Agent-assisted software testing – Managing software quality with AI

Safeguarding quality in a world of AI agents

AI is changing not only how software is built but also how software is tested. AI assistants and coding agents generate test cases, analyze existing tests, and help find defects. At the same time, this shift calls for a new perspective on quality assurance: how do you stay in control of quality when AI performs more and more of the work?

AI can speed up the testing process, although more generated tests don't automatically mean better software quality. The challenge is to make AI collaborate effectively with testers and developers, supported by clear frameworks, solid context, and reliable feedback loops.

In this training, you'll learn how to apply AI to test design, test automation, analysis, and quality assurance. You'll explore the possibilities of tooling such as GitHub Copilot, Codex, and Claude Code, and above all the setup around it. With harness engineering, context engineering, and an agent-ready test framework, you'll lay the foundation for scalable and manageable AI-assisted testing.

The training is developed and delivered by Betabit specialists who work on software quality and business-critical software every day. They use AI tooling to build test frameworks and test software in practice, and they guide test teams through the adoption of AI tooling.

What you’ll learn

During this training, you'll discover how AI contributes to your software quality and to better, more efficient testing:

• The fundamentals: LLMs and agents
  • How LLMs and agents work
  • What this means for software testing and QA
  • The possibilities and limitations of AI
• Prompts and prompt engineering
  • Communicating effectively with AI
  • Providing context and instructions
  • Assessing and improving results
• Quality assurance-use cases
  • Generating test cases and test data
  • Analyzing and improving existing tests
  • Applying AI to test analysis, reviews, and defect detection
  • Supporting exploratory and automated testing
  • Quality assurance during ideation and refinement sessions
• Tooling in practice
  • Tips and best practices for software agent tools
  • Tips and best practices for your tool (GitHub Copilot, Codex, Claude Code, or comparable)
  • Choosing the right tool for the right QA task
• Harness engineering
  • Guides, sensors, and tools
  • Providing agents with reliable feedback
  • Guardrails and quality controls for agent-assisted testing
• Risk and cost management
  • Recognizing and managing the risks of AI in the testing process
  • Costs and scalability
  • Context engineering: making sure agents have the right test and domain knowledge
• Architecture of an agent-ready test framework
  • Requirements for your test framework so it collaborates effectively with agents
  • Preparing test automation for a future in which agents play an ever-larger role

Software quality that grows with AI

After this training, you'll know how to apply AI as part of your QA strategy while staying in control of quality. You'll understand how agents, tooling, context, and feedback loops come together. And you'll know which choices are needed to prepare your testing process and test architecture for the next step in software quality.