GenAI & AI Agents for QA Automation | Copilot & Claude code

Masterclass: Boost Your Testing Productivity with AI Agents (Copilot, Claude, n8n, MCP &;Agentic Solutions)

Learn Agentic AI – Build Multi-Agent Automation Workflows

Build Autonomous AI Agents with AutoGen & MCP –End-to-End Agentic AI Workflows with 6 Agents for Real-World Automation. Welcome to the future of AI automation – Agentic AI with the AutoGen Framework. This course is your ultimate guide to building intelligent, autonomous AI agents that collaborate, self-correct, and execute complex tasks without constant human intervention. Even if you're new to this space, we start from the absolute basics—no prior knowledge of agent frameworks is required. You’ll start by mastering the core foundations – Large Language Models (LLMs), AI Agents, Multi-Agents, and the Model Context Protocol (MCP), which serves as the backbone for agent-to-tool communication. Next, you’ll learn how to engineer contexts and craft prompts that unlock the true potential of your AI agents. Through hands-on projects, you’ll build real-world agents like Browser Automation Agents, Database Agents, API Agents, and Jira Agents.

2026 - ISTQB AI Testing (CT-AI) Certification - Crash Course

A complete practical course to make you understand AI Testing with ISTQB exam readiness.Traditional software testing assumes predictable logic and fixed expected outputs. AI systems don’t work that way. They learn from data, evolve over time, behave probabilistically, and often operate as black boxes. This shift breaks many traditional testing assumptions. In this course, you will learn how to test AI-based systems the right way, using globally accepted ISTQB AI Testing (CT-AI) principles, explained clearly and practically for testers. This course starts by building strong foundations. You will first understand what AI really is, how AI-based systems differ from conventional software, and why new testing strategies are required. You’ll then learn machine learning fundamentals — supervised, unsupervised, and reinforcement learning — not as a data scientist, but from a tester’s mindset.