The Trust Stack: How to Design an AI Agent That Earns Its Autonomy

About this session

Everyone says their AI agent improves itself, and most of those claims fall apart the moment you ask how the agent knows it actually got better. In customer service, where one wrong answer can cost a brand its credibility, the hard engineering problem lives in the system you build around the model, because the model itself will always be fallible. This talk introduces the Trust Stack, a bottom-up way to design agents where autonomy is something the system earns one layer at a time. It shows why an agent has to be scoped, observable, and correctable before it has any business changing itself, and how each of those layers runs in production at Delight, including a self-improvement loop that only ships a change once it has proven safe. The talk is also honest about what is genuinely hard, what still needs a human in the loop, and where the industry hype runs ahead of what these systems can really do. Anyone building or evaluating AI agents will leave with a practical framework for deciding how much freedom to hand them.