[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-84264-en":3,"doc-seo-84264-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},84264,1374391974564,"Clementine","https://ap-avatar.wpscdn.com/avatar/14000253aa45c000a9e?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779874745381141002",8,"Research & Report","ADE-PRF System Dynamics Approach to Health Trajectory Prediction for Multi-Agent Systems","LLM-driven multi-agent systems face a major reliability problem: conventional monitoring tracks liveness and resource usage, yet fails to quantify progressive semantic-level degradation. The ADE Predictive Reliability Framework (ADEPRF) transitions from passive degradation detection to proactive health trajectory prediction. ADEPRF unifies 20 heterogeneous runtime signals across five layers into a single Trust Margin metric, supports 8-hour forward forecasts via parallel triple-method prediction, and validates deployment using large-scale prediction records plus sandbox experiments.","arXiv :2607 .07689v 1 [ cs .MA] 8 Jul 2026  \nADE-PRF: A System Dynamics Approach to Health Trajectory Prediction for Multi-Agent Systems  \nDexing Liu  \nShanghai Qijing Digital Technology Co. , Ltd.  \nJuly 2026  \nAbstract  \nAs large language model (LLM)-driven multi-agent systems assume increasingly complex autonomous tasks in production environments, their long-term operational reliability remains a central challenge. Traditional infrastructure monitoring covers only process liveness and resource consumption, lacking quantifiable means to perceive progressive degradation at the semantic reasoning level. This paper proposes the ADE Predictive Reliability Framework (ADEPRF), building upon prior work on Channel Fracture [1], Silent Failure [2], and the ADE Stability Engineering Framework [3], enabling a transition from passive degradation detection to proactive health trajectory prediction.  \nADE-PRF makes three core contributions. First, it aggregates 20 heterogeneous runtime signals across five layers into a single Trust Margin (TM) metric, achieving system health quantification over a dynamic range of 39.2 points. Second, it enables 8-hour forward-looking forecasts via triple-method parallel prediction, with the Exponential method achieving MAE of 1 .228 points and Direction Accuracy of 76 .8%, with 99 .65% of predictions within ±10-point tolerance. Third, production deployment validation collected 380,227 predictions and 280,579 validation records across six agent profiles during 15 days of continuous operation, complemented by seven sandbox-controlled experiments.  \nKey findings reveal that in unprotected environments, significant degradation can occur while external metrics remain deceptively normal—a “false prosperity” phenomenon. Upon integration of the ADE runtime plugin, TM immediately coupled with ground-truth system states, with 16 of 20 factors directly relying on ADE-collected data. The Exponential method substantially outperformed Kalman across all evaluation windows. These results establish ADEPRF as among the earliest reliability quantification frameworks for LLM production deployment with forward-looking warning capability.  \nIntroduction  \nBackground and Motivation  \nLarge Language Model (LLM) agents are undergoing a fundamental shift—from tools to autonomous collaborators. With the introduction of mechanisms such as ReAct [16] reasoning chains, Tree of Thought, and reflective self-correction, agents are now capable of maintaining contextual coherence across multi-turn dialogues, making autonomous decisions under uncertainty, and interacting with external environments via tool invocation. This transformation reshapes the reliability boundaries of software systems.  \nHowever, long-horizon multi-agent collaboration introduces new dimensions of reliability risk absent in traditional single-turn systems. When agents execute long-horizon tasks spanning hours or even days, errors accumulate within the system in subtle ways. A minor deviation  \nin one subtask may amplify in subsequent steps. Output degradation in one agent may propagate through dependency chains to the entire system. Such anomalies remain undetected for extended periods in surface-level system metrics (e.g., task completion rate, response latency) .  \nIndustrial deployment practices reveal a common pattern: external observability metrics remain normal over extended periods, while actual output quality gradually degrades. By the time this degradation is manually detected, substantial technical debt has often already accumulated. This “breakdown between observability and actual state” constitutes the core reliability challenge in current industrial deployments.  \nA deeper challenge lies in the fact that existing software reliability engineering methodologies presuppose that system behavior changes follow interpretable causal chains—a premise broken by LLM agent systems. Model behavior exhibits intrinsic randomness and context sensitivity: identical inpu","cbCaiq7JBJYJCjh7","https://ap.wps.com/l/cbCaiq7JBJYJCjh7","pdf",4981924,5,1,117,"English","en",105,"# Background and Motivation\n## Observability Gap\n## Infrastructure Blind Spot","[{\"question\":\"Why do traditional monitoring systems struggle with LLM multi-agent reliability?\",\"answer\":\"They mainly measure process liveness and resource consumption or log I/O traces, but they cannot quantify semantic-level degradation or assess system health as it progresses over long horizons.\"},{\"question\":\"What is the Trust Margin (TM) metric in ADEPRF?\",\"answer\":\"ADEPRF aggregates 20 heterogeneous runtime signals across five layers into a single Trust Margin value to quantify system health over a dynamic range.\"},{\"question\":\"How does ADEPRF perform forward-looking health prediction and how is it validated?\",\"answer\":\"It provides 8-hour forecasts using a triple-method parallel prediction strategy, achieving reported error and direction accuracy, and validates production deployment using large volumes of predictions and validation records over 15 days plus controlled sandbox experiments.\"}]",1784194463,295,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"ade-prf-system-dynamics-approach-to-health-trajectory-prediction-for-multi-agent-systems","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/ade-prf-system-dynamics-approach-to-health-trajectory-prediction-for-multi-agent-systems/84264/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-07-28","2026-07-16",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why do traditional monitoring systems struggle with LLM multi-agent reliability?","Question",{"text":76,"@type":77},"They mainly measure process liveness and resource consumption or log I/O traces, but they cannot quantify semantic-level degradation or assess system health as it progresses over long horizons.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What is the Trust Margin (TM) metric in ADEPRF?",{"text":81,"@type":77},"ADEPRF aggregates 20 heterogeneous runtime signals across five layers into a single Trust Margin value to quantify system health over a dynamic range.",{"name":83,"@type":74,"acceptedAnswer":84},"How does ADEPRF perform forward-looking health prediction and how is it validated?",{"text":85,"@type":77},"It provides 8-hour forecasts using a triple-method parallel prediction strategy, achieving reported error and direction accuracy, and validates production deployment using large volumes of predictions and validation records over 15 days plus controlled sandbox 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