Most AI agents don’t fail dramatically—they just fade away. They’re tried a few times, maybe used in a sprint or two, and then dropped. Others become routine, but don’t actually speed up delivery, improve code quality, or reduce incidents—and sometimes add more process. Clearly, “we’re using AI” isn’t the same as “our engineering teams are shipping better and faster because of it.”


This talk highlights what most AI stories skip: focusing on real engineering pain and measuring impact across the delivery pipeline.
We’ll start by identifying concrete challenges—not cool AI demos—like slow code reviews, context switching, on-call overload, or hard-to-find documentation. Then we’ll look at designing agents and prompts tied to clear technical outcomes: reduced lead time, faster incident resolution, less toil, better developer experience, and happier customers. I’ll introduce a simple, “flight plan”–style framework any engineering org can use to track AI impact—from small experiments to rollout and long-term adoption—so you can see which agents really move the needle.


You’ll see real-world agent examples: where they made a measurable difference, removed friction, and changed workflows to unlock value. You’ll leave with a practical way to judge your own AI efforts: which engineering problems to solve first, what to measure along the SDLC, and how to keep only the agents that clearly improve outcomes—for the business and for developers writing, reviewing, and running the code.