The deployment of long-lived AI agents is becoming more common, yet current evaluation benchmarks do not adequately address the aging of these systems.
Existing methods tend to treat AI agents as if they are newly initialized, overlooking the critical factors that influence their performance over time.
Research emphasizes the necessity for tailored evaluation strategies that reflect the unique challenges posed by persistent operational systems.