AI-Augmented Developer
Accelerate the productivity of your development teams with Agentic Coding. In a market where development speed and code quality make the difference, this training transforms your developers into “augmented developers,” capable of leveraging the most advanced AI agents. Focused on real-world use cases, the training emphasizes immediate value creation and concrete improvement in developer productivity. Participants leave with methods, workflows, and assets directly applicable to their corporate projects.
What you will learn
- Accelerate all phases of the development cycle, from architectural design to resolving complex bugs, increasing their velocity while ensuring code quality, security, testing, documentation, and maintainability.
- Collaborate continuously with AI by adopting AI-augmented work methods that enhance productivity and efficiency.
- Master the ecosystem of leading tools for coding with AI and AI frameworks (like Claude Code, Gemini CLI, GitHub Copilot, Cursor AI), evaluate and adopt new emerging tools, understanding their respective strengths and limitations.
- Transform a classic team into an augmented team by orchestrating collaborative AI agents, establishing team standards (shared contexts, reusable prompts), and disseminating best practices for AI-assisted development that accelerate onboarding and standardize workflows.
Prerequisites
- Practical mastery of at least one programming language (Python, JavaScript, Java, C#, TypeScript, Go...).
- Daily experience with Git and the use of a modern IDE (VS Code, IntelliJ, WebStorm, etc.).
- Possess basic command-line and file editing skills.
- Teamwork experience: code review, collaborative workflows.
- A sensitivity to generative AI and prompt engineering is an asset to maximize the benefits of the training.
- A standard laptop (16 GB recommended) with rights to install software.
- Recent operating system (Windows 10+, macOS 10.15+, Linux).
- Git installed and configured with access to GitHub/GitLab.
- Recent version of NodeJS and npm installed and configured.
- An IDE of choice (VS Code, IntelliJ, WebStorm...).
- A stable internet connection.
- Docker (optional but highly recommended to benefit from an automated setup via our devcontainer).
Target audience
- Software Engineer (operational backend/frontend developers), Software Architect, Tech Leader in companies, IT services companies, startups, and scale-ups who want to boost their efficiency with AI while maintaining a high level of code quality., Teams concerned with maintainability, robustness, and best practices, looking to use AI to modernize their methods while improving the quality of their deliverables.
Training Program
8 modules to master the fundamentals
Topics covered
- →AI Refresher: Quick review, history of AI / ML / NLP / Generative AI, arrival of ChatGPT/LLM
- →Market Models: GPT-5, Claude, Gemini - key differences
- →The Art of Prompting: how to communicate effectively with AI to get quality code
- →Evolution of tools: from ad-hoc assistance to collaborative agents
- →Context and tokens: understanding the constraints
- →Overview of tools: ChatGPT, Claude, etc.
Activities
Prompt engineering: comparison of 3 approaches on a concrete case
Topics covered
- →Vibe Coding: definition, limits, and dangers
- →Agentic Coding: vision of the augmented developer
- →Continuous collaboration vs passive generation
- →Impact on quality, maintainability, and documentation
Activities
Setting up vibe coding tools
Guided experiment: vibe code an application without looking at the generated code and explain the results, constraints, and advantages
Topics covered
- →Practical introduction: what is Agentic Coding for a developer?
- →Live demonstration: traditional development vs augmented development
- →Quality focus: how AI can improve code quality (conventions, patterns, best practices)
- →Agentic workflow: Specify → Plan → Tasks → Implement → Validation
- →Context management: the effective .md context file (locally for now)
- →Structuring requests and iterations
- →Best practices: documentation, testing, quality
Activities
Scenario 1: legacy - Refactoring legacy: getting started with the project to make it Agentic Coding compliant. Going as far as the migration plan
Scenario 2: from scratch and evolutions - Project from scratch: architecture, technical choices, structuring; Development of a complete feature with the agentic workflow
Topics covered
- →Token management
- →New generation IDEs (e.g., Cursor AI, Kiro, Windsurf, Zed)
- →Practical comparison: which tool for which need?
- →Hybrid workflow: combining multiple AI assistants
- →Tool usage possibility matrix: AI coding agent tool (Github Copilot / Claude Code / Open Code / Gemini CLI) versus models (Sonnet, Gemini, ...)
- →License/key management
- →Demonstrations
Topics covered
- →Model Context Protocol (MCP): Concepts and architecture; installation and use of MCP clients (examples: playwright, context7, Atlassian ...); Concrete use case: MCP Playwright to facilitate tests with visibility of outputs in the browser
- →Claude Code Specifics: sub-agents, Hooks, Skills...
Activities
Installation of an MCP and complex interaction: screen capture + test generation
Topics covered
- →Team standards: shared context and instructions (e.g., AGENTS.md)
- →Shared standards: defining team conventions - plugins, Claude Code marketplace, sharing hooks and configuration
- →Agent security: configuring their settings (.env not shared) and their agents according to their roles
- →Sharing reusable prompts and patterns
- →Accelerated onboarding of new developers
- →Augmented team code review
- →CI/CD integration and automation
- →Human-agent-team collaboration: assisted review, augmented pair programming
Activities
Creation of several AGENTS.md and definition of the team context
Collaborative workflow simulation: feature development → code review → merge
Implementation of a Git workflow with agents (pre-commit hooks, automated reviews)
Collaborative development: several devs + agents on the same project (with conflicts, resolution)
Team simulation: onboarding a new developer with AI assistance
Topics covered
- →The importance of the human-in-the-loop
- →Maintaining classic development skills and loss of technical knowledge: myth or reality?
- →Dependency on AI tools: risks and mitigation
- →Security: code review, vulnerabilities, sensitive data
- →Intellectual property and compliance
- →Ethics and responsibility of the augmented developer
- →Future perspectives
Topics covered
- →Online exam (MCQ type) with 20 practical questions covering the topics addressed during the training. Minimum passing score: 80%.
Quality Process
SFEIR Institute's commitment: an excellence approach to ensure the quality and success of all our training programs. Learn more about our quality approach
- Lectures / Theoretical Slides — Presentation of concepts using visual aids (PowerPoint, PDF).
- Technical Demonstration (Demos) — The instructor performs a task or procedure while students observe.
- Guided Labs — Guided practical exercises on software, hardware, or technical environments.
The achievement of training objectives is evaluated at multiple levels to ensure quality:
- Continuous Knowledge Assessment : Verification of knowledge throughout the training via participatory methods (quizzes, practical exercises, case studies) under instructor supervision.
- Progress Measurement : Comparative self-assessment system including an initial diagnostic to determine the starting level, followed by a final evaluation to validate skills development.
- Quality Evaluation : End-of-session satisfaction questionnaire to measure the relevance and effectiveness of the training as perceived by participants.
Upcoming sessions
Train multiple employees
- Volume discounts (multiple seats)
- Private or custom session
- On-site or remote