AI BootCamps (6 Courses)
Career Oriented
Course Category Career Oriented
Course Category 
Google Rating
Professionals Upskilled
Live Cohorts Delivered
Enterprise Teams Trained
GenAI is now the top-ranked skill for quality engineers cited by 63% of professionals in The World Quality Report 2025-26, and the shift from AI-assisted testing to Agentic QA is happening faster than any other area of software delivery. Teams that spent years maintaining Selenium and Playwright suites are already adopting agents that generate, execute, maintain, and self-heal tests autonomously. This 16-hour live Gen AI and Agentic AI for QA and Test Engineers course applies Gen AI and Agentic AI across 12 QA domains: test strategy, test planning, test case design, manual testing, API testing, performance testing, security testing, automation frameworks, regression testing, bug reporting, CI/CD quality gates, and QA metrics and reporting both AI levels in every module.
You will be able to generate complete test suites from requirements, build self-healing automation, deploy bug detection agents that run on every build, and take on the quality strategist role that AI is creating at the top of the QA career ladder.
16+4 hours, 12 modules, from test strategy to CI/CD quality gates, Gen AI and Agentic AI in every session
Self-healing test suites, AI detects UI changes and auto-updates locators, eliminating post-release maintenance cycles
AI test case generation, full coverage from user stories including edge cases humans routinely miss
Bug detection agents, autonomous inspection on every build with structured, actionable diagnostic reports
200+ QA-specific prompts in a personal library, 6 completed real-world capstone projects and earn 24 PDUs and 24 SEUs, valid for credential renewal
Learning Objective:
Use AI to build comprehensive test strategies faster, ensuring complete coverage without the hours of manual analysis that traditional test planning requires.
Topics:
Learning Objective:
Use AI to write better test cases faster, achieving higher coverage, catching more edge cases, and producing clearer documentation that any tester can follow.
Topics:
Learning Objective:
Use AI to write automation scripts faster, design maintainable test frameworks, and systematically expand the automated regression suite.
Topics:
Learning Objective:
Use AI to write clearer bug reports, manage defects more effectively, and accelerate communication between QA and development teams.
Topics:
Learning Objective:
Use AI to design and execute comprehensive API tests, verifying contracts, testing edge cases, and validating integration behaviour systematically.
Topics:
Learning Objective:
Use AI to design meaningful performance tests, interpret results accurately, and make evidence-based performance improvement recommendations.
Topics:
Learning Objective:
Use AI to incorporate security testing into the QA process systematically, identifying vulnerabilities before they reach production.
Topics:
Learning Objective:
Use AI to make regression testing faster, smarter, and more targeted, focusing effort where it matters most for each specific release.
Topics:
Learning Objective:
Use AI to create, manage, and maintain the test data that quality testing depends on, without manual effort or privacy violations.
Topics:
Learning Objective:
Use AI to measure, analyse, and communicate quality effectively, giving development teams, managers, and stakeholders the quality intelligence they need.
Topics:
Learning Objective:
Use AI to make exploratory testing more structured, productive, and documentable, getting more value from every unscripted testing session.
Topics:
Learning Objective:
Apply every skill from the course to a realistic end-to-end quality engineering scenario across planning, design, automation, and reporting.
Capstone Project 1:
Complete test plan for a defined feature with risk analysis and coverage strategy
Capstone Project 2:
Test case suite for a complex user story including edge cases and negative scenarios
Capstone Project 3:
Automation script for a critical regression scenario using a defined framework
Capstone Project 4:
Professional bug report and defect root cause analysis from a simulated failure
Capstone Project 5:
Sprint quality metrics report and release recommendation with supporting evidence
Capstone Project 6:
Personal 90-day AI adoption roadmap for your quality engineering practice
To fast-track your career and achieve
There is no exam for this course.
All students must complete AI Foundations before this course. AI Foundations covers Machine Learning, Generative AI, Prompt Engineering, Agentic AI, MLOps, and LLMOps — so this course starts immediately at the application level. Total program: 20 hours (4 hrs Foundations + 16 hrs role course). No technical background required.
Agilefever’s Gen AI and Agentic AI for QA and Test engineers training transformed the way I manage projects. AI manages my risk tracking and stakeholder communications; I’m finally ahead of the game.
The questions, tools, and case studies all felt tailor-made for a QA engineer like myself. Agilefever has helped me manage smoother sprints and more effective meetings.
I was hesitant about AI. Now I can’t work without it. AgileFever made Gen AI simple, useful, and surprisingly fun to learn.
Agents automate execution — not judgment. Research by Anthropic found developers can fully delegate only 0–20% of tasks to AI, and QA is similar. What changes is where your value sits: less manual script writing, more test strategy, AI governance, and quality architecture. The World Quality Report 2025-26 found GenAI is the #1 skill employers are hiring for in quality engineering — that is a demand signal, not a replacement signal.
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.
PDUs count toward PMP and CAPM renewal with PMI under Technical Education. SEUs count toward SAFe credentials and Scrum Alliance renewals. ISTQB accepts continuing education toward Advanced Level and CTAL-TAE renewal — verify current requirements with ISTQB directly. For most QA and testing credentials, this course covers a significant portion of a full renewal cycle.