Ensuring cognitive stability in autonomous multi-agent systems (MAS) is a central challenge for large-scale, distributed AI. While existing observability tools monitor system outputs, they cannot quantify how rapidly agentic workflows recover once reasoning coherence has been lost. We adapt classical reliability metrics-Mean Time-to-Recovery (MTTR), Mean Time Between Failures (MTBF), and related ratios-into the cognitive domain, defining MTTR-A (Mean Time-to-Recovery for Agentic Systems) as a runtime measure of cognitive recovery latency. MTTR-A quantifies the time required for a MAS to detect reasoning drift and restore consistent operation, capturing the recovery of reasoning coherence rather than infrastructural repair. A benchmark simulation using the AG~News corpus and the LangGraph orchestration framework was conducted, modeling recovery latencies across multiple reflex modes. Automated reflexes restored stability within approximately 6s on average, while human-approval interventions required about 12s. Across 200 runs, the median simulated MTTR-A was 6.21+-2.14s, MTBF=6.7+-2.14s, and NRR=0.08, demonstrating measurable runtime resilience across reflex strategies. By formalizing recovery latency as a quantifiable property of distributed reasoning-and deriving reliability bounds linking recovery time and cognitive uptime-this work establishes a foundation for runtime dependability in agentic cognition, transforming cognitive recovery from an ad-hoc process into a standardized, interpretable performance

翻译：确保自主多智能体系统（MAS）的认知稳定性是大规模分布式人工智能的核心挑战。现有的可观测性工具虽能监测系统输出，却无法量化智能体工作流在推理一致性丧失后的恢复速度。我们将经典可靠性指标——平均恢复时间（MTTR）、平均故障间隔时间（MTBF）及相关比率——引入认知领域，定义MTTR-A（智能体系统平均恢复时间）作为认知恢复延迟的运行时度量。MTTR-A量化了MAS检测推理漂移并恢复一致运行所需的时间，捕捉的是推理一致性的恢复而非基础设施修复。通过使用AG~News语料库和LangGraph编排框架进行基准模拟，建模了多种反射模式下的恢复延迟。自动化反射平均约6秒内恢复稳定性，而需人工审批的干预则需约12秒。在200次运行中，模拟MTTR-A中位数为6.21±2.14秒，MTBF=6.7±2.14秒，NRR=0.08，证明了不同反射策略下可度量的运行时韧性。通过将恢复延迟形式化为分布式推理的可量化属性，并推导出连接恢复时间与认知正常运行时间的可靠性边界，本研究为智能体认知的运行时可靠性奠定了基础，将认知恢复从临时过程转变为标准化、可解释的性能指标。