Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems
Quick Answer
AgingBench introduces a longitudinal benchmark for evaluating the reliability of AI agents post-deployment, revealing that agent aging involves multiple mechanisms like compression and interference aging.
Quick Take
AgingBench introduces a longitudinal benchmark for evaluating the reliability of AI agents post-deployment, revealing that agent aging involves multiple mechanisms like compression and interference aging. The study shows that effective lifespan evaluation and targeted repairs are essential for maintaining agent performance over time, as behavioral tests may not reflect factual precision decay.
Key Points
- AgingBench measures agent reliability over time, focusing on degradation forms and repair targets.
- Four aging mechanisms identified: compression, interference, revision, and maintenance aging.
- Study involved ~400 runs across 7 scenarios and 14 models, highlighting multi-dimensional aging.
- Behavioral tests may remain stable while factual precision deteriorates significantly.
- Effective agent deployment requires lifespan evaluation and mechanism-level diagnosis.
Paper Resources
Article Content
From source RSS / original summaryarXiv:2605. 26302v1 Announce Type: new 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.
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