Traditional software testing struggles to keep pace with rapidly evolving applications—resulting in brittle test cases, time-consuming maintenance, and poor bug detection. This session introduces a smarter, adaptive approach using AI-powered Multi-Agent Systems that automate and continuously improve testing workflows.

We’ll explore how Multi-Agent Retrieval-Augmented Generation (RAG) transforms testing by dynamically generating test cases, adjusting to app changes in real-time, and detecting bugs with greater accuracy. Each agent has a specialized role—retrieving context, generating tests, and analyzing results—working together as a self-learning testing team.

The session will include a live walkthrough of a Python-based pipeline using PyTest, Selenium, LangChain, and ML models to:
• Automate UI and regression testing with minimal manual intervention
• Generate intelligent, context-aware test cases from code and API specs
• Use anomaly detection to flag subtle bugs based on test logs
• Continuously evolve test logic as the app evolves

By leveraging AI agents, teams can reduce manual QA efforts, improve test coverage, and increase reliability across fast-moving software projects. Whether you're a QA engineer, developer, or test automation architect, this talk will give you practical tools and ideas to build scalable, AI-driven QA systems using Python.