Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems
The paper discusses the aging of AI agents deployed in operational systems and introduces a new benchmark called AgingBench. This benchmark evaluates the reliability of agents over time, focusing on various mechanisms of degradation. The findings suggest that effective agent deployment requires ongoing evaluation and targeted repairs rather than relying solely on initial model performance.
- ▪Long-lived AI agents are evaluated like freshly initialized models, which overlooks their reliability over time.
- ▪AgingBench measures agent degradation and identifies specific areas for repair through a longitudinal approach.
- ▪The study reveals that agent aging is complex, with behavioral tests remaining intact while factual precision declines.
Opening excerpt (first ~120 words) tap to expand
Computer Science > Artificial Intelligence arXiv:2605.26302 (cs) [Submitted on 25 May 2026] Title:Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems Authors:Jianing Zhu, Yeonju Ro, John Robertson, Kevin Wang, Junbo Li, Haris Vikalo, Aditya Akella, Zhangyang Wang View a PDF of the paper titled Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems, by Jianing Zhu and 7 other authors View PDF HTML (experimental) Abstract:Long-lived AI agents are increasingly deployed as persistent operational systems, yet they are still evaluated like freshly initialized models.
…
Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
Discussion
0 commentsMore from arXiv cs.AI
