AI Con USA 2026 - Test Automation Engineer

Although there are several controversies and misunderstandings surrounding AI and machine learning, one thing is apparent — people have quality concerns about the safety, reliability, and trustworthiness of these types of systems. Not only are ML-based systems shrouded in mystery due to their largely black-box nature, they also tend to be unpredictable since they can adapt and learn new things at runtime. Validating ML systems is challenging and requires a cross-section of knowledge, skills, and experience from areas such as mathematics, data science, software engineering, cyber-security,...

You can write 100% of your code with AI, and only be 15% more productive. Want to know why? Solving the problem of moving faster as an engineer is about way more than code. You design, you problem solve, you discuss and communicate, you test, you shepherd the PR through review, you deploy, and you observe. So why are you being told you should be 10x faster now that AI can write code? David will discuss practical solutions for safely accelerating your entire workflow with AI. He will look at how to collaborate with PM, how to use AI planning, how to ensure validations are solid and builds...

The next generation of security automation isn’t found in a chat interface—it’s found in Agents. While basic AI assistants can provide security advice and even remediate vulnerabilities, the real breakthrough for security teams lies in using an Agentic Workflow for security testing: the ability to build, customize, and orchestrate specialized agents that take ownership of the testing and remediation lifecycle. In this session, Jeffery Payne demonstrates how to move beyond conversational AI to create autonomous security agents using standardized formats compatible across tools like GitHub...