Testing in modern software development faces unprecedented challenges: faster release cycles, complex integrations, and dynamic user interfaces demand testing approaches that are both comprehensive and adaptable. Traditional test automation often falls short, requiring significant maintenance and struggling to evolve with rapidly changing applications.

This session demonstrates how Large Language Models (LLMs), Small Language Models (SLMs), and Model Context Protocol (MCP) can be orchestrated to create an intelligent, self-improving test automation ecosystem. We'll showcase a complete testing lifecycle powered by AI: from automated test case generation based on context, through test script development, intelligent execution with self-healing capabilities, to insightful test reporting that bridges technical and business perspectives.