Training course · AI Agent Development · React & Front End

AI Agent Development for React and Front-End Developers

Build AI agents professionally: architectures, tool use, agent SDKs, LangGraph, memory, evals, guardrails and deployment.

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

“AI Agent Development for React and Front-End Developers” is designed for roles such as React developers in product teams and agencies, Front-end engineers working with Next.js, Vite or similar frameworks and Maintainers of design systems and component libraries and shows step by step how to use AI Agent Development productively, safely and transparently in React & Front End – from the fundamentals to advanced workflows.

AI agents go beyond conventional chatbots: they break tasks down, call tools and APIs, evaluate the results and keep iterating until a goal is met. In this two-day course you learn to build such systems so that they work reliably, transparently and safely in day-to-day business. You compare architectural patterns ranging from simple workflows through the agent loop to multi-agent systems, and you implement them hands-on with the Claude Agent SDK, the OpenAI Agents SDK and LangGraph.

The focus is not on a quick demo but on the path to production. You design tool interfaces and context deliberately, connect tools via the Model Context Protocol, plan short-term and long-term memory and build evaluations that surface regressions early. On top of that come guardrails against prompt injection and harmful actions, human-in-the-loop approvals, tracing, cost control and deployment. By the end you can judge when an agent is worthwhile, when a deterministic workflow is enough and which framework fits your project.

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 choose the right pattern for a task, from a fixed workflow to an autonomous agent.
  • You design tools and context so that agents work reliably and cost-effectively.
  • You implement agents with the Claude Agent SDK, the OpenAI Agents SDK or LangGraph.
  • You set up evaluations and tracing to measure quality and detect regressions.
  • 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 Agent architectures and design patterns
  • Workflows versus agents: prompt chaining, routing, parallelisation, orchestrator-worker
  • The agent loop: plan, act, observe, reflect
  • Multi-agent systems, subagents and handoffs between agents
  • Criteria for choosing model, framework and level of autonomy
Module 2 Tool use and context engineering
  • Function calling and structured outputs with JSON Schema
  • Designing good tools: granularity, naming, error messages, idempotency
  • Connecting and reusing tools via MCP servers
  • Managing the context window: summarisation, compaction, retrieval and prompt caching
Module 3 Frameworks in practice
  • Claude Agent SDK: agent loop, tools, subagents, permissions and hooks
  • OpenAI Agents SDK: agents, handoffs, guardrails and tracing
  • LangGraph: stateful graphs, checkpointing and human-in-the-loop
  • Side-by-side comparison on the same task and criteria for choosing a framework
Module 4 Memory, evals and observability
  • Short-term and long-term memory, storage strategies and forgetting
  • Evaluation datasets, LLM-as-a-judge and task-based success metrics
  • Tracing tool calls, token usage and latency
  • Regression testing when prompts, models or tools change

Day 2

Module 5 Guardrails, security and deployment
  • Protecting against prompt injection, data exfiltration and over-privileged tools
  • Input and output checks, approval steps and sandboxing
  • Deploying as a service, asynchronous execution, retries and cost limits
  • Governance, audit logging and requirements under GDPR and the EU AI Act
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

Solid programming skills in Python or TypeScript, experience with REST APIs and some hands-on experience with LLM APIs or AI assistants. Hands-on experience with React and modern JavaScript or TypeScript, plus a working knowledge of CSS, npm and Git.

Technical requirements

A laptop with Python or Node.js, Git and a code editor, plus API access to at least one LLM provider such as Anthropic or OpenAI. Credentials for the exercises can be provided if needed.

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.

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Thu, 10 December 2026 – Fri, 11 December 2026 Bremen Request
  • Thu, 14 January 2027 – Fri, 15 January 2027 Online Request
  • Thu, 28 January 2027 – Fri, 29 January 2027 Salzburg Request
  • Mon, 8 February 2027 – Tue, 9 February 2027 Online Request
  • Mon, 22 February 2027 – Tue, 23 February 2027 Kaiserslautern Request

Frequently asked questions

Which agent framework should we use?

It depends on your model strategy, the level of control you need and your team’s skills. Vendor SDKs offer a lean start with tightly integrated features, while LangGraph gives you finer control over state and flow. In the course you solve the same task with several frameworks and derive clear selection criteria for your own project.

How do you stop an agent from taking wrong or dangerous actions?

With several layers of protection: narrowly scoped tools with minimal permissions, input and output validation, approval steps for critical actions, sandboxing and continuous evaluation. Prompt injection via external content is treated as a realistic threat. You practise these measures directly on your own agent.

Does every automation need an agent?

No. Many tasks are handled more reliably, cheaply and transparently by a fixed workflow of a few LLM calls. Agents pay off when the solution path cannot be planned in advance and the system must react flexibly to intermediate results. We train exactly this trade-off using practical examples.

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 “AI Agent Development 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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