AI-Driven End to End Test Automation powered by Azure
Brillio
Modern enterprises rely on interconnected applications and workflows, making software testing a critical yet complex and time-consuming process. Manual and siloed testing efforts often lead to longer release cycles, increased QA costs, and reduced confidence in software quality. Brillio’s AI-Driven End-to-End Test Automation addresses these challenges by automating the entire QA lifecycle using an intelligent, agent-based architecture.
This AI-native solution empowers QA and engineering teams to define high-level testing goals or expected outcomes rather than detailed steps. Intelligent agents plan, generate, and execute tests using retrieval-augmented generation (RAG), breaking down complex test intents through multi-agent orchestration loops. These agents interact across disparate systems, reusing existing test artifacts and adapting to new scenarios with minimal manual input.
Key Capabilities: • AI-Powered Test Planning on Azure: Multi-agent workflows break down regression needs into testable components, leveraging system documentation, past test cases, and functional maps to plan end-to-end tests. • Autonomous Execution and Monitoring: Headless execution on Azure App Services using Playwright, with real-time evidence capture (logs, screenshots, videos) stored in Azure Blob Storage for detailed audit and compliance reporting. • Reusable and Scalable Architecture: Built on Azure-native components, the solution reuses assets across test cycles, integrates with enterprise CI/CD pipelines, and supports secure scaling using Azure API Management and Cosmos DB. • Outcome-Driven Automation: Enables business and QA stakeholders to focus on desired outcomes rather than execution specifics, boosting QA efficiency, agility, and traceability across releases.
Deployment Approach: Brillio’s solution is designed for fast cloud deployment via the Azure Marketplace, supporting hybrid and multi-cloud testing environments. It seamlessly integrates with your existing Azure DevOps, GitHub Actions, or third-party automation frameworks while leveraging Azure-native capabilities for scalability, observability, and governance.
Business Impact: • Reduced regression testing cycle from 7 days to 2.5 days per release • 3x faster QA cycles • ~ 50%- 60% reduction in test creation effort • Improved test accuracy and reduced total cost of ownership • Eliminate the need for agents to re-write tests through reusable test assets • Continuous Expansion of Test Coverage Over Time
Brillio’s solution enables rapid digital transformation, strengthens QA coverage, and accelerates time-to-market—across industries and Azure environments
Azure Services Utilized • Azure OpenAI - Powers the core semantic intelligence using Anthropic Sonnet models. • Azure App Services - Enables easy and reliable orchestration of Containers and application hosting • Azure AI Search - Enables high throughput Vector Search with precision • Azure API Management - Provides secure, scalable access to microservices and endpoints • Azure Blob Storage - Global storage for all reports and artifacts for all the tests results. • Cosmos DB - Application transactional DB and Agent artifact store
Highlights:
Intelligent Test Planning with AI Agents: Multi-agent system intelligently translates outcome-based test inputs into executable test plans using past assets and documentation reducing planning time and enhancing reuse.
Self-Driving Execution & Real-Time Monitoring: Autonomously writes and executes tests in headless environments using tools like Playwright, generating rich, traceable reports with embedded evidence for fast issue identification.
Cost-Effective and Scalable QA: Blends AI-native agents with existing automation tools to minimize redundancy, cut down test creation efforts, and scale across teams with high ROI and lower QA costs.