\section{Discussion: Seams, Not Components}\label{sec:discussion}

Two months in, facing a week with five incidents, we asked the obvious
question: \emph{is the system failing because it has become too complex?}
The postmortem-backed answer was more specific and, we believe, more
useful: \textbf{no individual failing part was complex.} A symlink. An
\texttt{abspath} call. A one-line registry entry. A boolean default.
Every part was simple and locally correct; every incident lived in a
\emph{combination}---symlink $\times$ non-resolving path API $\times$ a
particular invocation route; sync-enabled audit $\times$ bare-name entry
$\times$ format mismatch $\times$ non-dry-run path. Components are
covered by 4{,}286 tests; \textbf{combinations grow superlinearly and
tests cover only the combinations someone imagined.} Complexity is about
parts; incidents are about seams.

This reframing has bite because it inverts the instinctive response to
incidents---\emph{add a guard}---which itself adds a part, and therefore
seams. (Our convergence engine, built to close the declared-vs-runtime
seam, opened an observer-mutates-observed seam that produced three
incidents before being caged.) The stable posture we landed on, codified
as a standing ``Sunset Law'' with two operational meta-rules, is: before
adding any mechanism, attempt to \emph{retire} an equivalent one; one
logical entity must have one physical representation (multiple
representations \emph{will} drift, and the bug lives between them); and
defenses themselves are incident surfaces---prefer \textbf{seam
reduction} (unify representations, shrink the dev--production gap, make
observers read-only) over defense accretion. In the five weeks since
adoption, the system retired more representation duplicates than it added
invariants---and we note, with appropriate caution about confounds, that
incident frequency declined while feature velocity did not.

\paragraph{On the AI-assisted operation of an AI system.}
This system is developed and operated by one human domain expert working
with an AI engineering collaborator, governing a runtime powered by
\emph{other} LLMs. Several findings above are entangled with that
arrangement in both directions. The postmortem protocol's rigidity
(mandatory causal-chain diagram before any fix; the three-question rule
before touching code) exists substantially \emph{because} an AI
collaborator will otherwise pattern-match symptoms to plausible fixes
with great speed---industrializing the trigger-level cosmetic fix
(\S\ref{sec:tac}); one documented incident chain consisted of five
cascading ``fixes'' to a problem whose correct resolution was a single
file copy. Conversely, the framework's volume (90 invariants, 14
scanners, 4{,}286 tests, 22 publication-grade postmortems in eight weeks)
is achievable for a single human \emph{only} with such a collaborator. We
offer the observation, not a verdict: AI collaboration shifts the binding
constraint of reliability engineering from implementation bandwidth to
\textbf{judgment discipline}---exactly the property the meta-rules exist
to encode. The governance framework is, in this reading, not just the
system's immune system but the collaboration's contract.

\paragraph{Generalizability.}
We claim the \emph{taxonomy classes and cross-cutting structures}
(latency $\propto$ observational distance; trigger/amplifier/concealer;
fix$\rightarrow$rule$\rightarrow$scanner maturation;
audit-as-regression) generalize to LLM agent systems with long-running
scheduled autonomy and human-facing output---the mechanisms are not
artifacts of our stack, and Classes A/C/E have direct ancestry in systems
known to fail this way at every scale. We explicitly do not claim the
\emph{frequencies} generalize (single system, single operator pair, one
OS), nor that \classref{D} frequencies transfer to systems without
synthesis-and-push pipelines. What we most hope transfers is the method:
complete causal-chain postmortems, a mechanism-oriented catalog treated
as the unit of institutional memory, and sabotage-validated conversion of
every lesson into a machine check.
