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原文摘要

Long-lived AI agents are increasingly deployed as persistent operational systems, yet they are still mostly evaluated like freshly initialized models. Day-one benchmarks miss a basic systems question: how does an agent's reliability change 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, where writing and rewriting drops future-relevant details; interference aging, where accumulated similar memories crowd out the target fact; revision aging, where changed or derived state is not updated correctly; and maintenance aging, where lifecycle events such as model swapping trigger regressions. To diagnose these failures, AgingBench uses temporal dependency graphs and counterfactual probes that produce diagnostic profiles for the write, retrieval, and utilization stages of the memory pipeline. Across 6 scenarios, 10 memory systems and 6 models from two families, we find that agent aging is not one-dimensional over 400 runs spanning 50-200 sessions: a well-formed reply can hide a value the memory no longer supplies; derived state collapses as updates accumulate; and the same wrong answer can require different repairs depending on what the diagnostic profile points to.