Overview
Architected an intelligent test automation platform for a leading luxury automotive manufacturer, using multi-agent AI systems to autonomously convert test cases into Pytest scripts and validation reports. Built a simulation suite validating ADAS behavior across 80+ driving scenarios in compliance with US federal, state, and county traffic laws.
Delivered 13 milestones of L3/L4 autonomous vehicle demonstrations to the client, securing $1.5M in sales while serving as the primary client-facing software engineer for 1.5 years.
Key Results
- ▸70% reduction in manual testing effort using multi-agent AI (LangChain & Crew.AI) to autonomously generate and execute Pytest test cases
- ▸80+ driving scenarios validated across real-world conditions with comprehensive compliance to federal and state traffic laws
- ▸$1.5M in sales secured through 13 client deliverables demonstrating L3/L4 autonomous vehicle capabilities over 1.5-year engagement
Architecture & Technologies
Core Technologies
- • Python & PyTest framework
- • Multi-agent AI (LangChain & Crew.AI)
- • Generative AI models
- • Test-driven development
- • Simulation & validation frameworks
- • Cloud deployment
Key Features
- • AI-driven test case generation
- • Autonomous validation workflows
- • Dynamic requirement adaptation
- • Test coverage optimization
- • Real-time result tracking
- • Cross-team integration
Challenges & Solutions
AI-Driven Test Generation Accuracy
Implemented multi-stage validation loops and iterative NLP model refinement to ensure AI-generated test cases accurately captured functional requirements and real-world driving scenarios without manual refinement.
Autonomous Execution Reliability
Built robust error handling, automatic retry logic, and state normalization to enable unattended test execution across flaky environments and ensure consistent validation of L3/L4 behavior.