Alberto Romero

This talk introduces Meta AC, a novel framework designed to optimize AI agents by orchestrating multiple adaptation strategies beyond single-dimensional approaches. It addresses limitations in existing context engineering methods by employing a meta-controller that dynamically selects and allocates strategies based on task complexity, uncertainty, verifiability, and resource constraints. This multi-dimensional adaptation aims to improve agent performance, robustness, and efficiency across various tasks.