\section{Threats to Validity}\label{sec:threats}

\paragraph{Construct.} ``Silent failure'' requires a silence span with
green indicators; we applied the definition at postmortem time, and
borderline cases (loud-but-cause-free alerts, \S\ref{sec:classc}) were
included when the \emph{actionable} signal was absent. Reasonable
observers could draw the line differently for 2--3 incidents.

\paragraph{Internal.} Classification was performed by the system's two
operators (human + AI) without independent annotation; we report no
$\kappa$ and acknowledge confirmation-bias risk in mechanism assignment,
partially mitigated by the classes being load-bearing (each class drives
a real scanner whose findings are objective) rather than purely
descriptive. Postmortem quality varies with log retention; two early
incidents were reconstructed retroactively under the protocol.

\paragraph{External.} One system, one host OS, one operator pair, eight
weeks, ${\sim}40$ jobs: frequencies, shares, and latencies are
descriptive statistics of a case study, not population estimates. The
70\% user-view discovery share in particular may reflect this system's
unusually attentive operator; we present it as an existence proof that
mature automated stacks can be out-detected by a user's eye, not as a
constant.

\paragraph{Survivorship/selection.} The corpus contains only failures
that \emph{stopped} being silent. Failures silent at study end are absent
by construction; the latency distribution is therefore right-censored,
and the true tail is unknown---a point that argues for, not against, the
paper's thesis.

\paragraph{AI involvement.} The AI collaborator that co-wrote the
postmortems also co-wrote this paper; the audited numbers
(\S\ref{sec:audit}, test/check counts, dates) are mechanically derivable
from the public repository, which we offer as the primary check on
narrative bias.

\section{Conclusion}\label{sec:conclusion}

Eight weeks of complete postmortems from a production LLM agent runtime
yield a five-class, mechanism-oriented taxonomy of silent failures, of
which one class---fail-plausible chained fabrication---is specific to
systems that speak. The quantified record is uncomfortable in useful
ways: a defense stack of thousands of tests and hundreds of declarative
checks prevented \emph{none} of the novel incidents ex ante (while
blocking 87\% from recurring); the best detector was a human reading the
product; and the longest failures lived not in complex code but in the
seams between simple, correct parts. The constructive program that
follows---postmortems as causal chains, lessons as meta-rules, meta-rules
as scanners, guards proven by sabotage, declared state converged by
machine, observers kept read-only, and complexity actively retired---is
less a framework than a discipline, and it is the discipline, not the
specific artifacts, that we believe transfers.

The failure your agent system should frighten you with is not the crash.
It is the confident paragraph, on schedule, in perfect grammar, about a
crisis that does not exist---pushed to a user who has every reason to
believe it, by a pipeline in which every component worked.

\section*{Artifact Availability}

All 22 incident postmortems, the canonical failure-mode catalog, the
governance ontology (90~invariants~/ 827~checks~/ 23~meta-rules), the 14
mechanized scanners, and the full test suite are public in the system
repository. The governance engine is additionally published as a
standalone, project-agnostic package on PyPI
(\texttt{openclaw-ontology-engine}, v0.1.0): any agent-runtime project
can adopt the invariant/meta-rule/scanner framework with its own YAML
configuration. A data inventory mapping every number in this paper to its
repository source accompanies the draft.

\section*{Acknowledgments}

The author thanks Claude (Anthropic) for its role as AI engineering
collaborator throughout the system's development, the incident
postmortems, and the preparation of this manuscript, as disclosed on the
title page; and the OpenClaw open-source community, whose framework the
system builds upon.
