\begin{abstract}
Between 2024 and 2026 the Internet Engineering Task Force (IETF) saw a sharp
rise in Internet-Drafts addressing AI agents and autonomous systems: monthly
submissions in our corpus grew from an average of 3.7 (June 2024--May 2025) to
38.8 (since June 2025), peaking at 106 in March 2026---roughly $35\times$ a
typical 2024 month. We present a quantitative survey of this emerging area
based on a curated corpus of 524 AI/agent-related IETF Internet-Drafts spanning
January 2024 to May 2026. We characterise the corpus along four axes: temporal
submission dynamics, thematic category distribution, authorship and
working-group structure, and semantic redundancy measured through text
embeddings. Two findings stand out: the area is overwhelmingly
\emph{pre-standardization}---87\% of drafts are individual submissions not
adopted by any working group---and it is semantically dense, with 32\% of
drafts having a near-duplicate (cosine $>0.9$) elsewhere in the corpus.
Because the thematic categories are produced by an LLM-assisted pipeline, we
explicitly quantify their reliability through a two-model re-rating experiment:
categorical assignment is substantially reproducible (Cohen's $\kappa = 0.65$),
whereas LLM ordinal \emph{quality} scores such as novelty and overlap are not
($\kappa_w = 0.13$--$0.21$). We therefore report the category landscape but
deliberately exclude quality scores from our findings, and we argue this
distinction is a general caution for the growing practice of LLM-assisted
corpus analysis. All data, queries, and rating artifacts are released for
reproduction.
\end{abstract}
