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This paper introduces MTTR-A, a novel runtime reliability metric for multi-agent systems that quantifies cognitive recovery latency, addressing the increasing limitation of cognitive failures over infrastructure faults. MTTR-A adapts classical dependability theory to agentic orchestration, measuring the time to detect reasoning drift and restore coherent operation, and is complemented by MTBF and a normalized recovery ratio (NRR). Empirical evaluation using a LangGraph-based benchmark with simulated drift and reflex recovery demonstrates measurable recovery behavior across multiple reflex strategies, establishing a quantitative foundation for runtime cognitive dependability.
Quantifying cognitive recovery time in multi-agent systems reveals a new dimension of reliability beyond infrastructure faults.
Reliability in multi-agent systems (MAS) built on large language models is increasingly limited by cognitive failures rather than infrastructure faults. Existing observability tools describe failures but do not quantify how quickly distributed reasoning recovers once coherence is lost. We introduce MTTR-A (Mean Time-to-Recovery for Agentic Systems), a runtime reliability metric that measures cognitive recovery latency in MAS. MTTR-A adapts classical dependability theory to agentic orchestration, capturing the time required to detect reasoning drift and restore coherent operation. We further define complementary metrics, including MTBF and a normalized recovery ratio (NRR), and establish theoretical bounds linking recovery latency to long-run cognitive uptime. Using a LangGraph-based benchmark with simulated drift and reflex recovery, we empirically demonstrate measurable recovery behavior across multiple reflex strategies. This work establishes a quantitative foundation for runtime cognitive dependability in distributed agentic systems.