% =====================================================================
% When Errors Become Narratives: A Longitudinal Taxonomy of Silent
% Failures in a Production LLM Agent Runtime
%
% arXiv preprint (cs.SE; cross-list cs.AI, cs.DC)
% Build: pdflatex main.tex  (run twice for cross-references)
% No bibtex needed: bibliography is inline (thebibliography).
% All packages are standard arXiv/TeXLive. No external figure files:
% all 5 figures are TikZ, compiled in-document.
%
% Author: Wei Wu <wuweinanonuaa@gmail.com>, Independent researcher
% =====================================================================
\documentclass[11pt]{article}

\usepackage[margin=1in]{geometry}
\usepackage{amsmath,amssymb}
\usepackage{graphicx}
\usepackage{xcolor}
\usepackage{booktabs}
\usepackage{array}
\usepackage{enumitem}
\usepackage{microtype}
\usepackage{tikz}
\usetikzlibrary{arrows.meta,positioning,shapes.geometric,calc}
\usepackage[colorlinks=true,linkcolor=blue!60!black,citecolor=blue!60!black,urlcolor=blue!60!black]{hyperref}

% --- compact, readable lists -----------------------------------------
\setlist{itemsep=2pt,topsep=4pt,parsep=0pt}

% --- semantic macros ---------------------------------------------------
\newcommand{\failplausible}{\emph{fail-plausible}}
\newcommand{\classref}[1]{Class~#1}
\newcommand{\sysname}{\texttt{openclaw-model-bridge}}

% --- TikZ styles shared by all figures --------------------------------
\tikzset{
  box/.style={draw, rounded corners=2pt, align=center, font=\small,
              inner sep=4pt, minimum height=18pt},
  stage/.style={box, fill=gray!8},
  bad/.style={box, fill=red!8, draw=red!60!black},
  amp/.style={box, fill=orange!12, draw=orange!70!black},
  con/.style={box, fill=yellow!15, draw=yellow!50!black},
  good/.style={box, fill=green!8, draw=green!50!black},
  flow/.style={-{Stealth[length=2.2mm]}, thick},
  lbl/.style={font=\scriptsize\itshape, align=left},
}

\title{\textbf{When Errors Become Narratives: A Longitudinal Taxonomy of
Silent Failures in a Production LLM Agent Runtime}}

\author{%
  Wei Wu\thanks{Contact: wuweinanonuaa@gmail.com. \quad
  \textbf{Contributions \& AI disclosure:} the author designed, operates,
  and owns the system under study, provided domain expertise and all
  research direction, and made all editorial decisions; Claude
  (Anthropic), used through a coding-agent interface, served as AI
  engineering collaborator --- co-implementing the system and its defense
  framework, co-writing the incident postmortems under the author's
  protocol, preparing the quantitative inventory, and drafting this
  manuscript under the author's supervision. The arrangement is itself
  examined as a finding in \S\ref{sec:discussion}. All numbers are
  mechanically traceable to the public repository.}\\
  \small Independent researcher}

\date{June 2026 ~$\cdot$~ Draft v0.3 (LaTeX)}

\begin{document}
\maketitle

\begin{abstract}
\noindent
Large language model (LLM) agent systems are increasingly deployed as
long-running, autonomous runtimes---orchestrating scheduled jobs, calling
tools, maintaining memory, and pushing results to humans over messaging
channels. We present a longitudinal empirical study of \emph{silent
failures} in one such system: a personal-assistant agent runtime in
continuous production since March 2026, comprising roughly 40 scheduled
jobs, 8 LLM providers, a tool-governance proxy, and a knowledge-base
memory plane, defended by 4{,}286 unit tests and 827 declarative
governance checks. Over an eight-week window we documented 22 incidents
with full root-cause postmortems, within which a single
meta-pattern---\emph{a failure whose error signal never reaches a human
in actionable form}---manifested at least 28 times.

From these postmortems we derive a five-class, mechanism-oriented
taxonomy of silent failure: (A)~environment and platform quirks,
(B)~design-assumption mismatches, (C)~error swallowing and dilution,
(D)~chained hallucination and fabrication, and (E)~operational omission
and forensic blind spots. \classref{D} is, to our knowledge, specific to
LLM-based systems and the most dangerous: the system does not merely fail
to report an error---the LLM actively \emph{transforms} the error into
fluent, plausible narrative content delivered to the user. We term this
behavior \failplausible{}, and position it as the LLM-era escalation of
\emph{gray failure}'s differential observability: the observer is not
just blind, it is being convincingly lied to by the failure itself.

Three cross-cutting findings challenge common assumptions about agent
reliability engineering. First, discovery channel: roughly 70\% of silent
failures were ultimately caught by \emph{human user-view observation} of
system output, not by unit tests, health checks, or governance
audits---all of which stayed green through most incidents. Second, a
retrospective audit of 15 incidents found our declarative governance
layer had a \textbf{0\% ex-ante prevention rate but an 87\% ex-post
regression-blocking rate}---audits are regression engines, not prediction
engines. Third, incident latency (13 hours to 60 days of silence)
correlates with failure mechanism, not code complexity: the longest-lived
failures lived in the \emph{seams} between components---deployment
topology, cross-script contracts, observer--observed coupling---where, by
construction, no test runs. We describe the defense framework that
emerged (meta-rules, mechanized scanners, sabotage-validated invariants,
a declared-state convergence engine, and layered anti-fabrication
guards), and distill design principles for engineering LLM agent systems
whose failures are loud, attributable, and boring. All 22 postmortems,
the governance engine, and the defense framework are publicly available.
\end{abstract}

\input{sec1_intro}
\input{sec2_related}
\input{sec3_method}
\input{sec4_taxonomy}
\input{sec5_findings}
\input{sec6_defense}
\input{sec7_discussion}
\input{sec8_9_closing}

% =====================================================================
\begin{thebibliography}{14}\small

\bibitem{huang2017gray}
P.~Huang, C.~Guo, L.~Zhou, J.~R. Lorch, Y.~Dang, M.~Chintalapati, and
R.~Yao.
\newblock Gray failure: The Achilles' heel of cloud-scale systems.
\newblock In \emph{Proc.\ HotOS}, 2017.

\bibitem{gunawi2018failslow}
H.~S. Gunawi et~al.
\newblock Fail-slow at scale: Evidence of hardware performance faults in
  large production systems.
\newblock In \emph{Proc.\ USENIX FAST}, 2018. Extended in \emph{ACM
  Trans.\ Storage} 14(3), 2018.

\bibitem{cemri2025mast}
M.~Cemri, M.~Z. Pan, S.~Yang, et~al.
\newblock Why do multi-agent {LLM} systems fail?
\newblock arXiv:2503.13657, 2025.

\bibitem{ranganathan2025inference}
B.~Ranganathan, M.~Zhang, and K.~Wu.
\newblock Enhancing reliability in {AI} inference services: An empirical
  study on real production incidents.
\newblock arXiv:2511.07424, 2025.

\bibitem{zhou2025shielda}
J.~Zhou, J.~Chen, Q.~Lu, D.~Zhao, and L.~Zhu.
\newblock {SHIELDA}: Structured handling of exceptions in {LLM}-driven
  agentic workflows.
\newblock arXiv:2508.07935, 2025.

\bibitem{xiao2026air}
Z.~Xiao, J.~Sun, and J.~Chen.
\newblock {AIR}: Improving agent safety through incident response.
\newblock arXiv:2602.11749, 2026.

\bibitem{taraghi2026mcp}
M.~Taraghi, M.~M. Morovati, and F.~Khomh.
\newblock Real faults in {Model Context Protocol} ({MCP}) software: A
  comprehensive taxonomy.
\newblock arXiv:2603.05637, 2026.

\bibitem{beyer2016sre}
B.~Beyer, C.~Jones, J.~Petoff, and N.~R. Murphy, editors.
\newblock \emph{Site Reliability Engineering: How Google Runs Production
  Systems}.
\newblock O'Reilly, 2016.

\bibitem{ezell2025incident}
C.~Ezell, X.~Roberts-Gaal, and A.~Chan.
\newblock Incident analysis for {AI} agents.
\newblock In \emph{Proc.\ AAAI/ACM AIES}, 2025. arXiv:2508.14231.

\bibitem{owotogbe2026mcpruntime}
J.~Owotogbe, I.~Kumara, W.-J. van~den Heuvel, D.~A. Tamburri, A.~K.
  Iannillo, and R.~Natella.
\newblock A taxonomy of runtime faults in {Model Context Protocol}
  servers.
\newblock arXiv:2606.05339, 2026.

\bibitem{huang2023hallucination}
L.~Huang, W.~Yu, W.~Ma, W.~Zhong, Z.~Feng, H.~Wang, Q.~Chen, W.~Peng,
  X.~Feng, B.~Qin, and T.~Liu.
\newblock A survey on hallucination in large language models: Principles,
  taxonomy, challenges, and open questions.
\newblock arXiv:2311.05232, 2023 (rev.\ 2024).

\bibitem{basiri2016chaos}
A.~Basiri, N.~Behnam, R.~de~Rooij, L.~Hochstein, L.~Kosewski,
  J.~Reynolds, and C.~Rosenthal.
\newblock Chaos engineering.
\newblock \emph{IEEE Software}, 33(3):35--41, 2016.

\bibitem{ghosh2022fight}
S.~Ghosh, M.~Shetty, C.~Bansal, and S.~Nath.
\newblock How to fight production incidents? {An} empirical study on a
  large-scale cloud service.
\newblock In \emph{Proc.\ ACM SoCC}, 2022. Best paper.

\bibitem{zheng2023judge}
L.~Zheng, W.-L. Chiang, Y.~Sheng, S.~Zhuang, Z.~Wu, Y.~Zhuang, Z.~Lin,
  Z.~Li, D.~Li, E.~P. Xing, H.~Zhang, J.~E. Gonzalez, and I.~Stoica.
\newblock Judging {LLM}-as-a-judge with {MT-Bench} and {Chatbot Arena}.
\newblock In \emph{Proc.\ NeurIPS}, 2023. arXiv:2306.05685.

\end{thebibliography}

\end{document}
