Training course · Agentic Coding · React & Front End

Agentic Coding for React and Front-End Developers

Use autonomous coding agents effectively: specs instead of prompts, task decomposition, test-driven loops, multi-agent setups and governance.

Duration
2 days (14 hrs)
Format
Online or on-site
Language
German or English
Participants
open dates or exclusive for your team
Certificate
Certificate of attendance

Overview

This course connects Agentic Coding with the everyday processes of React & Front End. Participants work on realistic scenarios and leave with prompts, workflows and checklists they can use straight away.

Agentic coding means working with AI agents that carry out development tasks largely on their own: they read code, plan changes, run commands and tests, and correct themselves based on the results. Tools such as Claude Code, OpenAI Codex, the GitHub Copilot coding agent and Cursor’s agent mode have made this way of working practical for everyday use. This vendor-neutral course teaches the underlying methodology so that you achieve reliable results whatever the tool, and redefine the developer’s role within your engineering team.

The core shift is from ad hoc prompts to robust specifications. You will learn to decompose and describe tasks so that agents solve them reliably, to set up test-driven loops in which tests act as objective acceptance criteria, and to run several agents in parallel or with a division of labour. We also cover review strategies for agent-generated code and team governance: permissions, security boundaries, accountability, cost control and the gradual introduction of agentic workflows into your existing development processes.

Front-end development today means component architectures, state management, server components, design systems, accessibility and performance budgets – often all at once. AI coding assistants and agents speed up exactly the work that eats time: deriving components from designs, converting class components to hooks, writing Storybook stories and Testing Library tests, fixing ARIA issues or tracking down unnecessary re-renders. Because front-end code thrives on conventions, context is everything: design tokens, folder structure, styling approach and preferred libraries must be known to the assistant. This course shows you how to set those guardrails and how to verify results reliably, both visually and technically.

The course is delivered live – either online in a virtual classroom or on-site in our training rooms in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart and Kaiserslautern. We can also run it exclusively for your team.

Who is this course for?

  • React developers in product teams and agencies
  • Front-end engineers working with Next.js, Vite or similar frameworks
  • Maintainers of design systems and component libraries
  • Full-stack developers with a front-end focus
  • UI engineers responsible for accessibility and performance

Typical challenges we address

  • Inconsistent components and styles despite an existing design system
  • Hard-to-trace re-renders, bloated bundles and weak Core Web Vitals
  • Accessibility gaps and growing pressure from legal requirements
  • Legacy code with class components, outdated patterns and missing tests

What you will be able to do

  • You understand how coding agents work and choose the right tool and level of autonomy.
  • You write specifications and project context that agents can work with reliably.
  • You break initiatives into verifiable sub-tasks and steer agents through test-driven loops.
  • You use multi-agent setups and parallel work in a controlled way.
  • Have AI agents generate components, stories and tests that match your design system.
  • Use AI deliberately to improve accessibility, performance and legacy code in a systematic way.
  • Check generated front-end code visually, functionally and for maintainability before it ships.

Course outline

Day 1

Module 1 Foundations of agentic development
  • How coding agents work: tool calls, context windows and feedback loops
  • Comparing tools: terminal agents, IDE agents and cloud agents
  • Recognising suitable tasks and choosing levels of autonomy deliberately
  • The new role in the team: from implementation to steering and acceptance
Module 2 Specifications instead of prompts
  • Describing tasks with goal, context, constraints and acceptance criteria
  • Keeping project context in files such as CLAUDE.md, AGENTS.md or rules
  • Plan-first approach: let the agent draft a solution and review it before implementation
  • Versioning specifications and maintaining them as part of the repository
Module 3 Task decomposition and test-driven agent loops
  • Breaking large initiatives into independent, verifiable sub-tasks
  • Having tests written first and using them as an objective stopping criterion
  • Using linters, type checks and builds as automatic feedback
  • Recognising stuck loops and restarting with a new strategy
Module 4 Multi-agent setups and parallel work
  • Defining specialised subagents for research, implementation, testing and review
  • Organising parallel work with branches, worktrees and isolated environments
  • Orchestration: merging results and resolving conflicts
  • Sharing tools and data sources through MCP

Day 2

Module 5 Review, security and team governance
  • Review strategies for large, agent-generated changes
  • Defining permissions, sandboxing and protection against prompt injection
  • Embedding agents in CI pipelines and pull request processes
  • Planning team guidelines, accountability, costs and a phased rollout
Module 6 Practice lab: React & Front End
  • Building components from designs
  • Testing Library tests
  • Auditing and improving accessibility
  • Modernising legacy code
  • Optimising performance
  • Forms and data fetching
Module 7 Data protection, quality & transfer to daily work
  • Under the European Accessibility Act and its national implementations, many digital services must be accessible, so generated UI code needs proper checks. Also watch the licences of reused components and icons, keep secrets out of the client bundle and make sure tracking and consent logic complies with GDPR and ePrivacy rules.
  • Ground rules for safe AI use in your team
  • Checking results: facts, sources, tests and the four-eyes principle
  • Personal action plan for the next 30 days
  • Open questions, outlook and resources for further learning

Practical scenarios from your field

Building components from designs

From screenshots, design specs or Figma exports, the assistant produces typed React components that use your existing design tokens and libraries, and adds Storybook stories covering every state.

Testing Library tests

The AI writes user-centred component tests with React Testing Library and Vitest or Jest, simulates interactions, mocks API calls and covers loading, error and empty states.

Auditing and improving accessibility

The agent reviews components for semantic HTML, ARIA attributes, focus management and contrast, interprets axe results and fixes findings transparently against WCAG success criteria. Keyboard and screen reader behaviour is covered too.

Modernising legacy code

Class components are converted to function components with hooks, prop drilling is replaced by context or a proper state management approach and PropTypes give way to TypeScript types – with tests as a safety net.

Optimising performance

The AI pinpoints unnecessary re-renders, proposes memoisation, code splitting and lazy loading, analyses bundle reports and explains how each measure affects metrics such as LCP and INP.

Forms and data fetching

Complex forms with validation schemas, error messages and optimistic updates are generated alongside data-fetching logic with caching, retries and clean handling of loading and error states.

Possible hands-on projects

Interactive KPI dashboard

A React dashboard with charts, filters and a responsive layout that loads data from a REST API, built entirely by an AI agent from a short specification.

Component library with Storybook

A small, accessible component library with buttons, dialogs and form fields, including design tokens, Storybook documentation and automated tests. It forms the basis for consistent interfaces across several projects.

Accessibility checker for pull requests

A script that runs axe against Storybook stories automatically and turns the findings into a clear report attached to pull requests.

Prerequisites

You have several years of development experience, are confident with Git, testing and CI, and ideally have already used AI coding tools. Hands-on experience with React and modern JavaScript or TypeScript, plus a working knowledge of CSS, npm and Git.

Technical requirements

Your own laptop with Git, a terminal and your development environment, plus access to at least one coding agent such as Claude Code, Codex, Copilot or Cursor.

Data protection & compliance

Under the European Accessibility Act and its national implementations, many digital services must be accessible, so generated UI code needs proper checks. Also watch the licences of reused components and icons, keep secrets out of the client bundle and make sure tracking and consent logic complies with GDPR and ePrivacy rules.

Suggested dates

All dates are suggestions – your preferred date is always possible.

  • Thu, 26 November 2026 – Fri, 27 November 2026 Online Request
  • Thu, 10 December 2026 – Fri, 11 December 2026 Stuttgart Request
  • Mon, 11 January 2027 – Tue, 12 January 2027 Online Request
  • Mon, 25 January 2027 – Tue, 26 January 2027 Berlin Request
  • Mon, 1 February 2027 – Tue, 2 February 2027 Online Request
  • Tue, 9 February 2027 – Wed, 10 February 2027 Munich Request

Frequently asked questions

How does agentic coding differ from vibe coding?

Vibe coding relies on fast, intuitive iteration in natural language and is particularly suited to prototypes. Agentic coding is an engineering discipline for professional codebases: precise specifications, tests as acceptance criteria, clear permissions and structured reviews ensure that agents deliver reliable, maintainable results even in complex, long-lived projects.

Is the course tied to a specific tool?

No. The course teaches methods that work with Claude Code, OpenAI Codex, the GitHub Copilot coding agent, Cursor and comparable tools. The exercises highlight differences and common ground, for example in context files, permissions and subagents. You can work with whichever tool your organisation already uses or plans to adopt.

How do we stay in control of quality and security with autonomous agents?

Control comes from several layers: tightly scoped permissions and sandboxing, tests and static analysis as automatic checks, mandatory human review before merging, and clear team rules on accountability. During the course you develop a governance concept that fits your processes, security requirements and regulatory obligations.

Can AI implement designs pixel-perfectly?

AI assistants now translate layouts from screenshots or specs remarkably well, especially when they know your design tokens and components. Pixel-perfect results still come from iteration, though. On the course you learn to feed back visual deviations precisely and to use Storybook and visual tests as your control mechanism.

Does the framework I use matter?

The course centres on React, but the working methods transfer to Next.js, Remix or Vite-based setups. We show how to capture framework-specific rules such as server and client components or routing conventions in instruction files, so the assistant produces code that fits your stack.

How much does “Agentic Coding for React and Front-End Developers” cost?

The course costs a flat €949 plus VAT per participant – whether online or on-site. This includes 2 training day(s) with a live trainer, course materials, a lab environment and a certificate of attendance.

Can I suggest my own date?

Yes. Our suggested dates are just a guide. In the request form you can always describe your preferred date or period in free text – we check availability and confirm within one working day.

Is the course online or on-site?

Both are possible: live online in a virtual classroom or on-site in Munich, Salzburg, Berlin, Augsburg, Hanover, Bremen, Stuttgart or Kaiserslautern. We can also run the course exclusively for your team.

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