提出评估智能体长期可靠性的新方法，揭示其随时间退化的四种机制。
Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems
- 构建纵向评测框架AgingBench，追踪智能体部署后行为变化
- 实测显示8-200次会话中，事实精度下降但行为测试仍通过
- 支持按阶段诊断与修复，适用于长期运行的智能体系统
长期运行的AI智能体正被广泛部署为持续性系统，但当前评估方式仍如刚初始化模型般进行。首日基准忽略了核心系统问题：智能体在部署后能保持多长时间可靠？即使模型权重冻结，其有效状态仍会因交互历史压缩、记忆库增长、事实更新修订及常规维护而持续变化。可靠性因此成为整个智能体系统的寿命属性，而非仅基模型的瞬时特性。本文提出AgingBench——一个面向智能体寿命工程的纵向可靠性评测基准，不仅检测部署后是否退化，还分析退化形式并定位修复点。AgingBench将智能体老化归纳为四类机制：压缩老化、干扰老化、修订老化与维护老化。通过时间依赖图与成对反事实探针，生成内存流水线中写入、检索、使用各阶段的诊断画像。在7种场景、14个模型、多种记忆策略及人工控制与自主智能体下，经超过400次运行（覆盖8至200个会话）验证，智能体老化非单一维度：行为测试可保持完好，而事实精确度却持续下降；衍生状态追踪可能在单次模型内急剧崩溃；同一错误答案需不同修复策略，取决于诊断画像指向的具体阶段。结果表明，可靠部署需寿命评估、机制级诊断与阶段靶向修复，而不仅是更强的初始模型。
原文摘要 · Abstract (English)
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.
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