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Reza Bozorgi edited this page Jun 9, 2025 · 1 revision

AutoSDLC System Overview

#AutoSDLC #Core #Architecture #Overview

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Executive Summary

AutoSDLC revolutionizes software development by creating an autonomous team of AI agents that collaborate to design, implement, test, and deploy software. Built on Claude Code's MCP (Model Context Protocol) capabilities and deep GitHub integration, it transforms how software is created.

Vision

"Transform software development from a manual, error-prone process to an intelligent, self-organizing system where AI agents collaborate seamlessly to deliver high-quality software."

Key Concepts

1. Agent-Based Architecture

  • Autonomous Agents: Each agent has a specific role and expertise
  • Collaborative Intelligence: Agents work together, not in isolation
  • Self-Organizing: Agents coordinate without human intervention

2. Continuous Development

  • Iterative Process: Small, frequent improvements
  • Automated Testing: Every change is validated
  • Continuous Deployment: Automated release pipeline

3. Human-in-the-Loop

  • Oversight: Humans maintain control over critical decisions
  • Customization: Agents can be configured and prompted
  • Monitoring: Real-time visibility into agent activities

System Components

Agent Team

  1. Customer Agent #Agent

    • Maintains product vision
    • Validates implementations
    • Provides requirements clarification

  2. Product Manager Agent #Agent

    • Translates requirements
    • Manages GitHub issues
    • Coordinates development

  3. Coder Agent #Agent

    • Implements features
    • Writes tests
    • Creates pull requests

  4. Code Reviewer Agent #Agent

    • Reviews code quality
    • Ensures standards
    • Suggests improvements

  5. Tester Agent #Agent

    • Monitors CI/CD
    • Analyzes test results
    • Reports issues

Infrastructure Components

Core Workflows

Feature Development Flow

graph LR
    A[Customer Request] --> B[PM Analysis]
    B --> C[Issue Creation]
    C --> D[Coder Implementation]
    D --> E[Code Review]
    E --> F[Automated Testing]
    F --> G[Deployment]
    G --> H[Customer Validation]
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Bug Fix Flow

graph LR
    A[Bug Report] --> B[PM Triage]
    B --> C[Issue Creation]
    C --> D[Coder Fix]
    D --> E[Review & Test]
    E --> F[Deploy Fix]
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Benefits

For Development Teams

  • ✅ Increased Velocity: 10x faster feature delivery
  • ✅ Improved Quality: Consistent code standards
  • ✅ Reduced Burnout: Automate repetitive tasks
  • ✅ 24/7 Development: Agents work continuously

For Organizations

  • ✅ Cost Reduction: Lower development costs
  • ✅ Faster Time-to-Market: Rapid iteration
  • ✅ Scalability: Handle multiple projects
  • ✅ Innovation: Focus on creative work

Implementation Philosophy

1. Gradual Adoption

Start with simple workflows and expand gradually. Begin with automated code reviews, then add implementation capabilities.

2. Human Oversight

Maintain checkpoints for critical decisions. Humans approve architectural changes and validate business logic.

3. Continuous Learning

Agents improve through feedback loops. Performance metrics drive optimization.

4. Transparency

All agent actions are logged and auditable. Decision rationale is documented.

Success Metrics

Technical Metrics

  • Response Time: < 5 seconds per agent action
  • Accuracy: > 95% successful implementations
  • Uptime: > 99.9% system availability

Business Metrics

  • Velocity Increase: 5-10x improvement
  • Defect Reduction: 70% fewer bugs
  • Cost Savings: 60% reduction in development costs

Getting Started

  1. Read the Getting Started Guide
  2. Understand the Architecture
  3. Set up MCP Integration
  4. Configure GitHub

Related Documents

Tags: #AutoSDLC #Overview #Core #Architecture Last Updated: 2025-06-09 Next: System Architecture →

AutoSDLC Documentation

Overview

Agent System

Technical Integration

API & Data

User Interface

Operations

Development

Resources

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