Hugging Face's logo

Spaces:
MCP-1st-Birthday
/
sdlc-agent
Runtime error

App Files Community
3
sdlc-agent / docs /agentdesign.md
Veeru-c's picture
Veeru-c
initial commit
17c3b9d
preview code
|
raw
history blame contribute delete
12.2 kB

A newer version of the Gradio SDK is available: 6.1.0

Upgrade

AI Agent Design: Automated Software Development Workflow

Overview

This document outlines the design for an AI-powered agent that automates the complete software development lifecycle from requirement gathering to code deployment. The agent integrates with multiple systems (RAG, JIRA, Git, Testing) to provide an end-to-end automated development experience.

System Architecture 

graph TD
    A[User Input: 3-5 Sentence Requirement] --> B[Web Dashboard]
    B --> C[RAG & Fine-tuning via Gradio MCP]
    C --> D[Product Spec Generation]
    D --> E[Git Branch Creation]
    E --> F[Code Generation in src/agent/]
    F --> G[Automated Code Review]
    G --> H[Git Commit]
    H --> I[Unit Testing]
    I --> J{Manual Approval}
    J -->|Approved| K[Submit PR]
    J -->|Rejected| F
    K --> L[PR Merge Notification]
    L --> M[Summary Report with Git URL]

Workflow Steps

1. Requirement Input & Dashboard

Input: User provides 3-5 sentence requirement via web interface

Components:

  • Web-based dashboard displaying:
    • Current activity status
    • Progress bar for each step
    • Real-time logs
    • File modification tracker
  • Built with modern web framework (React/Next.js or Gradio)

Output: Structured requirement text

2. RAG & Fine-tuning System Integration via Gradio MCP

Process:

  1. Query RAG system with user requirement via Gradio MCP
  2. If needed, query fine-tuned model for domain-specific insights
  3. Generate detailed product specification including:
    • Feature description
    • Acceptance criteria
    • Technical requirements
    • Dependencies
    • Implementation approach
  4. Use Gradio MCP Server to access both:
    • RAG system (vector database + LLM)
    • Fine-tuned models (for specialized knowledge)

Technologies:

  • RAG: Vector database (ChromaDB/Pinecone) + LLM
  • Fine-tuned Models: Domain-specific models (e.g., insurance, finance)
  • Gradio MCP Server: Unified interface for RAG and fine-tuning systems
  • LangChain: For orchestrating RAG queries

Output:

  • Detailed product specification document
  • Technical implementation plan
  • Recommended architecture
  • Code generation prompts

3. Git Branch Creation & Code Generation

Process:

  1. Create feature branch: feature/JIRA-{epic-id}-{description}
  2. Create directory structure: src/agent/{feature-name}/
  3. Generate initial code files based on user stories:
    • Implementation files
    • Test files
    • Configuration files
    • Documentation

Technologies:

  • Git CLI / GitPython
  • Code generation: LLM-based (GPT-4/Claude)

Output:

  • New Git branch
  • Initial code structure in src/agent/

4. Automated Code Review

Process:

  1. Run static analysis tools:
    • Linters (ESLint, Pylint, etc.)
    • Type checkers (TypeScript, mypy)
    • Security scanners (Bandit, Snyk)
  2. LLM-based code review:
    • Code quality assessment
    • Best practices validation
    • Architecture review
    • Documentation completeness
  3. Generate review report with:
    • Issues found
    • Suggestions for improvement
    • Severity ratings

Technologies:

  • Static analysis: Language-specific tools
  • LLM: GPT-4/Claude for semantic review
  • Code review frameworks

Output:

  • Code review report
  • List of issues to fix
  • Auto-fix suggestions

5. Git Commit

Process:

  1. Stage all modified files
  2. Generate meaningful commit message:
    • Format: feat(JIRA-{id}): {description}
    • Include:
      • What changed
      • Why it changed
      • Related JIRA ticket
  3. Commit to feature branch
  4. Push to remote repository

Technologies:

  • Git CLI / GitPython
  • Conventional Commits standard

Output:

  • Commit SHA
  • List of files committed

6. Unit Testing & Summary

Process:

  1. Auto-generate unit tests if not present
  2. Run test suite:
    • Unit tests
    • Integration tests (if applicable)
    • Coverage analysis
  3. Generate test summary report:
    • Total tests run
    • Pass/Fail count
    • Code coverage percentage
    • Failed test details
    • Performance metrics

Technologies:

  • Testing frameworks: pytest, Jest, JUnit
  • Coverage tools: coverage.py, Istanbul
  • Test generation: LLM-based

Output:

  • Test summary report (JSON/HTML)
  • Coverage report
  • Pass/Fail status

7. Manual Approval & PR Submission

Process:

  1. Display test results to user via dashboard
  2. Wait for manual approval:
    • Approve: Proceed to PR
    • Reject: Return to code generation (Step 3)
  3. If approved:
    • Create Pull Request with:
      • Title: JIRA ticket reference
      • Description: Auto-generated from commits
      • Reviewers: Auto-assigned
      • Labels: Auto-tagged
    • Link PR to JIRA ticket

Technologies:

  • GitHub/GitLab/Bitbucket API
  • JIRA API (via Gradio MCP)
  • Web dashboard for approval UI

Output:

  • PR URL
  • PR number
  • Approval timestamp

8. PR Merge Notification & Summary

Process:

  1. Monitor PR status (webhook or polling)
  2. Once merged:
    • Send notification to user
    • Update JIRA ticket status
    • Generate final summary report:
      • Git repository URL
      • Branch name
      • Commit SHAs
      • List of all modified files
      • Test results
      • Code review summary
      • PR link
      • Merge timestamp

Technologies:

  • Git webhooks
  • Notification system (email, Slack, dashboard)
  • JIRA API

Output:

  • Completion notification
  • Git URL: https://github.com/{org}/{repo}/tree/{branch}
  • Modified files list with diff stats
  • Full workflow summary

Technology Stack

Core Components

Component Technology
Web Dashboard HTML/CSS/JS (implemented)
Backend API FastAPI (implemented)
RAG System LangChain + ChromaDB/Pinecone
Fine-tuned Models Domain-specific models
LLM GPT-4 / Claude 3.5
MCP Server Gradio MCP for RAG & Fine-tuning
Git Integration GitPython
Testing pytest / Jest
CI/CD GitHub Actions / GitLab CI

External Integrations

  • RAG System: Via Gradio MCP Server for knowledge retrieval
  • Fine-tuned Models: Via Gradio MCP Server for specialized insights
  • Git: GitHub/GitLab/Bitbucket API
  • Notification: Email/Slack/Discord

Data Flow 

User Input (Text)
    ↓
RAG Query (Embeddings) via Gradio MCP
    ↓
Fine-tuned Model Query (if needed)
    ↓
Product Spec (Structured JSON)
    ↓
Implementation Plan Generation
    ↓
Code Generation (LLM)
    ↓
Git Operations (Branch, Commit, Push)
    ↓
Code Review (Static + LLM)
    ↓
Testing (pytest/Jest)
    ↓
Manual Approval (Dashboard)
    ↓
PR Creation (Git API)
    ↓
Merge Notification (Webhook)
    ↓
Summary Report (Dashboard + Email)

Dashboard UI Components

Main Dashboard 

┌─────────────────────────────────────────────────────┐
│  AI Development Agent - Workflow Status             │
├─────────────────────────────────────────────────────┤
│                                                     │
│  [Input Requirement]                                │
│  ┌───────────────────────────────────────────────┐ │
│  │ Enter your requirement (3-5 sentences)...     │ │
│  └───────────────────────────────────────────────┘ │
│  [Submit]                                           │
│                                                     │
│  Workflow Progress:                                 │
│  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ │
│  1. ✅ Requirement Analysis          [Complete]    │
│  2. ✅ RAG & Fine-tuning Query       [Spec Ready]  │
│  3. ✅ Git Branch Created            [feature/...] │
│  4. 🔄 Code Generation               [In Progress] │
│  5. ⏳ Code Review                   [Pending]     │
│  6. ⏳ Git Commit                    [Pending]     │
│  7. ⏳ Unit Testing                  [Pending]     │
│  8. ⏳ Manual Approval               [Pending]     │
│  9. ⏳ PR Submission                 [Pending]     │
│  10.⏳ PR Merge & Notification       [Pending]     │
│                                                     │
│  Activity Log:                                      │
│  ┌───────────────────────────────────────────────┐ │
│  │ [12:34:56] Starting code generation...        │ │
│  │ [12:35:12] Created src/agent/feature/main.py │ │
│  │ [12:35:45] Generated 5 files                 │ │
│  └───────────────────────────────────────────────┘ │
│                                                     │
│  Modified Files: [View Details]                     │
│                                                     │
└─────────────────────────────────────────────────────┘

Configuration

Environment Variables 

# Gradio MCP Server (for RAG & Fine-tuning)
GRADIO_MCP_SERVER_URL=http://localhost:7860

# RAG System
VECTOR_DB_TYPE=chromadb  # or pinecone
VECTOR_DB_URL=http://localhost:8001  # for ChromaDB
VECTOR_DB_API_KEY=xxxxx  # for Pinecone
OPENAI_API_KEY=sk-xxxxx

# Fine-tuned Models
FINETUNED_MODEL_PATH=/path/to/finetuned/model
FINETUNED_MODEL_TYPE=insurance  # or finance, etc.

# Git
GIT_REPO_URL=https://github.com/org/repo
GIT_TOKEN=ghp_xxxxx
GIT_DEFAULT_BRANCH=main

# Testing
TEST_FRAMEWORK=pytest
COVERAGE_THRESHOLD=80

# Notifications
SLACK_WEBHOOK_URL=https://hooks.slack.com/xxxxx
EMAIL_SMTP_SERVER=smtp.gmail.com

Error Handling

Retry Logic

  • RAG Query Failure: Retry 3 times with exponential backoff
  • JIRA API Failure: Retry 3 times, fallback to manual creation
  • Git Operations: Retry 2 times, alert user on failure
  • Test Failures: Allow manual intervention or auto-fix

Rollback Strategy

  • Code Review Failure: Revert to previous commit
  • Test Failure: Option to fix or abandon
  • PR Rejection: Keep branch for manual fixes

Security Considerations

  1. API Keys: Store in secure vault (AWS Secrets Manager, HashiCorp Vault)
  2. Code Scanning: Run security scans before commit
  3. Access Control: RBAC for dashboard and Git operations
  4. Audit Logging: Log all actions with timestamps
  5. Code Isolation: Run code generation in sandboxed environment

Future Enhancements

  1. Multi-language Support: Extend beyond Python/JavaScript
  2. AI Code Reviewer: More sophisticated LLM-based reviews
  3. Auto-deployment: Deploy to staging after PR merge
  4. Performance Testing: Add load/performance tests
  5. Documentation Generation: Auto-generate API docs
  6. Slack/Teams Integration: Real-time notifications
  7. Analytics Dashboard: Track metrics (velocity, quality, etc.)

Success Metrics

  • Automation Rate: % of steps completed without human intervention
  • Time to PR: Average time from requirement to PR submission
  • Code Quality: Pass rate of code reviews
  • Test Coverage: Average coverage across projects
  • User Satisfaction: Feedback scores from developers

Conclusion

This AI agent design provides a comprehensive, automated software development workflow that reduces manual effort, improves code quality, and accelerates delivery. By integrating RAG, JIRA (via Gradio MCP), Git, and testing tools, the system creates a seamless experience from requirement to deployment.