MTTR-A: Measuring Cognitive Recovery Latency in Multi-Agent Systems
A reliability metric for measuring how quickly multi-agent AI systems recover from cognitive drift and restore coherent operation.
Abstract
Reliability failures in LLM-based multi-agent systems are often cognitive rather than infrastructural: agents may keep running while reasoning drifts, memories desynchronize, or coordination degrades. This paper introduces MTTR-A, Mean Time-to-Recovery for Agentic Systems, as a runtime reliability metric for measuring cognitive recovery latency in multi-agent orchestration. The framework adapts classical dependability metrics such as MTTR and MTBF to distributed reasoning, defines a normalized recovery ratio (NRR), and links recovery latency to long-run cognitive uptime. A LangGraph-based benchmark with simulated reasoning drift and reflex recovery evaluates recovery behavior across multiple reflex strategies, establishing a practical foundation for quantitative runtime dependability in agentic AI systems.
Key Innovations
MTTR-A for cognitive recovery
Adapts Mean Time To Recovery from infrastructure reliability to agentic reasoning, orchestration, and runtime cognition.
Reflex taxonomy and telemetry loop
Defines recovery, human-in-the-loop, runtime control, coordination, and safety reflexes connected to telemetry and policy hooks.
NRR and cognitive uptime bounds
Combines MTBF, MTTR-A, normalized recovery ratio, and reliability theorems to estimate long-run cognitive uptime.