Build AI Agents: Claude Code, Codex, and Next-Gen Dev Tools Training

This course is designed to help developers, software engineers, and AI enthusiasts master the latest generation of AI-powered coding assistants, including Claude Code, Codex, and other cutting-edge developer tools. Through practical projects and real-world workflows, you’ll learn how to integrate AI into your daily development process to improve productivity, code quality, and problem-solving skills.

Whether you’re building web applications, automating development tasks, or exploring AI-assisted programming, this course provides the practical experience needed to become an AI-augmented developer.

Key Learnings

• Understand the evolution of AI coding assistants and autonomous coding agents.
• Set up and configure Claude Code and other AI development environments.
• Write effective prompts for code generation and debugging.
• Generate clean, maintainable, and production-ready code using AI Use AI for code reviews, refactoring, optimization, and documentation.
• Automate repetitive programming tasks with AI agents.
• Build complete applications with AI-assisted development workflows.
• Integrate AI into Git, GitHub, CI/CD, and modern development pipelines
• Compare Claude Code, Codex, Gemini, Cursor, Windsurf, GitHub Copilot, and other AI coding platforms.
• Apply AI responsibly while maintaining code quality, security, and best practices
• Develop real-world projects using AI-first software engineering workflows

Course Content

Module 1: Foundations of AI-Assisted Development

Introduction to AI Coding

• Evolution of AI in Software Development
• Understanding AI Coding Agents
• AI-Assisted vs Traditional Programming
• How LLMs Generate Code
• Choosing the Right AI Coding Tool

Development Environment Setup

• Installing Visual Studio Code
• Configuring Git & GitHub
• Setting Up Claude Code
• Setting Up OpenAI Codex
• Introduction to Cursor & Windsurf
• API Keys and Secure Configuration

Programming Workflow Essentials

• Project Structure
• Version Control Basics
• AI-Friendly Development Practices
• Using Terminal Effectively

Module 2: Prompt Engineering for Developers

Prompt Design Fundamentals

• Anatomy of Great Coding Prompts
• Zero-shot vs Few-shot Prompting
• Chain-of-Thought Techniques
• Context Management
• Prompt Templates

AI for Everyday Coding

• Generate Functions
• Create Classes
• Build APIs
• SQL Query Generation
• HTML & CSS Generation
• JavaScript Automation
• Python Scripting

Practical Lab

• Build a Utility Application Using AI

Module 3: AI-Powered Software Development

Building Applications Faster

• Rapid Prototyping
• Frontend Development with AI
• Backend Development
• REST API Creation
• Database Integration
• Authentication Systems
• CRUD Applications

Best Practices

• Writing Clean Code
• Code Organization
• Modular Architecture
• Error Handling

Mini Project

• AI-Assisted Task Management Application

Module 4: Debugging, Testing & Refactoring

AI Debugging Techniques

• Finding Bugs Faster
• Stack Trace Analysis
• Performance Optimization
• Memory Issues
• Security Vulnerabilities

Testing

• Unit Test Generation
• Integration Testing
• API Testing
• Test Automation

Code Quality

• Refactoring Legacy Code
• Documentation Generation
• Code Reviews Using AI

Mini Project

• Refactor an Existing Open-Source Project

Module 5: AI Agents & Workflow Automation

Understanding AI Agents

• Agentic Development Concepts
• Multi-Step Task Execution
• AI Planning Strategies
• Autonomous Coding Workflows

Developer Automation

• Git Automation
• Pull Request Assistance
• Documentation Automation
• Issue Tracking
• Release Notes Generation

MCP & Tool Integration

• Introduction to Model Context Protocol (MCP)
• Connecting External Tools
• AI Tool Orchestration

Mini Project

• AI Development Workflow Assistant

Module 6: Advanced AI Developer Tools

Modern AI IDEs

• Claude Code Workflows
• OpenAI Codex Features
• Cursor AI
• GitHub Copilot
• Windsurf
• Gemini Code Assist

Comparing AI Platforms

• Accuracy
• Context Window
• Performance
• Cost
• Enterprise Features

Team Collaboration

• AI Pair Programming
• Code Reviews
• Team Productivity
• Best Practices

Mini Project

• Multi-Agent Coding Environment

Module 7: Production-Ready AI Engineering

Building Professional Applications

• Software Architecture
• Microservices Overview
• CI/CD Pipelines
• Docker Basics
• Deployment Strategies

Responsible AI Development

• AI Limitations
• Security Considerations
• Privacy & Compliance
• Human Oversight
• Ethical AI Practices

Portfolio Preparation

• GitHub Optimization
• Project Documentation
• Technical Presentations

Module 8: Capstone Project

Final Project: AI Software Engineering Assistant

Design and build an end-to-end AI-powered developer assistant that demonstrates the skills learned throughout the course.

Project Requirements

The application should include:

• User Authentication
• AI Chat Interface for Developers
• Code Generation Module
• Code Explanation Feature
• Bug Detection & Debugging Assistant
• Code Refactoring Suggestions
• Unit Test Generator
• API Documentation Generator
• GitHub Repository Integration
• Project File Analyzer
• Markdown Documentation Creator
• Conversation History
• Export Code & Reports
• Responsive User Interface

This course is ideal for

• AI & Machine Learning Enthusiasts
• Beginner to Intermediate Programmers
• Computer Science & Software Engineering Students
• Frontend Developers
• Backend Developers
• Full Stack Developers
• Python, JavaScript, Java, C#, or Go, Developers
• Software Engineers

Career Opportunities

After completing this course, learners can pursue roles such as:

• AI Software Developer
• AI-Augmented Full Stack Developer
• AI Application Developer
• Prompt Engineer (Development)
• Software Engineer
• Backend Developer
• Frontend Developer
• Full Stack Engineer
• DevOps Engineer

Flexible Class Options

• Weekend Classes For Professionals  SAT | SUN
• Corporate Group Trainings Available
• Online Classes – Live Virtual Class (L.V.C), Online Training