\section{Introduction}
\label{sec:intro}

Standardization activity around AI agents and autonomous systems has surged at
the Internet Engineering Task Force (IETF). In the corpus studied here, monthly
submissions of AI/agent-related Internet-Drafts grew from an average of $3.7$
per month over June 2024--May 2025 to $38.8$ per month since June 2025, peaking
at $106$ drafts in March 2026---roughly $35\times$ a typical 2024 month. A surge
of this size, concentrated in barely a year, makes the area difficult to
navigate: there is no settled taxonomy, the boundaries between proposals are
fluid, and it is unclear how much of the activity represents distinct technical
work versus overlapping attempts at the same problems.

A natural response is to characterise the space quantitatively, and
LLM-assisted corpus analyses are an increasingly common instrument for doing
so---using a large language model to classify documents, score them, and
summarise trends at a scale that manual reading cannot reach
\cite{tan2024survey,gilardi2023chatgpt}. Such studies, however, rarely report whether the labels they rely on
are reproducible. An LLM applied to the same documents under a different model,
or even a different run, may produce different categories and scores; without a
reliability check, it is impossible to tell which conclusions rest on stable
signal and which on rater noise.

To our knowledge no verified quantitative survey of the AI-agent
standardization space at the IETF exists. We address this gap with a
descriptive survey that is explicit about which of its measurements can be
trusted. This paper is deliberately neutral: it characterises the landscape
rather than advocating for any particular proposal or design.

\paragraph{Contributions.}
\begin{itemize}[leftmargin=1.4em,topsep=2pt,itemsep=1pt]
  \item A curated corpus of \textbf{524} AI/agent-related IETF Internet-Drafts
        spanning January 2024 to May 2026, constructed by keyword candidate
        selection followed by explicit false-positive filtering
        (Section~\ref{sec:method}).
  \item A characterization of the corpus along four axes---temporal submission
        dynamics, thematic category distribution, authorship and working-group
        structure, and semantic redundancy measured through text embeddings
        (Section~\ref{sec:landscape}).
  \item An explicit two-model inter-rater reliability check that separates the
        labels we can trust from those we cannot: categorical assignment is
        substantially reproducible (Cohen's $\kappa \approx 0.65$), whereas the
        LLM ordinal \emph{quality} scores are not ($\kappa_w = 0.13$--$0.21$ for
        the least stable dimensions). We report the former and exclude the
        latter (Section~\ref{sec:reliability}).
  \item A full release of the data, queries, and rating artifacts used in the
        analysis, so that every reported number can be recomputed.
\end{itemize}

\paragraph{Roadmap.}
Section~\ref{sec:method} describes corpus construction, the LLM-assisted
classification pipeline, and the embedding setup.
Section~\ref{sec:landscape} presents the landscape across the four axes:
temporal dynamics (Section~\ref{sec:temporal}), category distribution
(Section~\ref{sec:categories}), authorship and working-group structure
(Section~\ref{sec:authors}), and semantic redundancy
(Section~\ref{sec:redundancy}).
Section~\ref{sec:reliability} reports the two-model reliability experiment that
underwrites the category labels and disqualifies the quality scores.
Section~\ref{sec:discussion} discusses implications and limitations, and
Section~\ref{sec:conclusion} concludes with directions for future work.