Automated UI testing is essential but notoriously fragile, often failing to keep pace with rapidly evolving interfaces and documentation. In this talk, we introduce a novel multi-agent architecture that leverages large language models (LLMs) to transform static documentation into dynamic, executable UI tests—without writing code. Our system extracts structured user journeys from product manuals, refines them into atomic tasks using LLM agents, and executes them autonomously in live browser environments.
This talk is for developers, QA engineers, and AI practitioners building or maintaining test automation pipelines in enterprise environment. Attendees will gain insights into architecting LLM-powered agents for structured extraction, and improving test resilience. We'll explore how this system achieves high execution accuracy and adaptive self-correction, even under ambiguous or incomplete specs.