Agent Delivery Engineering Predictive Reliability Framework

---
name: agent-delivery-engineering-predictive-reliability-framework
description: 'Long-horizon LLM multi-agent systems face reliability risks invisible to infrastructure monitoring. We propose the ADE Predictive Reliability Framework (ADE-PRF), enabling proactive health trajectory. Based on arXiv:2607.07689.'
---

# Agent Delivery Engineering Predictive Reliability Framework

**arXiv**: 2607.07689 | **Authors**: Dexing Liu | **Utility**: 0.85

## Overview

Long-horizon LLM multi-agent systems face reliability risks invisible to infrastructure monitoring. We propose the ADE Predictive Reliability Framework (ADE-PRF), enabling proactive health trajectory prediction from passive degradation detection. ADE-PRF aggregates 20 heterogeneous signals across five layers into a Trust Margin (TM) metric (39.2-point dynamic range). Triple-method parallel prediction enables 8-hour forecasts: the Exponential method achieves MAE=1.228, Direction Accuracy=76.8%, with 99.65% within +/-10-point tolerance. Production validation spans 380,227 predictions and 280,579 validations across six agent profiles over 15 continuous days, plus seven sandbox-controlled experiments. Key findings include detection of "false prosperity" -- degradation concealed by normal surface metrics -- and immediate TM coupling with ground-truth states upon ADE plugin integration, with 16/20 factors relying on ADE-collected data. Exponential consistently outperforms Kalman. ADE-PRF provides among the earliest reliability quantification with forward-looking warnings for production LLM agents.

## Key Contributions

1. Long-horizon LLM multi-agent systems face reliability risks invisible to infrastructure monitoring.
2. We propose the ADE Predictive Reliability Framework (ADE-PRF), enabling proactive health trajectory prediction from passive degradation detection.
3. ADE-PRF aggregates 20 heterogeneous signals across five layers into a Trust Margin (TM) metric (39.2-point dynamic range).
4. Triple-method parallel prediction enables 8-hour forecasts: the Exponential method achieves MAE=1.228, Direction Accuracy=76.8%, with 99.65% within +/-10-point tolerance.

## Implementation Notes

- **Keywords**: multi-agent, llm
- **Categories**: cs.MA
- **Published**: 2026-07-08

## Activation Criteria

Use this skill when working on tasks involving: multi-agent, llm.