🏆SFEIR is the Google Cloud EMEA Training Partner of the Year 2025🤝New partnership: Official GitLab Training🤖New training: AI-Augmented Developer
🏆SFEIR is the Google Cloud EMEA Training Partner of the Year 2025🤝New partnership: Official GitLab Training🤖New training: AI-Augmented Developer
GENAI200DEVAUGMENTE

AI-Augmented Developer Training - Master Agentic Coding

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.

Your teams will learn to orchestrate and collaborate with intelligent agents to generate reliable and maintainable code, automate testing and documentation, accelerate refactoring and bug resolution, and standardize AI-assisted development practices.

The training is adapted to the market-leading tools (Claude Code, Gemini CLI, GitHub Copilot, Cursor AI, etc.) and teaches participants to build their own custom business agents. Note: the choice of tools is updated according to the most relevant and mature solutions on the market.

By adopting this approach, your teams will move from "vibe coding" to a structured agentic methodology, ensuring code quality, security, and maintainability while drastically accelerating delivery.

At the end of this training, the final assessment in the form of a certification test allows participants to obtain the SFEIR Certified AI-Augmented Developer certification.

✓ Official training SFEIR InstituteLevel Intermediate⏱️ 2 days (14h)

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 (TypeScript, Java, Python, JavaScript, C#, 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.

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 the vibe coding tool

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
  • →The following points are covered and practiced according to the actual capabilities of the chosen agentic tool (Claude Code, GitHub Copilot, Gemini CLI, Codex CLI, etc.)
  • →Tool overview
  • →Key features, CLI commands, and shortcuts for each tool
  • →Custom agents: creating specialized sub-agents with isolated context for targeted workflows
  • →Custom commands / prompts: reusable prompt templates for recurring tasks
  • →Agent skills: composable knowledge loaded on demand
  • →Hooks: shell commands triggered on lifecycle events
  • →Plugins: distributable packages bundling agents, skills, hooks, and MCP servers
  • →Configuration files
  • →Context file
  • →Choosing the right type of feature for each scenario
Activities

Deep dive on the tool of your choice: create custom agents, commands, skills, hooks, plugins, etc.

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 testing with visibility of outputs in the browser
Activities

Installation of an MCP and complex interaction: screen capture + test generation

Topics covered
  • →Using agent skills in a team context: learn to leverage existing reusable skills to consistently apply architecture rules, coding standards, and testing practices across the entire team. (Claude/Copilot/Gemini)
  • →Leveraging custom agents for roles: interact with role-specific agents (e.g., Architecture Guardian, Test Strategist, API Contract Agent) to get contextual guidance suited to your tasks. (Claude/Copilot)
  • →Running custom commands: practice invoking custom slash commands (e.g., /code-reviewer, /prepare-pr, /trace-flow) to speed up PR review, diff analysis, and test planning, etc. (Claude/Copilot/Gemini)
  • →Using marketplace plugins: install and use Claude Code plugins from marketplaces to extend functionality (commands, agents, skills, hooks, and MCP servers). (Claude)
  • →Configuring custom instructions: define repository-wide and path-specific rules to guide the AI. (Copilot)
  • →Using hooks for automated guardrails: apply pre-commit and post-edit hooks for quality gating. (Claude)
  • →Using hooks for automation: use Gemini hooks (e.g., BeforeTool, AfterTool, BeforeAgent) for validation and automation. (Gemini)
  • →Accessing repository knowledge: explore architecture docs, standards, and the business glossary. (Claude/Copilot/Gemini)
  • →AI-assisted collaborative code review: produce consistent and actionable comments. (Claude/Copilot/Gemini)
  • →Diff analysis and risk management in practice: interpret recommendations on risky changes. (Claude/Copilot/Gemini)
Activities

Hands-on: run /code-reviewer on a PR diff and analyze the recommendations. (Claude/Copilot/Gemini)

Hands-on: interact with the Architecture Guardian agent to verify architectural compliance. (Claude/Copilot)

Hands-on: interact with the topology-awareness skill to assess changes. (Claude/Copilot/Gemini)

Hands-on: install a plugin from a marketplace and use its commands/agents/skills. (Claude)

Hands-on: use /prepare-pr to generate a PR description and a checklist. (Claude/Copilot/Gemini)

Hands-on: run /trace-flow on a backend endpoint and examine the generated explanation. (Claude/Copilot/Gemini)

Hands-on: observe pre-commit and post-edit hooks in action on sample changes. (Claude)

Hands-on: observe Gemini hooks (e.g., BeforeTool, AfterTool, BeforeAgent) in action. (Gemini)

Hands-on: configure shared instructions and observe the impact on recommendations. (Copilot)

Hands-on: consult shared references during a command/agent to improve context. (Claude/Copilot/Gemini)

Hands-on: analyze a risky diff and discuss the merge decision. (Claude/Copilot/Gemini)

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%.

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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

Teaching Methods Used
  • 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.
Evaluation and Monitoring System

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.

Frequently Asked Questions

This training is designed for backend/frontend developers, architects and Tech Leads who want to boost their productivity with AI while maintaining high code quality.
Vibe Coding means passively generating code without reviewing it. Agentic Coding is continuous collaboration with AI: you specify, plan, implement and validate each step.
The training is built around the market's leading tools (Claude Code, Gemini CLI, GitHub Copilot, Cursor AI, etc.) and the selection is kept up to date with the most mature solutions. The Deep Dive module is practiced according to the actual capabilities of the chosen agentic tool, and for private company sessions we adapt the tools to your stack.
MCP is a protocol for extending AI agent capabilities with external tools (Playwright, Context7, Atlassian, etc.). The training covers its concepts, architecture, and a concrete use case with MCP Playwright.
Yes, the training concludes with the SFEIR Certified AI-Augmented Developer certification exam. It is an online MCQ with 20 practical questions and a minimum passing score of 80%.
Our training organizations SFEIR SAS and SFEIR-Est are Qualiopi certified for training activities. Contact us for a quote.

1,580€ excl. VAT

per learner

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