Agentic Development Workflow

Modern, AI-powered development workflow optimized for Claude Code CLI and future-proof practices

Overview

This repository implements an agentic principled development workflow designed for November 2025 and beyond. It integrates cutting-edge AI tooling, automated observability, and self-organizing documentation practices.

Core Principles

1. Agent-First Development

• All development tasks are designed to be executable by AI agents
• Human intervention focuses on strategic decisions, not implementation details
• Continuous learning and adaptation through feedback loops

2. Observable by Default

• Every action generates telemetry
• Errors are automatically captured and contextualized
• Performance metrics are collected without manual instrumentation

3. Self-Documenting Code

• Documentation is generated from code, not maintained separately
• Changes automatically update relevant documentation
• Single source of truth for all technical specifications

Quick Start

# Clone the repository
git clone git@github.com:verlyn13/agentic-workflow.git
cd agentic-workflow

# Run the setup script
./scripts/setup.sh

# Initialize development environment
make dev-init

# Start the development workflow
make dev

Project Structure

.
├── .claude/              # Claude Code CLI configuration
├── .github/              # GitHub Actions and repository configuration
├── docs/                 # Auto-generated documentation
├── src/                  # Source code
├── scripts/              # Automation scripts
├── observability/        # Monitoring and logging configuration
├── tests/                # Test suites
└── workflows/            # Agentic workflow definitions

Key Features

AI-Powered Development

• Integrated Claude Code CLI workflows
• Automated code review and optimization
• Self-healing CI/CD pipelines

Observability Stack

• OpenTelemetry integration
• Distributed tracing
• Automatic error correlation
• Performance profiling

Documentation System

• Markdown-based documentation
• Automatic API documentation generation
• Interactive examples
• Version-controlled documentation

Development Workflow

1. Task Definition

Tasks are defined in .claude/tasks/ using YAML:

task: feature-development
triggers:
  - issue-created
  - pr-opened
steps:
  - analyze-requirements
  - generate-implementation
  - create-tests
  - update-documentation

2. Automated Execution

Agents automatically execute tasks based on triggers:

# Manual trigger
claude-code execute task:feature-development

# Watch mode (automatic execution)
claude-code watch

3. Continuous Monitoring

All executions are monitored and logged:

# View recent executions
make logs

# Check system health
make health

# View metrics dashboard
make dashboard

Configuration

Environment Variables

Copy .env.example to .env and configure:

# AI Configuration
CLAUDE_API_KEY=your-api-key
CLAUDE_MODEL=claude-3-opus-20240229

# Observability
OTEL_EXPORTER_OTLP_ENDPOINT=http://localhost:4317
LOG_LEVEL=info

# Development
DEV_MODE=true
AUTO_FIX=true

Claude Code CLI Settings

Configuration in .claude/config.yaml:

version: 1.0
model: claude-3-opus-20240229
context:
  max_tokens: 100000
  include_patterns:
    - "**/*.{js,ts,py,go}"
  exclude_patterns:
    - "**/node_modules/**"
    - "**/.git/**"

Commands

Development

make dev          # Start development environment
make test         # Run all tests
make lint         # Run linters
make format       # Format code

Documentation

make docs         # Generate documentation
make docs-serve   # Serve documentation locally
make docs-check   # Check documentation health

Deployment

make build        # Build for production
make deploy       # Deploy to production
make rollback     # Rollback last deployment

Observability

Logging

Structured logging with automatic context propagation:

logger.info('Processing request', {
  requestId: ctx.requestId,
  userId: ctx.userId,
  action: 'user.login'
});

Metrics

Automatic metric collection:

// Automatically tracked
@Instrument()
async processPayment(amount: number) {
  // Method execution time, success/failure rate tracked
}

Tracing

Distributed tracing across all services:

// Automatic span creation
@Trace()
async handleRequest(req: Request) {
  // Spans automatically created for all async operations
}

Contributing

Development Process

1. Issues are automatically triaged by AI
2. Implementation suggestions are generated
3. Code is reviewed by both AI and humans
4. Documentation is automatically updated

Code Style

• Enforced by pre-commit hooks
• Automatic formatting on save
• AI-powered style suggestions

Security

• Automated dependency scanning
• Secret detection in commits
• Security-first CI/CD pipeline
• Regular penetration testing

License

MIT License - See LICENSE file for details

Support

• Documentation: docs.agentic-workflow.dev
• Issues: GitHub Issues
• Discussions: GitHub Discussions

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