Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems
A new benchmark called AgingBench reveals that deployed AI agents degrade over time through four aging mechanisms, requiring lifespan evaluation and targeted repair rather than just stronger base models.
Article intelligence
Key points
- AI agents degrade after deployment due to memory and state changes.
- AgingBench identifies four aging mechanisms: compression, interference, revision, and maintenance.
- Diagnostic profiles help pinpoint specific stages in the memory pipeline for repair.
- Lifespan engineering is essential for reliable long-term agent deployment.
Why it matters
This matters because AI agents degrade after deployment due to memory and state changes.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.26302] Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems
[Submitted on 25 May 2026]
Title:Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems
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Abstract:Long-lived AI agents are increasingly deployed as persistent operational systems, yet they are still evaluated like freshly initialized models. Day-one benchmarks miss a basic systems question: how long does an agent remain reliable after deployment? Even when model weights are frozen, an agent's effective state keeps changing as it compresses interaction history, retrieves from a growing memory store, revises facts after updates, and undergoes routine maintenance. Reliability therefore becomes a lifespan property of the full agent harness, not only a snapshot property of the base model. We introduce AgingBench, a longitudinal reliability benchmark for agent lifespan engineering: measuring not only whether deployed agents degrade, but what form the degradation takes and where repair should target. AgingBench organizes agent aging into four mechanisms: compression aging, interference aging, revision aging, and maintenance aging. To diagnose these failures, AgingBench uses temporal dependency graphs and paired counterfactual probes that produce diagnostic profiles for the write, retrieval, and utilization stages of the memory pipeline. Across 7 scenarios, 14 models, multiple memory policies, and both runner-controlled and autonomous agents, over ~400 runs spanning 8 - 200 sessions show that agent aging is not one-dimensional: behavioral tests can remain clean while factual precision decays; derived-state tracking can collapse sharply within a single model; and the same wrong answer can require different repairs depending on what the diagnostic profile points to. These results suggest that reliable agent deployment requires lifespan evaluation, mechanism-level diagnosis, and stage-targeted repair, not only stronger day-one models.
Subjects:
Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Multiagent Systems (cs.MA)
Cite as: arXiv:2605.26302 [cs.AI]
(or arXiv:2605.26302v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2605.26302
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Jianing Zhu [view email] [v1] Mon, 25 May 2026 19:55:12 UTC (5,159 KB)
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