AI agents promise autonomy, reasoning, and efficiency—but in practice they often lose context, make questionable decisions, or behave like they’ve forgotten what they were doing halfway through a task.
If that sounds familiar, you’re not alone.
In this session, I’ll share the real-world mistakes I made while building and experimenting with AI agents—and the practical solutions that actually worked. We’ll look at how to improve reasoning, preserve context, design better decision flows, and avoid common architectural pitfalls that quietly kill performance.
With more vendors shifting from “hook you in” pricing to transparent pay‑for‑what‑you‑use models, efficiency isn’t just a technical concern anymore—it’s a financial one. I’ll show you how smarter agent design can significantly reduce costs without sacrificing capabilities.
You won’t leave with a “perfect” agent—but you will leave with proven patterns, hard-earned lessons, and clear ideas you can apply immediately to build more reliable, predictable, and cost‑efficient AI agents.