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QE Leaders Have a New Mandate. You should never audit your own work. With AI, that is often exactly what happens. The team building the system also decides whether it behaves as intended. And that is the first hard problem. AI now reasons across steps, calls tools, uses enterprise context and takes action. So “Does the software work?” is no longer enough. The real question is: Who independently verifies that the AI behaves as intended? Groundedness. Tool use. Permissions. Policy compliance. Failure modes. Human intervention. We have spent the last year figuring out how to make that assurance independent, testable and repeatable. There is a second hard problem too: As AI accelerates the SDLC, can QE accelerate with it — or does QE become the bottleneck? On September 23 at 11 AM ET, our QE architects will open up exactly how we have approached both. No theory. No pitch. Just the architecture, how it works, what it tests and where it can fail. Register: https://lnkd.in/g_XSabax #QualityEngineering #AIQE #AgenticAI #IndependentAssurance #AIAssurance #Apexon #WeEngineerIntelligentEnterprises Somesh Khanna | Sriniketh Chakravarthi | Bhavesh Mehta | Justin Marcucci | Shikhar Puri | Rajasekhar Akiri | Karthikeyan Vedagiri | Shridhara Bhat | Siddharth Rao | Raman J. | Jaspreet Singh | Rajashree Ramapure

Does validating a model guarantee its success in real-world applications? Not always. That’s why testing the surrounding system matters as much as refining the algorithm itself. Discover how true assurance starts here.

Trust isn't built overnight; it's earned through consistency and excellence. At TrustAlpha, we help you ensure that every aspect of your AI systems performs impeccably, aligning perfectly with what matters most to you and fostering confidence in its outcomes.

What happens when 2% of your AI's decisions miss the mark? Every misstep can ripple through claims and charts, creating inefficiencies. Testing the entire application, not just the model, ensures every decision aligns perfectly with your goals.

Achieve unparalleled accuracy in AI-driven decisions by focusing on comprehensive quality assurance. It's not just about testing algorithms, it's about ensuring that each part of your system works together flawlessly to meet your objectives without compromise.