SAAT - Solution Architecture Agent Toolkit

🤖 AI-Powered Software Architecture Analysis & Quality Assurance Platform

📋 Table of Contents

What is SAAT?
Quick Start
Installation
Usage Guides
Core Concepts
Available Agents
Documentation
Examples
Contributing
Appendix

🎯 What is SAAT?

SAAT is an AI-powered architecture quality assurance platform that helps you:

Analyze existing codebases (brownfield) - Discover architecture automatically
Design new systems (greenfield) - Extract requirements and generate architecture
Evaluate quality - Score architecture against 14 standard characteristics
Validate compliance - Check against PCI-DSS, HIPAA, GDPR, SOC2
Analyze security - Identify vulnerabilities and generate threat models
Generate documentation - Create comprehensive docs in multiple formats
Create infrastructure - Generate production-ready Terraform for AWS/Azure/GCP

Why SAAT?

Traditional architecture work is manual and time-consuming. SAAT automates the entire process:

Traditional Approach:          SAAT Approach:
┌─────────────────┐           ┌─────────────────┐
│ Manual Analysis │           │ /saat-discover  │
│ (days/weeks)    │           │ (minutes)       │
└─────────────────┘           └─────────────────┘
         ↓                             ↓
┌─────────────────┐           ┌─────────────────┐
│ Draw Diagrams   │           │ /saat-generate  │
│ (hours)         │           │ (automatic)     │
└─────────────────┘           └─────────────────┘
         ↓                             ↓
┌─────────────────┐           ┌─────────────────┐
│ Write Docs      │           │ /saat-analyze   │
│ (days)          │           │ (with scores)   │
└─────────────────┘           └─────────────────┘

Key Features

✅ 9 Specialized AI Agents - Discovery, Generation, Requirements, Quality Analysis, Validation, Security, Documentation, Infrastructure, Orchestration ✅ Architecture Quality Analysis - Evaluate against 14 characteristics (Mark Richards methodology) ✅ Conversational Slash Commands - Type /saat- in Claude Code for guided workflows ✅ C4 Model Standard - Industry-standard architecture diagrams ✅ Multi-Model Support - Claude, GPT-4, Gemini, Ollama ✅ Human-in-the-Loop - Interactive approval with auto-approve mode ✅ Production Ready - Retry logic, streaming, error handling

🚀 Quick Start

The Easy Way: Claude Code Slash Commands (Recommended)

Install SAAT and configure Claude Code:

# Clone and install SAAT
git clone https://github.com/DavidROliverBA/SAAT.git
cd SAAT
pip install -e .

# Install Claude Code slash commands
./install-claude-commands.sh

# Set API key
export ANTHROPIC_API_KEY="your-key"

Analyze an existing project (Brownfield):

Open Claude Code in your project directory, then:

You: /saat-orchestrate

Claude: I'll help you analyze your architecture!
        What would you like to do?
        1. Analyze existing codebase (brownfield)
        2. Design new system (greenfield)
        3. Review specific architecture aspect

You: Analyze my existing codebase

Claude: [Runs discovery, generates C4 model, analyzes quality,
        validates compliance, checks security, generates docs]

        ✅ Complete analysis finished!

        📊 Results Summary:
        - Architecture Model: 5 systems, 12 containers, 28 components
        - Quality Score: 68/100 (Needs Improvement)
        - Compliance: 85/100 (PCI-DSS)
        - Security: 3 critical issues found
        - Documentation: Generated in docs/

        🚨 Top Priorities:
        1. Fix critical security issue: Unencrypted database connection
        2. Add load balancer for availability (currently 55/100)
        3. Implement auto-scaling for scalability

        Next: Would you like me to generate Terraform to deploy fixes?

Design a new system (Greenfield):

You: /saat-orchestrate

Claude: What would you like to do?

You: Design a new payment processing system

Claude: I'll guide you through designing your architecture.

        Do you have requirements documents? If so, please provide paths.
        Otherwise, I can help you capture requirements conversationally.

You: I have docs/requirements.md

Claude: [Extracts requirements, generates architecture, analyzes quality,
        validates, checks security, generates infrastructure]

        ✅ Architecture design complete!

        📊 Your New System:
        - 3 systems, 8 containers, 15 components
        - Quality Score: 82/100 (Good)
        - Compliance: PCI-DSS ready (98/100)
        - Security: All checks passed
        - Infrastructure: AWS Terraform ready to deploy

        Files created:
        - architecture.json (C4 model)
        - docs/ (comprehensive documentation)
        - infrastructure/ (Terraform IaC)

        You can now: terraform apply to deploy!

Individual Commands

All commands are conversational and guide you step-by-step:

/saat-orchestrate - Start here! Recommends what to do based on your needs
/saat-discover - Analyze existing codebase
/saat-requirements - Extract requirements from documents
/saat-generate - Create architecture model
/saat-analyze-characteristics - Evaluate architecture quality
/saat-validate - Check compliance
/saat-security - Analyze security
/saat-document - Generate documentation
/saat-terraform - Create infrastructure code
/saat-help - Show all commands

See: Complete Usage Guides for detailed walkthroughs.

📦 Installation

Step 1: Install SAAT

# Clone repository
git clone https://github.com/DavidROliverBA/SAAT.git
cd SAAT

# Install with pip
pip install -e .

# Or with all extras (Logfire, OpenAI, Gemini)
pip install -e ".[all]"

# Or with Poetry
poetry install

Step 2: Set API Key

# Create .env file
cp .env.example .env

# Add your API key
echo "ANTHROPIC_API_KEY=your-key-here" >> .env

# Optional: Choose default model
echo "SAAT_MODEL=anthropic:claude-sonnet-4" >> .env

Step 3: Configure Claude Code (Recommended)

Option A: Install Slash Commands Globally (Available in all projects)

./install-claude-commands.sh

Option B: Per-Project (Already available when you run Claude Code from SAAT directory)

# Commands are in .claude/commands/ - no installation needed!

Configure MCP Server (Required for slash commands to work):

Add to ~/.config/claude/config.json:

{
  "mcpServers": {
    "saat": {
      "command": "python",
      "args": ["/path/to/SAAT/saat_mcp_server.py"],
      "env": {
        "ANTHROPIC_API_KEY": "${env:ANTHROPIC_API_KEY}"
      }
    }
  }
}

Then restart Claude Code.

Step 4: Verify Installation

# Check SAAT CLI
saat --version
saat info

# In Claude Code, type:
# /saat-help
# You should see all available commands

📖 Usage Guides

For New Users: Start Here!

The Orchestrator (/saat-orchestrate) is your starting point. It will:

Ask you what you want to do
Recommend which agents to run
Guide you through the entire process
Keep you updated on progress
Ask for help when it needs information

Detailed Step-by-Step Guides

📘 Greenfield Guide - Designing a new system from scratch

Extract requirements from documents
Generate architecture automatically
Evaluate quality before building
Generate deployment infrastructure
Complete walkthrough with examples

📗 Brownfield Guide - Analyzing existing codebases

Discover architecture from code
Identify quality issues
Check compliance and security
Generate missing documentation
Create infrastructure-as-code
Complete walkthrough with examples

📙 Customization Guide - Tailor SAAT to your needs

Customize agent behavior
Add custom validation rules
Create custom architecture characteristics
Extend with your own patterns
Build custom agents

🧠 Core Concepts

The 9 Agents

SAAT uses specialized AI agents, each expert in one area:

🎯 Orchestrator Agent ⭐ NEW - Coordinates all other agents, creates plans, guides users
🔍 Discovery Agent - Analyzes codebases to discover architecture
📋 Requirements Agent - Extracts requirements from documents
🏗️ Generator Agent - Creates C4 architecture models
📊 Architecture Characteristics Agent - Evaluates quality against 14 standards
✅ Validation Agent - Validates compliance (PCI-DSS, HIPAA, GDPR, SOC2)
🔒 Security Agent - Analyzes security and creates threat models
📚 Documentation Agent - Generates docs (Markdown, PlantUML, Mermaid, ADRs)
🏗️ Terraform Agent - Creates infrastructure-as-code

The C4 Model

SAAT uses the C4 model for architecture:

Level 1 - System Context: High-level view of systems and external dependencies
Level 2 - Container: Applications, databases, microservices
Level 3 - Component: Code-level modules and services

Architecture Characteristics

SAAT evaluates architecture against 14 standard characteristics from Mark Richards:

Operational (7): Availability, Scalability, Performance, Security, Reliability, Fault Tolerance, Recoverability

Structural (5): Maintainability, Testability, Deployability, Configurability, Extensibility

Cross-Cutting (2): Interoperability, Usability

Each characteristic gets:

Score (0-100)
Status (Fully Compliant, Mostly Compliant, Partially Compliant, Non-Compliant)
Gap Analysis with severity levels (critical, high, medium, low)
Recommendations with implementation steps

Criticality Levels

SAAT assigns criticality to guide infrastructure decisions:

Level	Uptime	Infrastructure	Use Case
CS1	99.99%	Multi-AZ, auto-scaling 2-10, 35d backups	Mission critical (payments, auth)
CS2	99.9%	Multi-AZ, auto-scaling 2-5, 7d backups	Business critical (APIs, databases)
SL1	99.5%	Single-AZ, basic monitoring, 3d backups	Standard services
SL2	99%	Single-AZ, minimal config, 3d backups	Internal tools
STANDARD	Best effort	Minimal, no backups	Development, testing

🤖 Available Agents

1. 🎯 Orchestrator Agent ⭐ NEW

Purpose: Your AI architecture consultant that coordinates all other agents

The Orchestrator Agent is your primary interface to SAAT. It:

Understands your goals through conversation
Recommends what to do based on your situation
Creates a plan and gets your approval
Coordinates agents to execute the plan
Keeps you updated with progress reports
Asks for help when it needs documentation or clarification

Usage:

/saat-orchestrate

Example Conversation:

You: /saat-orchestrate

Orchestrator: Hello! I'm your SAAT architecture consultant.
              I'll help you with architecture discovery, design,
              quality analysis, validation, and infrastructure.

              What brings you here today?
              1. I have an existing codebase I want to analyze
              2. I want to design a new system
              3. I have an architecture I want to review
              4. I'm not sure - help me figure it out

You: I want to analyze my existing payment processing application

Orchestrator: Great! I'll help you analyze your payment app.

              First, let me understand your situation:
              - Where is the codebase? (path)
              - Do you have any existing architecture docs?
              - What are your main concerns? (security, performance, etc.)
              - Any compliance requirements? (PCI-DSS, etc.)

You: The code is in /app/payment-system, no existing docs,
     I'm concerned about PCI-DSS compliance and security

Orchestrator: Perfect! Based on what you've told me, here's my plan:

              📋 Analysis Plan:
              1. ✅ Discover architecture from /app/payment-system
              2. ✅ Generate C4 model
              3. ✅ Analyze architecture quality (14 characteristics)
              4. ✅ Validate PCI-DSS compliance
              5. ✅ Deep security analysis with threat modeling
              6. ✅ Generate comprehensive documentation
              7. ✅ Create infrastructure-as-code (if needed)

              Estimated time: 5-10 minutes

              Does this plan look good? (y/n)

You: yes

Orchestrator: Excellent! Starting analysis...

              [1/7] 🔍 Discovering architecture...
              Found: 3 services (payment-api, auth-service, notification-service)
              Technologies: Node.js, PostgreSQL, Redis, RabbitMQ
              Pattern: Microservices with event-driven communication
              ✅ Discovery complete!

              [2/7] 🏗️ Generating C4 model...
              Created: 3 systems, 8 containers, 15 components
              ✅ Model generated!

              [3/7] 📊 Analyzing architecture quality...
              Overall Score: 68/100 (Needs Improvement)
              Critical Issues: 2 (Availability, Security)
              ✅ Analysis complete!

              [4/7] ✅ Validating PCI-DSS compliance...
              Compliance Score: 72/100
              ⚠️ Found 5 compliance issues
              ✅ Validation complete!

              [5/7] 🔒 Running security analysis...
              🚨 CRITICAL: Unencrypted database connections
              🚨 CRITICAL: Missing authentication on admin endpoints
              ⚠️ HIGH: Weak password hashing (MD5)
              ✅ Security scan complete!

              [6/7] 📚 Generating documentation...
              Created: README.md, architecture diagrams, ADRs
              ✅ Documentation complete!

              [7/7] 🏗️ Creating infrastructure code...
              Generated: AWS Terraform with PCI-DSS configuration
              ✅ Infrastructure code ready!


              🎉 Analysis Complete!

              📊 Summary:
              - Quality: 68/100 (Needs Improvement)
              - Compliance: 72/100 (PCI-DSS)
              - Security: 🚨 2 CRITICAL, 1 HIGH, 3 MEDIUM issues

              🚨 TOP PRIORITIES:
              1. Fix unencrypted database connections (CRITICAL)
              2. Add authentication to admin endpoints (CRITICAL)
              3. Upgrade password hashing to bcrypt (HIGH)
              4. Add load balancer for high availability
              5. Implement auto-scaling

              📁 Generated Files:
              - architecture.json (C4 model)
              - archchar-analysis.md (quality report)
              - validation-report.json (PCI-DSS)
              - security-report.json (threat model)
              - docs/ (comprehensive documentation)
              - infrastructure/ (Terraform)

              What would you like to do next?
              1. Review the security issues in detail
              2. See recommendations for fixing availability
              3. Deploy the infrastructure
              4. Something else

See: docs/ORCHESTRATOR_GUIDE.md for complete guide.

2-9. Specialized Agents

The Orchestrator coordinates these specialized agents as needed:

🔍 Discovery Agent - Analyzes codebases (brownfield)
📋 Requirements Agent - Extracts requirements (greenfield)
🏗️ Generator Agent - Creates C4 models
📊 Architecture Characteristics Agent - Evaluates quality
✅ Validation Agent - Checks compliance
🔒 Security Agent - Analyzes security
📚 Documentation Agent - Generates docs
🏗️ Terraform Agent - Creates infrastructure

Each agent has its own slash command if you want to use it directly:

/saat-discover
/saat-requirements
/saat-generate
/saat-analyze-characteristics
/saat-validate
/saat-security
/saat-document
/saat-terraform

For detailed documentation on each agent, see:

docs/AGENTS_QUICK_REFERENCE.md - All agents overview
Appendix: Agent API Reference - Python API for each agent

📚 Documentation

Main Guides

README.md - This document (overview and quick start)
GREENFIELD_GUIDE.md - Complete greenfield workflow
BROWNFIELD_GUIDE.md - Complete brownfield workflow
CUSTOMIZATION_GUIDE.md - How to customize SAAT

Agent Documentation

ORCHESTRATOR_GUIDE.md - Complete orchestrator guide
AGENTS_QUICK_REFERENCE.md - All agents overview
ARCHITECTURE_CHARACTERISTICS_USAGE.md - Quality analysis guide
ARCHCHAR_INTEGRATION_ANALYSIS.md - Integration patterns

Integration Guides

BAC4_INTEGRATION.md - Visual editor integration
.claude/README.md - Slash commands reference

Technical Documentation

ARCHITECTURE.md - SAAT system architecture
CHANGELOG.md - Version history and roadmap
RELEASE_NOTES.md - Release information

💡 Examples

Example 1: Quick Architecture Review

You: /saat-orchestrate

Orchestrator: What would you like to do?

You: Quick review of my microservices app in ./services

Orchestrator: [Runs discovery → generate → analyze → validate → security]

              ✅ Quick Review Complete!

              Quality: 75/100 (Good)
              Top Issues:
              1. No load balancer (availability: 60/100)
              2. Missing auto-scaling (scalability: 65/100)
              3. No caching layer (performance: 70/100)

              Recommendation: Add these to improve to 90/100

Example 2: Design Payment System

You: /saat-orchestrate

You: Design a PCI-DSS compliant payment system

Orchestrator: Do you have requirements docs?

You: Yes, in docs/requirements.md

Orchestrator: [Extracts requirements → generates architecture →
              analyzes quality → validates PCI-DSS →
              checks security → generates infrastructure]

              ✅ Design Complete!

              Your system:
              - 3 systems, 8 containers, 15 components
              - Quality: 88/100 (Excellent)
              - PCI-DSS: 98/100 (Ready for certification)
              - Infrastructure: AWS Terraform ready

              You can deploy with: cd infrastructure && terraform apply

🤝 Contributing

Contributions welcome! Please:

Fork the repository
Create a feature branch
Make your changes with tests
Submit a pull request

Development Setup

git clone https://github.com/DavidROliverBA/SAAT.git
cd SAAT
pip install -e ".[dev]"

📄 License

MIT License - See LICENSE file

🙏 Acknowledgments

PydanticAI - Agent framework
C4 Model - Architecture visualization methodology
Mark Richards - Architecture characteristics methodology
Structurizr - JSON schema for C4 models

Built with ❤️ using PydanticAI

📎 Appendix

Appendix A: CLI Reference

For users who prefer command-line interface over conversational commands.

Discovery & Generation

saat discover --path /repo --output discovery.json
saat generate --discovery discovery.json --output architecture.json
saat analyze --path /repo --output architecture.json  # Combined

Requirements (Greenfield)

saat discover-requirements \
  -f docs/PRD.md \
  -n "Project Name" \
  -o requirements.json

Quality Analysis

saat analyze-characteristics \
  -m architecture.json \
  -c characteristics.json \
  -o archchar-analysis

Validation

saat validate-model -m architecture.json -f PCI-DSS
saat validate-model -m architecture.json -f HIPAA

Security

saat security-scan -m architecture.json --threat-model

Documentation

saat generate-docs \
  -m architecture.json \
  -f markdown -f plantuml -f mermaid \
  -o docs/

Infrastructure

saat generate-terraform -m architecture.json -p aws -r us-east-1 -o infrastructure/

Global Options

# Auto-approve (skip prompts)
saat -y validate-model -m architecture.json

# Use different model
saat --model openai:gpt-4 analyze --path /repo

Full CLI documentation: Run saat --help or saat <command> --help

Appendix B: MCP Tools Reference

For developers integrating SAAT into other applications.

Available MCP tools:

orchestrate - Start orchestrated workflow
discover_architecture - Analyze repository
discover_requirements - Extract requirements
generate_c4_model - Generate model
analyze_architecture_characteristics - Evaluate quality
validate_model - Validate compliance
analyze_security - Security analysis
generate_documentation - Create docs
generate_terraform - Infrastructure code
full_analysis - Complete workflow

All tools use auto_approve=True for seamless integration.

Appendix C: Agent API Reference

For Python developers using SAAT programmatically.

Orchestrator Agent

from saat.agents import OrchestratorAgent

agent = OrchestratorAgent()
result = await agent.orchestrate(
    user_goal="Analyze my payment system",
    context={
        "codebase_path": "/app/payment",
        "compliance": ["PCI-DSS"],
        "concerns": ["security"]
    },
    auto_approve=False
)

Discovery Agent

from saat.agents import DiscoveryAgent

agent = DiscoveryAgent()
discovery = await agent.analyze_repository(
    path="/path/to/repo",
    max_depth=3,
    auto_approve=False
)

Generator Agent

from saat.agents import GeneratorAgent

agent = GeneratorAgent()
model = await agent.generate_model(
    discovery=discovery,
    business_context=context,
    auto_approve=False
)

Requirements Agent

from saat.agents import RequirementsAgent

agent = RequirementsAgent()
result = await agent.discover_requirements(
    file_paths=["docs/PRD.md"],
    project_name="Payment Platform",
    auto_approve=False
)

Architecture Characteristics Agent

from saat.agents import ArchCharAgent

agent = ArchCharAgent()
result = await agent.analyze_architecture(
    model=c4_model,
    characteristics_input=archchar_input,
    auto_approve=False
)

Validation Agent

from saat.agents import ValidationAgent

agent = ValidationAgent()
result = await agent.validate_model(
    model=c4_model,
    framework="PCI-DSS",
    auto_approve=False
)

Security Agent

from saat.agents import SecurityAgent

agent = SecurityAgent()
result = await agent.analyze_security(
    model=c4_model,
    threat_model=True,
    auto_approve=False
)

Documentation Agent

from saat.agents import DocumentationAgent

agent = DocumentationAgent()
result = await agent.generate_documentation(
    model=c4_model,
    output_dir="docs/",
    formats=["markdown", "plantuml", "mermaid"],
    auto_approve=False
)

Terraform Agent

from saat.agents import TerraformAgent

agent = TerraformAgent()
result = await agent.generate_terraform(
    model=c4_model,
    provider="aws",
    region="us-east-1",
    output_dir="infrastructure/",
    auto_approve=False
)

Appendix D: Model Support

SAAT supports multiple AI models:

Anthropic Claude (Default)

export ANTHROPIC_API_KEY="your-key"
saat --model anthropic:claude-sonnet-4 analyze --path /repo

OpenAI

export OPENAI_API_KEY="your-key"
saat --model openai:gpt-4 analyze --path /repo

Google Gemini

export GEMINI_API_KEY="your-key"
saat --model gemini:gemini-1.5-pro analyze --path /repo

Local (Ollama)

ollama serve
ollama pull llama3.1
saat --model ollama:llama3.1 analyze --path /repo

Appendix E: Structurizr Integration

SAAT integrates with bac4-standalone for visual editing.

# Export to Structurizr format
saat export-structurizr -m architecture.json -o structurizr.json

# Import from Structurizr
saat import-structurizr -s structurizr.json -o architecture.json

See: BAC4_INTEGRATION.md for complete guide.

Appendix F: Future Roadmap - Agent Learning

🔮 Coming in v2.1+: Agents that learn and improve over time

Vision

Each SAAT agent will gain experience from every analysis, building organizational knowledge:

Pattern Recognition: Learn common patterns in your codebases
Recommendation Quality: Improve suggestions based on what worked
Domain Knowledge: Build expertise in your specific industry/domain
Team Preferences: Learn your team's architecture preferences
Historical Context: Remember past decisions and their outcomes

Research Areas

We're researching best practices in agent learning:

Memory Systems
- Vector databases for semantic memory (Pinecone, Weaviate)
- Graph databases for relationship memory (Neo4j)
- Time-series for evolution tracking
Feedback Loops
- User feedback on recommendations (👍/👎)
- Outcome tracking (did the fix work?)
- A/B testing different approaches
Knowledge Transfer
- Cross-project learning
- Industry best practices database
- Community-contributed patterns
Privacy-Preserving Learning
- Local-only learning mode
- Federated learning for team knowledge
- Opt-in cloud knowledge sharing

Implementation Plan

Phase 1: Memory Storage (v2.1)

Store analysis history in local SQLite
Track patterns and decisions
Basic recommendation improvement

Phase 2: Learning from Feedback (v2.2)

User feedback mechanism
Outcome tracking
Recommendation scoring

Phase 3: Advanced Learning (v2.3)

Vector embeddings for pattern matching
Cross-project knowledge transfer
Community knowledge base (opt-in)

Phase 4: Adaptive Agents (v3.0)

Agents that adapt to your organization
Personalized recommendations
Predictive architecture guidance

Relevant Research

MemGPT - Virtual context management for LLMs
Voyager - Lifelong learning agents
Reflexion - Self-reflection for agents
RAG Systems - Retrieval-augmented generation

See: docs/AGENT_LEARNING.md for detailed research and design (coming soon)

📞 Support

Issues: https://github.com/DavidROliverBA/SAAT/issues
Discussions: https://github.com/DavidROliverBA/SAAT/discussions

Name		Name	Last commit message	Last commit date
Latest commit History 54 Commits
.claude		.claude
docs		docs
examples		examples
saat		saat
.env.example		.env.example
.gitignore		.gitignore
ARCHITECTURE.md		ARCHITECTURE.md
BAC4_INTEGRATION.md		BAC4_INTEGRATION.md
CHANGELOG.md		CHANGELOG.md
LICENSE		LICENSE
README.md		README.md
RELEASE_NOTES.md		RELEASE_NOTES.md
SAAT-AGENTS-GUIDE.md		SAAT-AGENTS-GUIDE.md
V1.0.0_RELEASE_COMPLETE.md		V1.0.0_RELEASE_COMPLETE.md
create-pr.sh		create-pr.sh
create_pr.sh		create_pr.sh
install-claude-commands.sh		install-claude-commands.sh
pyproject.toml		pyproject.toml
requirements.txt		requirements.txt
saat_mcp_server.py		saat_mcp_server.py

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SAAT - Solution Architecture Agent Toolkit

📋 Table of Contents

🎯 What is SAAT?

Why SAAT?

Key Features

🚀 Quick Start

The Easy Way: Claude Code Slash Commands (Recommended)

Individual Commands

📦 Installation

Step 1: Install SAAT

Step 2: Set API Key

Step 3: Configure Claude Code (Recommended)

Step 4: Verify Installation

📖 Usage Guides

For New Users: Start Here!

Detailed Step-by-Step Guides

🧠 Core Concepts

The 9 Agents

The C4 Model

Architecture Characteristics

Criticality Levels

🤖 Available Agents

1. 🎯 Orchestrator Agent ⭐ NEW

2-9. Specialized Agents

📚 Documentation

Main Guides

Agent Documentation

Integration Guides

Technical Documentation

💡 Examples

Example 1: Quick Architecture Review

Example 2: Design Payment System

🤝 Contributing

Development Setup

📄 License

🙏 Acknowledgments

📎 Appendix

Appendix A: CLI Reference

Discovery & Generation

Requirements (Greenfield)

Quality Analysis

Validation

Security

Documentation

Infrastructure

Global Options

Appendix B: MCP Tools Reference

Appendix C: Agent API Reference

Orchestrator Agent

Discovery Agent

Generator Agent

Requirements Agent

Architecture Characteristics Agent

Validation Agent

Security Agent

Documentation Agent

Terraform Agent

Appendix D: Model Support

Appendix E: Structurizr Integration

Appendix F: Future Roadmap - Agent Learning

Vision

Research Areas

Implementation Plan

Relevant Research

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