Kay Malcolm

The model is the easy part. Everything that makes an agent survive contact with production lives in the harness around it: orchestration, tooling, governance, and the memory core that keeps the system grounded when the model itself is probabilistic, forgetful, and non-deterministic. This talk walks the surface areas of an agent harness and consolidates the lessons we're learning as we ship them, from agentic applications in their current form (autonomous systems that now build their own automations) to the continual-learning loops that let agents improve from their own experience. We'll look at how the discipline is segmenting. AI application development is no longer one role but several: agent engineers, memory engineers, and platform engineers. We'll map Oracle's primitives onto each as the current state of harness engineering takes shape. We'll also examine the two populations betting on this stack at once, enterprise customers who need governance, reliability, and scale, alongside the cracked developers who need fast, composable primitives, and why a well-engineered harness serves both. And we'll make the case that has held through every shift in the stack: memory isn't a feature you bolt on, it's the foundation the rest of the harness stands on. The database remains the memory core, and when everything above it is probabilistic, it's the last line of defense.