Jaebau is a specialized Agentic End-to-End (E2E) testing and diagnostics platform designed to streamline the identification, reproduction, and sharing of failure causes in browser-based testing environments. The solution integrates Large Language Model (LLM) agents with automated testing workflows to provide high-fidelity "FAIL Reports" and evidence-based triage for modern web applications. By shifting focus from manual script maintenance to automated cause classification, Jaebau optimizes the feedback loop between quality assurance and software development. Target Audience and Use Cases The platform is engineered for QA Leads, Software Developers in Test (SDETs), and Developer Experience (DevEx) teams who manage high-frequency deployment pipelines. Key use cases include automated triage for unstable test suites, the generation of shareable evidence for bug reproduction, and the implementation of self-healing regression tests within existing Continuous Integration (CI) systems. Key Product Capabilities AI-Powered Scenario Builder: Jaebau provides a no-code interface where users define user journeys in natural language. The AI agent interprets these instructions to interact directly with the web interface, eliminating the need for traditional code-heavy test authoring. Automated Failure Triage and Diagnostics: Beyond simple execution, the platform analyzes the root cause of test failures. It categorizes errors into functional bugs, network latencies, or UI inconsistencies, providing development teams with actionable insights immediately. Evidence-Based FAIL Reports: Every unsuccessful test run generates a comprehensive report containing high-definition video replays, console logs, and network snapshots. This evidence simplifies communication between QA and engineering by providing a clear visual record of the failure point. Technical Value Proposition By addressing the core difficulties of browser-based testing—specifically the high cost of maintenance and the complexity of failure analysis—Jaebau reduces the total cost of quality ownership. The platform enables teams to scale their testing coverage without increasing technical debt, ensuring that web applications maintain consistent functional integrity. This objective, evidence-based approach to quality assurance allows organizations to accelerate their release cycles while maintaining a high standard of user experience.