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.
2 outlets in our directory ran this story, first to last over 6 hours. All of the coverage we found sits in one bucket: centre. That one-sidedness is itself worth noticing.
arXiv cs.AI files mainly under ai research. We currently carry 1,128 of its stories.
Story provenance
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Story provenance
Attribution is not the same as permission. This drawer separates discovery metadata, excerpts, WeSearch-generated summaries, reuse status, and whether the publisher receives the visit. Nothing here claims a legal grant the publisher has not made.
Record
| Original publisher | arXiv cs.AI |
| Canonical URL | https://arxiv.org/abs/2605.26302 |
| Publication time | Wed, 27 May 2026 00:00:00 -0400 |
| Retrieval time | 2026-05-27T04:07:56.398Z |
| Last seen | 2026-05-27T04:07:56.398Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | CQb6pcO65nbn · 2 stories |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
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.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at arXiv cs.AI.
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