Diane Lin

This talk addresses the common issue of AI agents exhibiting inconsistent behavior, often disagreeing with their own previous outputs or decisions. It explores the underlying causes of this self-disagreement and provides practical strategies for engineers to mitigate these problems, leading to more reliable and predictable agent performance. The core thesis is that understanding and managing agent internal state and decision-making processes is crucial for building robust AI systems.