\section{Conclusion and Future Work}
\label{sec:conclusion}

We have presented a verified quantitative survey of AI/agent-related IETF
Internet-Drafts, based on a curated corpus of $524$ documents spanning January
2024 to May 2026. The corpus exhibits a sharp recent surge---from $3.7$ to
$38.8$ drafts per month, peaking at $106$ in March 2026---and is dominated by
individual submissions ($87\%$ not adopted by any working group) with
substantial semantic redundancy ($32.4\%$ of drafts having a near-duplicate),
the signature of a young, pre-standardization design space. Crucially, we
treated the LLM-assisted labels themselves as objects of measurement: a
two-model re-rating shows that categorical assignment is substantially
reproducible (Cohen's $\kappa \approx 0.65$), while ordinal quality scores are
not ($\kappa_w = 0.13$--$0.21$ for the least stable dimensions), so we report
the category landscape and exclude the quality scores.

Several directions extend this work. \emph{Full-text classification} would
replace the abstract-only pipeline and test whether categories shift when the
classifier reads the complete document. \emph{Longitudinal re-runs} on later
snapshots would turn the single-snapshot picture into a moving record of how the
surge evolves and whether redundancy consolidates over time. \emph{Cross-SDO
extension} to ISO, ITU, ETSI, NIST, and W3C---contingent on reconciling their
heterogeneous metadata---would situate the IETF activity within the broader
standards landscape. Finally, \emph{tracking working-group adoption} of
individual drafts would reveal which of the many competing proposals clear the
bar from individual submission to adopted work, giving an empirical handle on
how the pre-standardization field resolves.