, platforms)

Centralized Docs: 150+ technical URLs in knowledge_base/system_config.json → technical_references

Two-layer supervisor-worker pattern:

 Supervisor Agent ↓ ┌───────────────┼───────────────┐ ↓ ↓ ↓ Requirements Architecture Engineering Supervisor ↓ ↓ ↓ │ │ ┌──────┼──────┐ │ │ ↓ ↓ ↓ │ │ Streamlit Claude AWS │ │ UI Code Bedrock │ │ +14 more specialists │ ↓ └───────→ Deployment + Optimization Shared Knowledge Base: ├─ system_config.json (platform constraints, tech refs) ├─ user_requirements.json (business requirements) └─ design_decisions.json (architecture, costs, plans) 

See: ARCHITECTURE.md for complete details

Agents do (you review):

• ✅ Generate code/docs/configs
• ✅ Analyze systems
• ✅ Recommend improvements
• ✅ Validate quality

YOU do (always):

• ✅ Review all outputs
• ✅ Approve architectures
• ✅ Execute deployments
• ✅ Make critical decisions

Agents NEVER:

• ❌ Commit code automatically
• ❌ Deploy to production
• ❌ Make business decisions
• ❌ Spend money without approval

See: docs/human-ai-collaboration.md for complete guide

Essential (start here):

• README.md - This file (overview + quick start)
• docs/getting-started.md - First project walkthrough (15 min)
• docs/deployment-guide.md - Platform deployment
• docs/human-ai-collaboration.md - Your role vs agent role

Reference:

• docs/workflow_guide.md - Complete workflows
• docs/engineering-agents-guide.md - All 16 specialists
• docs/executive_overview.md - Business value
• ARCHITECTURE.md - System architecture
• knowledge_base/README.md - Knowledge base guide
• templates/ - Requirements, architecture, checklists
• private/README.md - Security guidelines for sensitive data

multi-agent-ai-development-framework/ ├── .repo-metadata.json # Single source of truth (version, counts) ├── ai_agents/ # Agent system prompts │ ├── supervisor_agent.system.prompt.md (main entry point) │ ├── [5 top-level domain agents] │ ├── engineering_supervisor_agent.system.prompt.md │ └── [16 specialist agents] ├── knowledge_base/ # Shared state across agents │ ├── system_config.json (150+ tech refs, Well-Architected defs, validation framework) │ ├── user_requirements.json │ ├── design_decisions.json │ └── schemas/ (JSON schemas for validation) ├── user_prompts/ # Task-specific instructions ├── docs/ # Documentation ├── templates/ # Reusable templates ├── tests/ # Validation tests (auto-update metadata) ├── outputs/ # Generated systems go here └── private/ # Sensitive data (NEVER committed to Git) ├── README.md (security guidelines) └── sensitive-ai-agent-outputs/ (protected AI outputs) See .repo-metadata.json for current agent/prompt counts 

⚠️ Current Status: 0.1.0-alpha - Untested in production

Note: Repository is under development locally, not yet published to GitHub.

What works:

• ✅ All agents functional
• ✅ Complete workflows (requirements → deployment)
• ✅ Code generation quality-assured (TRM validation)
• ✅ Well-Architected enforcement
• ✅ Self-improvement system

Known limitations:

• ⚠️ No production validation yet
• ⚠️ Breaking changes expected before v1.0
• ⚠️ Some edge cases untested
• ⚠️ Documentation evolving (run user_prompts/self_improvement/improve_all_documentation.user.prompt.md for latest)

Use at your own risk. Report issues on GitHub. Production-ready in v1.0.

Documentation: Start with docs/getting-started.md
Issues: GitHub Issues for bugs/features
Discussions: GitHub Discussions for questions
Contributing: See CONTRIBUTING.md

MIT License - Full commercial use permitted

• GitHub: Repository available locally (not yet published to GitHub)
• Getting Started: docs/getting-started.md
• Deployment Guide: docs/deployment-guide.md
• Engineering Specialists: docs/engineering-agents-guide.md
• Workflows: docs/workflow_guide.md

Built with: Python • Streamlit • Anthropic Claude • AWS Bedrock • MCP • LangChain

🚀 Start building: Install Supervisor Agent in Cursor and say "Build a chatbot"