A five-layer, 20-signal Trust Margin metric and Exponential-smoothing prediction engine achieve 8-hour-ahead degradation forecasting for production LLM agent systems with MAE=1.228 and 76.8% direction accuracy.
Intelligence Entropy Principle and the ADE Stability Engineering Framework
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abstract
As LLM-driven multi-agent systems (MAS) transition from lab to production, system behavior exhibits nonlinear degradation. We introduce the Intelligence Entropy Principle: probability-driven systems spontaneously drift toward disorder, formalized as S(t) = S0 * exp(alpha*t/Cm), where Cm is a model capability coefficient we propose. Lyapunov analysis yields the stabilization condition lambda > alpha/Cm. We construct the ADE (Agent Delivery Engineering) four-layer framework (L1 Physical Laws through L4 User Adaptation) with 23 core components. Validation spans 100K-scale experiments and 33.6 days of production monitoring. We propose a Five-Layer Disorder Taxonomy unifying failures under structural collapse, and present Elastic Organization as an original MAS morphology. Results: channel fracture reduced from 69-98% to near 0%; system death probability below 0.02%.
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Agent Delivery Engineering Predictive Reliability Framework
A five-layer, 20-signal Trust Margin metric and Exponential-smoothing prediction engine achieve 8-hour-ahead degradation forecasting for production LLM agent systems with MAE=1.228 and 76.8% direction accuracy.