Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems
Jianing Zhu, Yeonju Ro, John Robertson, Kevin Wang, Junbo Li, Haris Vikalo, Aditya Akella, Zhangyang Wang
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.
AI Impact Assessments
(1 models)Scientific Impact Assessment: "Your Agents Are Aging Too: Agent Lifespan Engineering for Deployed Systems"
1. Core Contribution
This paper introduces AgingBench, a longitudinal benchmark for measuring, diagnosing, and localizing reliability degradation in long-lived AI agents. The central insight is that even with frozen model weights, deployed agents experience state drift through their memory pipeline — a phenomenon the authors term "agent aging." The paper makes four interconnected contributions:
1. A four-mechanism taxonomy of agent aging: compression (write-time information loss), interference (retrieval confusion from accumulated similar memories), revision (failure to propagate state updates), and maintenance (regressions from lifecycle events like recompaction).
2. A temporal dependency DAG framework with programmatic generators that encode cross-session fact relationships, version chains, interference pairs, and accumulator structures.
3. Counterfactual diagnostic probes (P1/P2/P3) that localize failures to write, retrieval, or utilization stages of the memory pipeline.
4. Empirical findings across 14 models, 7 scenarios, and ~400 runs demonstrating that aging is multi-dimensional and not capturable by single-score benchmarks.
The problem formulation is genuinely novel. While prior work has studied multi-session memory and long-context degradation, no existing benchmark jointly addresses longitudinal degradation curves, mechanism-level diagnosis, and component-level attribution within a unified framework.
2. Methodological Rigor
Strengths in design: The temporal dependency DAG is an elegant formalization. By encoding version chains, dependency edges, and interference pairs as explicit graph structures, the benchmark achieves gold-grounded, mechanism-specific scoring — a significant improvement over end-to-end recall metrics. The PressureConfig system with four independent dials (dependency density, update rate, chain depth, confusable pairs) enables controlled ablation studies, and Figure 9 validates that these axes behave as independent variables.
The counterfactual probe design (P1/P2/P3) is well-motivated and practically useful. The ablation ladder — baseline → oracle retrieval → oracle context — provides actionable diagnostic information, not just rankings. The authors are appropriately cautious about attribution claims, framing results as "diagnostic profiles" rather than causal decompositions.
Concerns: The programmatic generation, while enabling scale and reproducibility, introduces a validity question: do synthetic task streams faithfully represent real deployment pressures? The authors acknowledge this explicitly, calling it a "controlled measurement surface," but the gap between synthetic scenarios and production agent behavior remains unvalidated. The multi-seed validation (Tables 12-13) shows non-trivial standard deviations on some metrics, and some cells have only 2-3 seeds, limiting statistical confidence. The P2 probe is "abstained" for single-blob memory architectures, leaving a diagnostic gap for the most common deployment pattern.
3. Potential Impact
Immediate practical value: The finding that behavioral compliance and factual precision degrade independently (Finding II) has direct implications for production monitoring — current behavioral violation-based monitoring systems would miss silent precision decay. The diagnostic profiles (Figure 6) demonstrating that identical error rates require different repairs across models and scenarios challenges the "give it more memory" default.
Broader influence: This work could catalyze a shift in how the community thinks about agent evaluation — from snapshot capability to longitudinal reliability. The four-mechanism taxonomy provides shared vocabulary for discussing deployment failures. The framework architecture (pluggable memory policies, scenario generators, diagnostic harness) is designed for community extension.
Adjacent fields: The paper draws parallels to database index staleness, software technical debt, and regression testing — suggesting potential cross-pollination with systems engineering and software reliability communities. The "aging as runtime control problem" framing (Appendix I) opens connections to control theory and adaptive systems.
4. Timeliness & Relevance
This paper addresses a genuine gap at a critical moment. As agents move from demos to persistent deployments (coding assistants, enterprise knowledge bases, personal planners), the failure modes described here — silent precision loss, accumulator drift, maintenance regressions — represent real deployment risks that existing evaluation infrastructure does not cover. The inclusion of production agents (Claude Code, OpenHands) alongside controlled ReAct agents demonstrates practical relevance.
The timing is particularly apt given the rapid scaling of agent deployment in 2025-2026, where lifecycle management is emerging as a key bottleneck that day-one benchmarks cannot address.
5. Strengths & Limitations
Key strengths:
Notable limitations:
Overall Assessment
This paper makes a strong conceptual contribution by formalizing agent aging as a first-class evaluation concern. The benchmark design is thoughtful and the empirical findings are both surprising and practically relevant. The main weakness is the gap between controlled synthetic pressure and real deployment — but this is explicitly acknowledged and the controlled approach is well-justified for initial mechanism identification. The work is likely to influence how the community evaluates and monitors deployed agents, and the four-mechanism taxonomy provides a useful organizing framework for future work on agent reliability.
Generated May 27, 2026
Comparison History (20)
Paper 2 introduces a theoretically grounded, practically deployable method (CES) for hallucination detection with formal guarantees, addressing a critical barrier to LLM adoption. Its combination of theoretical rigor (finite-sample calibration, convergence proofs), practical efficiency (single forward pass, black-box access), and strong empirical results across 8 benchmarks and 10 models gives it broad applicability. While Paper 1 addresses an important emerging problem (agent aging), the field of persistent agent deployment is still nascent, limiting near-term impact. Paper 2's hallucination detection method solves a more immediate, widely-recognized problem with broader cross-field relevance.
Paper 1 has higher potential impact: it introduces a general, mechanistic framework (agent “aging”) and a longitudinal benchmark (AgingBench) applicable to many deployed agent systems beyond web search, directly targeting reliability over time—a key real-world deployment bottleneck. Its taxonomy (compression/interference/revision/maintenance aging) plus diagnostic tooling (temporal dependency graphs, counterfactual probes) suggests actionable, stage-targeted repairs, indicating strong methodological contribution and broad relevance across memory-augmented agents, continual operation, and MLOps. Paper 2 is timely and useful but narrower (search/browsing) and primarily benchmark-refresh oriented.
Paper 1 introduces a fundamentally new research direction—agent lifespan engineering—addressing the overlooked problem of how AI agents degrade over time post-deployment. AgingBench provides a novel benchmark framework with clear taxonomy (compression, interference, revision, maintenance aging), diagnostic methodology, and extensive empirical validation across 14 models and ~400 runs. This opens a new subfield with broad implications for reliable agent deployment. Paper 2, while technically impressive as a large MoE model release, is primarily an engineering contribution in the competitive LLM scaling space with incremental novelty. Paper 1's conceptual framework is more likely to spawn follow-up research and shift evaluation paradigms.
Paper 1 introduces a highly novel and impactful concept ('agent aging') that addresses a critical gap in evaluating deployed AI systems longitudinally rather than just at initialization. Its comprehensive approach to categorizing aging mechanisms and diagnosing lifespan reliability offers broader conceptual innovation and long-term real-world applicability compared to Paper 2's narrower focus on stress-testing memory system operations.
Paper 1 introduces a foundational theoretical framework (GEM) that redefines long-term agent memory as a new data-management workload with formal correctness conditions and impossibility results showing record-level systems are insufficient. This has broader impact by establishing new abstractions that could reshape how the database and AI communities think about agent memory infrastructure. Paper 2 (AgingBench) provides valuable empirical benchmarking of agent degradation, but is more incremental—a diagnostic tool rather than a paradigm shift. Paper 1's formalization opens multiple research directions and is more likely to spawn follow-on work across database systems and AI agent communities.
Paper 2 has higher likely impact because it defines a timely, under-addressed deployment problem (longitudinal agent reliability), proposes a concrete benchmark (AgingBench) with mechanistic taxonomy and diagnostics, and evaluates broadly across models, scenarios, memory policies, and agent regimes—supporting methodological rigor and generality. Its contributions apply across many deployed agent systems (memory, retrieval, maintenance), influencing evaluation standards and engineering practices beyond any single reasoning task. Paper 1 is novel in combining A* with post-training for proofs, but its scope is narrower (NLI/proof-style reasoning) and may be more sensitive to task/setup specifics.
Paper 1 introduces a novel, timely framing—agent reliability as a longitudinal “lifespan” property—and contributes a mechanism-level benchmark (AgingBench) with diagnostic tooling (temporal dependency graphs, counterfactual probes) that can generalize across domains wherever persistent agents with memory are deployed. Its concepts (compression/interference/revision/maintenance aging) and methodology could reshape how agents are evaluated and engineered, impacting systems, evaluation, and deployment practices broadly. Paper 2 is highly useful and likely impactful in biomedicine via standardization and open-source infrastructure, but its primary contribution is tooling consolidation within a specific domain rather than a broadly new scientific paradigm.
Paper 1 introduces a novel, rigorous benchmark (AgingBench) addressing a previously underexplored problem—longitudinal reliability degradation of deployed AI agents. It provides empirical results across 14 models, 7 scenarios, and ~400 runs with concrete diagnostic mechanisms. This fills a significant gap in AI evaluation methodology with broad implications for deployed agent systems. Paper 2 presents a conceptual framework for agentic technical debt with simulation, but is more of a managerial modeling exercise ('note') with narrower scope and less empirical depth, limiting its scientific impact.
Paper 1 is more novel and broadly impactful: it reframes evaluation from static “day-one” benchmarks to longitudinal reliability of deployed agent systems, introduces a mechanistic taxonomy of aging failures, and provides diagnosis tools (temporal dependency graphs, counterfactual probes) that can guide targeted repairs. This directly addresses a timely, under-studied deployment problem relevant across agentic AI, memory systems, reliability engineering, and MLOps. Paper 2 is useful and timely for education-oriented LMM evaluation, but its contribution is primarily a new benchmark/dataset in a narrower application domain with less conceptual generality.
Paper 2 introduces a fundamentally new evaluation paradigm (AgingBench) addressing an overlooked but critical problem—long-term reliability degradation of deployed AI agents. This opens an entirely new research direction (agent lifespan engineering) with broad practical implications for real-world AI deployment. Its taxonomy of aging mechanisms and diagnostic framework are highly novel and timely as persistent AI agents proliferate. Paper 1, while technically solid and combining mechanistic interpretability with CoT faithfulness detection in a novel way, addresses a narrower problem within an already active research area, limiting its breadth of impact.
Paper 1 introduces a paradigm shift in AI evaluation by moving from static day-one benchmarks to longitudinal 'lifespan' reliability for deployed agents. Conceptualizing and measuring 'agent aging' addresses a critical, timely bottleneck in autonomous agent deployment across all domains. In contrast, while Paper 2 provides a valuable domain-specific dataset for medical speech, its scope and methodological innovation are narrower. Paper 1's foundational framework, methodological rigor, and broad applicability give it a significantly higher potential for widespread scientific and real-world impact.
Paper 1 is more novel and timely, reframing agent evaluation as a longitudinal “lifespan” problem and introducing a benchmark plus diagnostic methodology (mechanism taxonomy, temporal dependency graphs, counterfactual probes) that targets deployed, memory-using agents—an increasingly important real-world setting. Its scope (multiple models, scenarios, memory policies, long session horizons) suggests higher methodological and systems relevance and broader impact across agent design, evaluation, and reliability engineering. Paper 2 is solid but narrower (single model, one dataset, limited perturbations) and finds mostly non-significant differences, limiting impact.
Paper 1 identifies a fundamental structural vulnerability in RLHF—the dominant alignment paradigm for LLMs—showing how misaligned biases can be amplified through the preference learning pipeline. This has immediate, broad implications for AI safety and alignment research, affecting virtually all RLHF-trained models. Paper 2 introduces a valuable but more niche benchmark for long-lived agent reliability. While timely, agent lifespan engineering addresses operational concerns rather than a core methodological vulnerability. Paper 1's findings are more likely to reshape alignment practices and inspire substantial follow-up research across the AI safety community.
Paper 2 addresses a timely and broadly impactful problem—the longitudinal reliability of deployed AI agents—that affects the entire growing ecosystem of persistent LLM-based agents. It introduces a concrete benchmark (AgingBench) with a novel taxonomy of aging mechanisms, tested across 14 models and ~400 runs, providing immediate practical utility. Paper 1 makes interesting theoretical contributions about policy gradient failures in long-horizon cumulative-damage problems, but its scope is narrower (two specific environments) and its audience more limited. Paper 2's framework is likely to catalyze a new subfield of agent reliability engineering with broader cross-disciplinary impact.
Paper 1 introduces a fundamentally new research paradigm—agent lifespan engineering—addressing a critical gap as persistent AI agents become widespread. AgingBench provides a comprehensive framework with novel concepts (compression/interference/revision/maintenance aging, temporal dependency graphs, counterfactual diagnostics) validated across extensive experiments. Its breadth of impact spans agent systems, reliability engineering, and deployment practices. Paper 2, while solid and practical with FEPoID for hallucination detection layer selection, addresses a narrower technical problem within an already active research area, offering incremental rather than paradigm-shifting contributions.
Paper 2 likely has higher impact: it introduces a general, formal-methods-inspired framework (POLARIS) that compiles natural-language policies into logic, enabling coverage-driven, reproducible safety testing with traceable guarantees. This is timely given regulatory and deployment pressure around LLM safety, and has broad applicability across domains requiring policy compliance (health, finance, enterprise, gov) and across fields (AI safety, formal methods, software testing). Paper 1 is novel and useful for agent reliability, but is narrower to long-lived agent memory/harness dynamics and benchmarking.
Paper 1 introduces a fundamentally new evaluation paradigm (AgingBench) for long-lived AI agents, addressing a critical gap as persistent agents become widespread. It defines a novel taxonomy of aging mechanisms, provides diagnostic tools, and presents extensive empirical evidence across 14 models and ~400 runs. This has broad impact across all AI agent deployments. Paper 2, while valuable for biomedical knowledge contextualization, addresses a more domain-specific problem with a framework (SCENE) that, while useful, is more incremental in its multi-agent optimization approach. Paper 1's timeliness and breadth of applicability give it higher impact potential.
Paper 1 likely has higher scientific impact due to broader, more foundational relevance: it reframes evaluation for deployed, long-lived agents and introduces AgingBench to diagnose multiple aging mechanisms across memory pipelines. This targets a core reliability problem for real-world agent systems (monitoring, maintenance, repair) and can influence benchmarking, agent architecture, and deployment practices across domains. Paper 2 is timely and useful for peer-review integrity, but its application scope is narrower and may be more venue- and dataset-specific, with impact concentrated in research publishing workflows rather than general agent reliability.
Paper 2 likely has higher scientific impact. It targets an emerging, widely relevant problem—reliability of long-lived deployed agents—introducing a concrete benchmark (AgingBench), a taxonomy of aging mechanisms, and diagnostics tied to actionable repairs across the memory pipeline, evaluated across many models/scenarios and long horizons. This combines novelty, methodological rigor, timeliness, and broad applicability to agentic systems, MLOps, and safety/reliability engineering. Paper 1 offers an important alignment measurement lens but is more domain-specific and its broader methodological generalization is less clearly demonstrated.
Paper 1 introduces a paradigm-shifting concept ('agent aging') and a comprehensive benchmark for long-lived AI systems. As AI transitions from stateless models to persistent agents, evaluating long-term memory degradation and reliability is critical. While Paper 2 offers a practical, prompt-based approach to unlearning, Paper 1 establishes a new foundational area in agent lifespan engineering, offering broader, more transformative long-term impact for deployed AI systems.