MTTR-A: Measuring Cognitive Recovery Latency in Multi-Agent Systems

• URL: http://arxiv.org/abs/2511.20663v2
• Date: Fri, 28 Nov 2025 15:40:30 GMT
• Title: MTTR-A: Measuring Cognitive Recovery Latency in Multi-Agent Systems
• Abstract summary: We adapt reliability metrics-Mean Time-to-Recovery (MTTR), Mean Time Between Failures (MTBF), and related ratios to the cognitive domain.<n>This work establishes a foundation for runtime dependability in agentic cognition, transforming cognitive recovery from an ad-hoc process into a standardized, interpretable performance.
• Score: 1.9544213396776273
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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