Gen AI and Agentic AI for QA and Test Engineers

AI test automation assists: it suggests scripts, completes code, or runs predefined tests. Agentic QA acts: it reads requirements, generates tests, executes them, reports results, and updates them when the application changes — without a human initiating each step. Most teams in 2026 are somewhere in the middle. This course shows you how to move toward the agentic end, where the human bottleneck in test creation is removed.

Basic familiarity with test automation concepts is expected — you should know what Selenium, Playwright, or similar frameworks are, even if you are not an expert. No AI background is required. The AI Foundations course is the only prerequisite. The course focuses on applying AI within QA workflows, not on building AI systems from scratch.

Self-healing tests use AI to detect when UI locators have changed — a button renamed, an element moved — and automatically update the test script to match the new state. They are in production today on platforms like Katalon, Quash, and others. For teams with frequent UI releases, they eliminate what is otherwise a continuous stream of manual locator fixes after every sprint. The course covers both the concept and the practical implementation patterns.

The course uses ChatGPT and Claude for test case generation and documentation, and covers AI-native testing concepts applicable to Playwright, Selenium, and Cypress frameworks. It is tool-agnostic by design — the prompting skills and agent patterns taught transfer to whatever automation stack your team runs on.

AI often catches edge cases humans miss because it can systematically enumerate input combinations, boundary values, and negative scenarios at scale. The course teaches you how to structure prompts so AI generates test cases from user stories with consistent coverage — and how to review and augment the output with your domain knowledge. The result is faster and more thorough coverage than manual authoring alone, not less.

Highly relevant for both. Manual testers benefit immediately from AI-assisted test case design, exploratory testing support, bug report generation, and test documentation — none of which requires automation experience. Automation engineers get additional value from the self-healing test, regression agent, and CI/CD quality gate modules. The course is designed to meet both profiles where they are.

With structured prompting, AI-generated bug reports are specific and actionable — reproduction steps, observed vs. expected behaviour, severity assessment, and related component flags. The course teaches you exactly how to give AI the right context to produce reports that developers can act on without back-and-forth, which is one of the most immediate time savers for QA teams.

Copilot autocompletes code. This course teaches a complete AI-augmented QA methodology — from test strategy and planning through automation, regression, bug triage, and quality reporting — covering both Gen AI for individual productivity and Agentic AI for autonomous quality workflows. Using Copilot for script completion is one narrow application. This course covers the full scope of what AI changes in the QA role.