Sonam Pankaj

This talk addresses a critical failure point in AI agents: the inability of evaluation signals to inform future actions, leading to repeated errors. The core thesis is that current agent memory systems are static and do not learn from past successes or failures. The presentation introduces a new runtime experience, Agent RX, designed to bridge this gap by allowing agents to improve dynamically based on outcomes without retraining or manual prompt engineering.