Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems

2026-05-25

AI Summary

This work addresses the critical gap in current AI agent evaluation, which predominantly emphasizes initial performance while overlooking the dynamic degradation of reliability over time post-deployment. To bridge this gap, the authors propose an Agent Lifespan Engineering framework that conceptualizes reliability as a lifecycle-wide property. They introduce AgingBench, a longitudinal benchmark designed to systematically track performance evolution under four aging mechanisms: compression, perturbation, revision, and maintenance. Leveraging temporal dependency graphs and pairwise counterfactual probes, the study enables fine-grained diagnosis of memory pipeline stages—writing, retrieval, and utilization. Through approximately 400 experiments across seven scenarios, 14 models, and diverse memory strategies, the findings reveal pronounced multidimensional heterogeneity in agent aging, underscoring the necessity of mechanism-level diagnostics and stage-targeted interventions rather than reliance on stronger initial models alone.

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.

Problem

• agent lifespan
• reliability degradation
• longitudinal evaluation
• memory aging
• deployed AI systems

Innovation

• agent lifespan engineering
• AgingBench
• longitudinal reliability
• memory aging mechanisms
• counterfactual probing