How AI Agents Can Simplify End-to-End QA | UiPath | Agentic AI | LLM

This is an end-to-end agentic QA orchestration solution built using UiPath Test Cloud, UiPath Coded Agents, Orchestrator, Test Manager, Data Service, React, TypeScript, and the UiPath TypeScript SDK. The solution is designed to reduce the manual coordination required across requirements, test planning, automation, execution, defect management, and release reporting. In this demo, I showcase how QualityOps QA Console supports the complete QA lifecycle, including: • Requirement analysis using AI • Human-in-the-loop approval • Automated test scenario generation • Azure DevOps test case creation • Risk-based test planning and prioritization • UiPath Test Manager / Test Cloud integration • Automation mapping and orchestration • Automated test execution • AI-powered failure analysis and result triage • Defect/bug creation • Release-readiness assessment • Automated stakeholder reporting and email communication • UiPath Data Service for persistent QA decisions and review history The architecture combines LLM-powered reasoning with deterministic QA logic. AI agents are used where reasoning is required, while rule-based logic handles areas such as risk scoring, prioritization, readiness checks, and status mapping. Human approval is maintained before critical downstream actions, keeping the QA process controlled while still taking advantage of agentic automation. Built for the UiPath AgentHack – Test Cloud Track. #UiPath #UiPathTestCloud #AgenticAI #QualityEngineering #TestAutomation #UiPathTestManager #AI #SoftwareTesting #QAautomation #CodedAgents #AzureDevOps #AgenticAutomation