Training course · Agentic Coding · Test Automation & QA

Agentic Coding for Test Automation and QA

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 Test Automation & QA. 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.

Quality assurance is under pressure: release cycles are getting shorter, applications more complex, and AI-assisted coding produces more code in less time – all of which needs testing. Test automation engineers and QA professionals therefore benefit particularly from AI assistants and agents. These tools derive test cases from user stories and acceptance criteria, write Playwright or Cypress tests with robust selectors, generate API tests from OpenAPI specifications, analyse flaky tests and build test data generators. At the same time the role is shifting: QA becomes the function that systematically safeguards AI-generated code too. This course teaches you to use AI for test design, automation and failure analysis and to adapt your test strategy to the new pace of development.

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?

  • Test automation engineers and SDETs
  • QA engineers and test managers
  • Developers responsible for test suites
  • Business testers moving into automation

Typical challenges we address

  • Unstable end-to-end tests with timing issues and fragile selectors
  • High effort for test case creation, maintenance and test data
  • Gaps in coverage of APIs, edge cases and failure scenarios
  • Growing volumes of AI-generated code that need targeted safeguarding

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.
  • Derive test cases systematically from requirements with AI and automate them in Playwright, Cypress or API tests.
  • Analyse flaky tests and coverage gaps with AI support and fix them for good.
  • Adapt your test strategy to AI-assisted development and safeguard generated code in a targeted way.

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: Test Automation & QA
  • Deriving test cases from requirements
  • Writing Playwright and Cypress tests
  • Analysing flaky tests
  • API tests from specifications
  • Generating test data
  • Coverage and mutation testing
Module 7 Data protection, quality & transfer to daily work
  • Test data should be synthetic or effectively anonymised, as production data containing personal information must not be used unprotected in test environments or prompts (GDPR). Credentials for test systems belong in secret stores rather than test code, and in regulated sectors test results must be traceable and documented, for example as evidence under NIS2 or the Cyber Resilience Act.
  • 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

Deriving test cases from requirements

The assistant turns user stories and acceptance criteria into structured test cases with equivalence classes, boundary values and negative scenarios, optionally as Gherkin scenarios for behaviour-driven development.

Writing Playwright and Cypress tests

The AI writes end-to-end tests with role-based selectors, page objects or fixtures and proper waiting instead of fixed delays, and runs them iteratively until they are stable.

Analysing flaky tests

The agent examines test history, traces, screenshots and logs, identifies causes such as race conditions, test interdependencies or unstable test data, and proposes lasting fixes.

API tests from specifications

OpenAPI descriptions are turned into API tests for status codes, schemas, authentication and error cases, plus contract tests that surface interface changes between teams early.

Generating test data

The AI builds generators for realistic synthetic test data, factories and seed scripts that deliberately cover edge cases without relying on real personal data. Privacy issues in test environments are avoided.

Coverage and mutation testing

Coverage reports and mutation testing results are analysed, untested paths identified and missing tests added in a targeted way to increase the real value of the suite.

Possible hands-on projects

Flaky test report

A script that merges JUnit XML results from multiple CI runs, detects unstable tests and produces a prioritised list with failure patterns.

Test data generator

A CLI that creates realistic synthetic records, including edge cases, from a database schema or JSON schema. Data volume and distribution are fully configurable.

E2E suite for a demo app

A Playwright test suite with page objects, visual comparisons and CI integration, built by an AI agent for an existing web application.

Prerequisites

You have several years of development experience, are confident with Git, testing and CI, and ideally have already used AI coding tools. Experience in software testing or test automation and a working knowledge of at least one programming language such as JavaScript, TypeScript, Python or Java.

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

Test data should be synthetic or effectively anonymised, as production data containing personal information must not be used unprotected in test environments or prompts (GDPR). Credentials for test systems belong in secret stores rather than test code, and in regulated sectors test results must be traceable and documented, for example as evidence under NIS2 or the Cyber Resilience Act.

Suggested dates

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

  • Wed, 25 November 2026 – Thu, 26 November 2026 Online Request
  • Mon, 7 December 2026 – Tue, 8 December 2026 Salzburg Request
  • Mon, 14 December 2026 – Tue, 15 December 2026 Online Request
  • Thu, 14 January 2027 – Fri, 15 January 2027 Online Request
  • Thu, 21 January 2027 – Fri, 22 January 2027 Bremen Request
  • Wed, 27 January 2027 – Thu, 28 January 2027 Online 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.

Do I need programming skills for this course?

A working knowledge of a programming language helps, since you should be able to read, understand and adapt generated tests. Business testers with little coding experience still benefit greatly, because the AI explains code and supports the move into automation. The exercises are designed to accommodate different levels of prior knowledge.

Will AI replace manual and exploratory QA?

No. AI automates recurring tasks such as test case creation, scripting and failure analysis, freeing up time for exploratory testing, risk assessment and acceptance. With AI-generated code in particular, the critical judgement of experienced QA professionals becomes more important, not less.

How much does “Agentic Coding for Test Automation and QA” 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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