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

2025-11-08

AI Summary

Quantifying cognitive recovery delay after reasoning inconsistency in multi-agent systems (MAS) remains an open challenge. Method: We propose MTTR-A (Mean Time to Cognitive Recovery), a novel cognitive-domain reliability metric, systematically extending classical reliability concepts (e.g., MTTR, MTBF) to distributed reasoning recovery. Using the AG-News corpus and LangGraph framework, we construct a reflection-pattern simulation benchmark to compare automated reflection versus human-in-the-loop approval in recovery dynamics. Contribution/Results: Automated reflection achieves stable recovery within 6 seconds on average; MTTR-A is 6.21±2.14 s (median), MTBF is 6.7±2.14 s, and normalized recovery rate (NRR) reaches 0.08. This work establishes a measurable, comparable, and reproducible evaluation paradigm for cognitive recovery—providing both theoretical foundations and empirical benchmarks for MAS resilience modeling.

Technology Category

• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning
• Multiagent Systems: Agreement, Argumentation & Negotiation
• Knowledge Representation and Reasoning: Computational Complexity of Reasoning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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

Problem

• Measuring cognitive recovery latency in multi-agent systems
• Quantifying reasoning coherence restoration after drift
• Establishing runtime dependability for agentic cognition

Innovation

• Adapts classical reliability metrics to cognitive domain
• Quantifies reasoning recovery latency via MTTR-A metric
• Establishes runtime dependability foundation for agentic cognition