As someone deeply engaged in the evolution of quality engineering, I’ve seen firsthand how traditional automation is struggling to keep up with the scale, speed, and complexity of today’s digital systems. In this session, I’ll share why I believe the future lies in autonomous testing—a smart, self-directed approach powered by Agentic AI, self-healing frameworks, and GenAI-driven test generation.
I’ll walk you through the key differences between rule-based automation and truly autonomous systems that can learn, adapt, and optimize test strategies in real time. We’ll explore the benefits, risks, and real-world considerations that engineering, and quality leaders must navigate when moving toward autonomy.
I’ll also highlight how this evolution supports business-critical operations, using United Parcel Service brokerage and customs clearance systems as a real-world example. In a global logistics environment where compliance, real-time decisioning, and cross-border visibility are essential, software failures are not an option. Autonomous testing ensures that these complex systems remain resilient, responsive, and agile—even under constantly changing regulatory and operational conditions.

• My goal is to provide a clear understanding of what it takes to begin your own autonomous testing journey—and how it can transform not just QA practices, but broader business outcomes.

AI technology is ubiquitous and playing a critical role in decision making in all the modern applications across varied domains. As technologist we are embracing AI to improve our productivity and AI integration goes very deep into the system architecture. But we need to be aware of the fact that AI models or LLM can collapse over a period of time. Model outcome quality can degrade over a period of time. Blind trust in algorithmic outputs can lead to biased judgments, ethical violations, and critical errors with far-reaching consequences. This session emphasizes the necessity of cultivating the role of the AI verifier—a professional or organizational function dedicated to scrutinizing, validating, and monitoring AI systems.

• Importance of AI verifier role
• Real value of human data to keep the LLMs sane
• Model verification and continuous improvement
• Mindset transformation needed to change from traditional QA to AI verifier