Unverified50% confidenceFactExact time
Long-running agents do not fail at the starting point but instead undergo chronic degradation over time in the form of latency drift, memory growth, and retrieval decay.
1
Sources
50%
Confidence
Long-term
Relevance
10/12/2026
First Seen
Sources
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Unverified对于智能体类应用，长时间稳定运行比运行速度更重要，因中断会导致缓存状态丢失和任务失败80% similarVerifiedThe Ebbinghaus forgetting curve reveals that memory strength decays exponentially over time, and this process can be slowed through spaced repetition.76% similarVerifiedOver-reliance on AI programming tools leads to the negative effects of cognitive offloading, with procedural memory and declarative memory declining at different rates and following different recovery pathways.75% similarUnverifiedOverly lenient stopping conditions cause agents to continue ineffective iterations on sufficiently good results, wasting computing resources, while overly strict conditions may cause the loop to never terminate.75% similarUnverifiedMemory drift is an easily overlooked failure mode in long-running agents, where early misjudgments can become premises for subsequent inferences, forming erroneous self-reinforcing loops.74% similar
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