Learn AI Tools & Build AI Agents for Testing & QA Automation

About this course

Course last Updated -August 2026 with topic : Claude Code Skill System workflowsAI is no longer just a buzzword in software testing. It is becoming a real productivity multiplier for QA engineers, automation testers, and quality engineering teams. This course is built to help you move beyond theory and learn how to actually use AI tools, AI agents, Claude Code, GitHub Copilot, MCP servers, n8n workflows, and low-code AI testing platforms in practical testing scenarios.

We begin with the fundamentals by covering AI testing terminology, privacy and security considerations, prompt engineering, token concepts, context window limitations, and techniques to generate better AI responses. You will learn how to use AI effectively for creating test plans, test cases, test strategies, and test data combinations from business requirements.

The course then moves into hands-on implementation. You will see how GitHub Copilot can help fix code issues and speed up automation development inside real coding environments. From there, we dive deep into Model Context Protocol (MCP) and show how to build powerful automation agents that can interact with browsers, APIs, SQL databases, local files, Excel sheets, and Git workflows.

A major highlight of this course is Building Agentic AI for Quality Engineering with Claude Code. You will learn how to work with Claude Code skill systems, create domain knowledge skills, design agent skills, avoid context bloat with smart references, and build agents that can understand project documentation, generate test scenarios, design test strategies, write tests, run tests, and even help fix failed tests by referring back to domain docs.

You will also learn how sub-agents, multi-agent collaboration, and agentic AI solutions can be used to break down complex

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