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% ── Title ─────────────────────────────────────────────────────────────────

\title{%
    \textbf{The AI Agent Standards Gold Rush:\\
    A Systematic Analysis of 474 IETF Internet-Drafts}%
}

\author{
    % TODO: Add your name, affiliation, and ORCID
    [Author Name]\\
    \texttt{[email]}
}

\date{March 2026}

\begin{document}
\maketitle

% ── Abstract ──────────────────────────────────────────────────────────────

\begin{abstract}
The Internet Engineering Task Force (IETF) is experiencing an unprecedented surge in standardization activity related to artificial intelligence and autonomous agents. We present the first systematic quantitative survey of this landscape, analyzing 474 Internet-Drafts from 710 authors across approximately 280 organizations submitted between 2024 and early 2026. After false-positive filtering (113 drafts excluded as not AI-relevant), 361 drafts form the core analytical corpus. Using a hybrid LLM-assisted pipeline---Anthropic Claude for multi-dimensional rating and idea extraction, Ollama/nomic-embed-text for semantic embedding and similarity analysis---we assess each draft on five dimensions (novelty, maturity, overlap, momentum, relevance), extract 462 deduplicated technical ideas (from approximately 1,780 raw extractions), identify 12 standardization gaps (2 critical), and map the co-authorship network. Cross-organizational convergence analysis reveals 132 ideas (33\% convergence rate) where independent teams arrived at similar solutions. Our analysis reveals three headline findings: (1) a safety deficit averaging 4:1 (varying 1.5:1 to 21:1 month-to-month) of capability-building drafts to safety-focused ones; (2) significant thematic redundancy, with 42 overlap clusters and 155 competing agent-to-agent protocol proposals; and (3) concentrated organizational authorship, with a single company contributing approximately 16\% of all drafts. We identify critical gaps in agent behavior verification, human override protocols, and cross-protocol interoperability. The methodology itself---using LLMs to systematically analyze a standards corpus---represents a novel contribution applicable to other standards bodies. Our open-source toolkit and dataset are released under the MIT license for reproducibility.
\end{abstract}

\noindent\textbf{Keywords:} IETF, Internet-Drafts, AI agents, standardization, protocol analysis, LLM-assisted analysis, embedding similarity, safety deficit, author networks

% ── 1. Introduction ──────────────────────────────────────────────────────

\section{Introduction}

The rapid deployment of large language models (LLMs) and autonomous AI agents has created urgent demand for interoperability standards. Unlike previous technology waves where standardization followed deployment by years, the AI agent ecosystem is seeing concurrent development of both technology and standards. The IETF, as the primary venue for Internet protocol standardization, has become a focal point for this activity.

The acceleration is dramatic. In 2024, just 9 AI/agent-related Internet-Drafts were submitted to the IETF---0.5\% of all submissions. By Q1 2026, AI/agent drafts account for 9.3\% of all new Internet-Drafts: nearly 1 in 10. This ``gold rush'' spans diverse topics including agent-to-agent (A2A) communication protocols, identity and authentication frameworks, discovery mechanisms, safety guardrails, and data format interoperability.

However, the speed and volume of this activity raises important questions:
\begin{itemize}[nosep]
    \item How much of this activity is novel versus duplicative?
    \item Which organizations and individuals are driving standardization?
    \item Are critical areas (e.g., AI safety) receiving proportional attention?
    \item What gaps exist in the current proposal landscape?
\end{itemize}

To answer these questions, we built an automated analysis pipeline that:
\begin{enumerate}[nosep]
    \item Harvests draft metadata and full text from the IETF Datatracker API (474 drafts, 710 authors), with false-positive filtering reducing the analytical corpus to 361 relevant drafts.
    \item Rates each draft on five dimensions---novelty, maturity, overlap, momentum, and relevance---using LLM-assisted analysis (Anthropic Claude), with scores clamped to 1--5 and validated for bounds.
    \item Generates semantic embeddings (Ollama/nomic-embed-text) and computes pairwise cosine similarity across all $\binom{474}{2} = 112{,}101$ draft pairs.
    \item Extracts approximately 1,780 raw technical ideas, deduplicated via SequenceMatcher (threshold 0.75) to 462 unique ideas classified into six primary types.
    \item Identifies 12 standardization gaps through systematic comparison of coverage.
    \item Maps the co-authorship network and organizational affiliations across 710 contributors.
    \item Performs cross-organizational convergence analysis, finding 132 ideas (33\%) independently proposed by multiple organizations.
\end{enumerate}

\noindent Our contributions are:
\begin{itemize}[nosep]
    \item \textbf{First systematic survey} of AI/agent-related IETF drafts at scale, covering 474 drafts (361 after false-positive filtering).
    \item \textbf{Quantitative evidence of a safety deficit}: averaging 4:1 capability-building to safety proposals (varying 1.5:1 to 21:1 month-to-month).
    \item \textbf{Gap analysis} identifying 12 underserved areas, including 2 critical gaps with near-zero coverage.
    \item \textbf{Cross-organizational convergence analysis}: 132 ideas independently proposed by multiple organizations, indicating implicit community consensus.
    \item \textbf{Reproducible LLM-assisted methodology} combining Claude-based rating with embedding-based similarity, applicable to other standards corpora.
    \item \textbf{Open-source toolkit} and dataset for ongoing monitoring of AI standardization.
\end{itemize}

% ── 2. Background and Related Work ──────────────────────────────────────

\section{Background and Related Work}

\subsection{IETF Standardization Process}

The IETF develops Internet standards through an open, consensus-based process~\citep{rfc2026}. Internet-Drafts (I-Ds) are the primary input: working documents that may evolve into Requests for Comments (RFCs) or expire without adoption. The Datatracker system\footnote{\url{https://datatracker.ietf.org}} provides programmatic API access to draft metadata, author information, and lifecycle states. I-Ds have a six-month expiry and can be submitted by any individual or working group.

\subsection{AI Agent Standardization Landscape}

Several parallel efforts address AI agent interoperability. Google's Agent-to-Agent (A2A) protocol~\citep{a2a2025} defines a framework for agent discovery and task execution. Anthropic's Model Context Protocol (MCP)~\citep{mcp2025} specifies how LLMs connect to external tools and data sources. Within the IETF, the newly formed AIPREF working group addresses AI content usage preferences, while proposals span identity (OAuth extensions, agentic JWTs), discovery (agent URIs, DNS-based registration), communication protocols (over QUIC, SIP, HTTP), and safety frameworks (accountability protocols, verifiable conversations).

\subsection{Automated Analysis of Standards Documents}

Prior work on automated standards analysis has focused on RFC evolution~\citep{arkko2019}, IETF participation patterns~\citep{simmons2019}, and working group dynamics. Bibliometric studies of standards bodies~\citep{baron2019} have examined citation networks and organizational influence. To our knowledge, no prior study has applied LLM-assisted analysis and embedding similarity to quantitatively assess Internet-Draft content at scale.

\subsection{LLM-Assisted Document Analysis}

Recent work demonstrates the effectiveness of LLMs for document classification~\citep{brown2020}, technical summarization, and multi-dimensional assessment. The use of LLMs as ``judges'' for evaluating text quality has gained traction in NLP research~\citep{zheng2023}. We extend this paradigm by combining LLM-based rating with local embedding models for similarity computation, providing both semantic understanding and quantitative comparability across a large technical corpus.

% ── 3. Methodology ──────────────────────────────────────────────────────

\section{Methodology}

Figure~\ref{fig:pipeline} illustrates our five-stage analysis pipeline. Each stage is described below.

\begin{figure}[H]
    \centering
    \fbox{\parbox{0.9\textwidth}{\centering
    \textbf{Pipeline Overview}\\[6pt]
    \texttt{Fetch} $\rightarrow$ \texttt{Analyze/Rate} $\rightarrow$ \texttt{Embed} $\rightarrow$ \texttt{Extract Ideas} $\rightarrow$ \texttt{Find Gaps}\\[4pt]
    {\small Datatracker API \quad Claude (Sonnet 4) \quad Ollama/nomic-embed-text \quad Claude \quad Claude}
    }}
    \caption{Five-stage analysis pipeline. All intermediate results are cached in SQLite for reproducibility.}
    \label{fig:pipeline}
\end{figure}

\subsection{Data Collection}

We queried the IETF Datatracker API v1\footnote{\url{https://datatracker.ietf.org/api/v1/doc/document/}} using twelve seed keywords: \texttt{agent}, \texttt{ai-agent}, \texttt{llm}, \texttt{autonomous}, \texttt{machine-learning}, \texttt{artificial-intelligence}, \texttt{mcp}, \texttt{agentic}, \texttt{inference}, \texttt{generative}, \texttt{intelligent}, and \texttt{aipref}. Keywords were matched against both draft names (\texttt{name\_\_contains}) and abstracts (\texttt{abstract\_\_contains}). For each matching draft (type \texttt{draft}), we retrieved:
\begin{itemize}[nosep]
    \item Metadata: title, abstract, submission date, revision number, page count, working group, states
    \item Full text: downloaded from \texttt{ietf.org/archive/id/\{name\}-\{rev\}.txt}
    \item Author information: via the \texttt{documentauthor} and \texttt{person} API endpoints
\end{itemize}
All data was stored in a SQLite database with FTS5 full-text search indexing, enabling efficient querying across the corpus.

\subsection{LLM-Assisted Rating}

Each draft was assessed using Anthropic Claude (Sonnet 4) on five dimensions, each scored 1--5:

\begin{itemize}[nosep]
    \item \textbf{Novelty}: Originality of the proposed approach relative to existing standards and other drafts.
    \item \textbf{Maturity}: Completeness of specification (protocol details, data formats, security considerations).
    \item \textbf{Overlap}: Degree of redundancy with other drafts in the corpus.
    \item \textbf{Momentum}: Evidence of community engagement (revisions, working group adoption, co-authors).
    \item \textbf{Relevance}: Importance to the AI/agent ecosystem specifically.
\end{itemize}

\noindent The prompt provided each draft's abstract and, where available, the first 4,000 characters of full text. Responses were cached by prompt SHA-256 hash to ensure reproducibility. A composite score was computed as:
\begin{equation}
    S = 0.30 \cdot \text{novelty} + 0.25 \cdot \text{relevance} + 0.20 \cdot \text{maturity} + 0.15 \cdot \text{momentum} + 0.10 \cdot (6 - \text{overlap})
\end{equation}

\noindent The weighting prioritizes novelty and relevance while penalizing overlap (inverted, so less overlap yields higher scores). We validated robustness by testing alternative weighting schemes (Section~\ref{app:sensitivity}).

\subsection{Embedding and Similarity Analysis}

We generated 768-dimensional embeddings for each draft using Ollama with the \texttt{nomic-embed-text} model, encoding a combination of title, abstract, and the first 4,000 characters of full text. Pairwise cosine similarity was computed across all $\binom{474}{2} = 112{,}101$ draft pairs:
\begin{equation}
    \text{sim}(a, b) = \frac{\mathbf{v}_a \cdot \mathbf{v}_b}{\|\mathbf{v}_a\| \cdot \|\mathbf{v}_b\|}
\end{equation}

\noindent Greedy clustering at thresholds of 0.85 and 0.90 identified groups of near-duplicate and highly similar drafts. Hierarchical clustering (Ward's method) was applied to the distance matrix ($1 - \text{sim}$) for visualization.

\subsection{Idea Extraction}

Claude was used to extract 3--8 discrete technical ideas per draft, each classified into one of six primary types: \textit{mechanism}, \textit{architecture}, \textit{pattern}, \textit{protocol}, \textit{requirement}, or \textit{extension}. Fuzzy string matching (SequenceMatcher, threshold 0.75) grouped similar ideas across drafts to identify convergent concepts---ideas that multiple independent teams arrived at independently.

\subsection{Gap Analysis}

Gaps were identified by comparing the idea coverage across categories against the requirements implied by the drafts themselves. Claude analyzed the full set of ideas and categories to identify areas where standardization work is missing or inadequate, assigning severity ratings (critical, high, medium) based on the breadth of the shortfall and the consequences of leaving it unfilled.

\subsection{Author Network Analysis}

Author and affiliation data were retrieved from Datatracker, yielding a bipartite graph of 710 authors across 474 drafts. We identified persistent co-author teams (``team blocs'') using a pairwise draft overlap threshold of $\geq$70\% with $\geq$3 shared drafts. Cross-organizational collaboration was measured by counting shared drafts between organizations.

\subsection{Reproducibility and Cost}

The entire analysis pipeline is implemented as a Python CLI tool (\texttt{ietf}) using Click, with all results stored in a SQLite database. LLM responses are cached to ensure reproducibility. The total API cost was approximately \$3.16 for initial analysis (330K input + 144K output tokens, Sonnet 4). All source code, the analysis database, and generated reports are released as open source.\footnote{Repository: \url{https://github.com/[redacted]/ietf-draft-analyzer}}

% ── 4. Dataset ──────────────────────────────────────────────────────────

\section{Dataset Overview}

\begin{table}[h]
\centering
\caption{Dataset summary statistics.}
\label{tab:dataset}
\begin{tabular}{lr}
\toprule
\textbf{Metric} & \textbf{Value} \\
\midrule
Internet-Drafts collected & 474 \\
False positives excluded & 113 \\
Relevant drafts (analytical corpus) & 361 \\
Unique authors & 710 \\
Organizations represented & $\sim$280 \\
Raw ideas extracted & $\sim$1,780 \\
Deduplicated ideas & 462 \\
Cross-org convergent ideas & 132 (33\%) \\
Standardization gaps identified & 12 \\
Drafts with ratings & 474 \\
Overlap clusters ($\geq$0.85 threshold) & 42 \\
Near-duplicate pairs ($\geq$0.90 threshold) & 34 \\
Time span & 2024 -- Mar 2026 \\
Embedding dimension & 768 (nomic-embed-text) \\
Pairwise similarity pairs & 112,101 \\
\bottomrule
\end{tabular}
\end{table}

The corpus spans drafts submitted from early 2024 through March 2026, with the overwhelming majority submitted after June 2025. Of the 474 drafts collected, 113 were flagged as false positives (relevance score $\leq 2$ or manually identified as non-AI-related, e.g., HPKE key encapsulation, PIE bufferbloat management), leaving 361 drafts in the analytical corpus. Table~\ref{tab:growth} shows the acceleration in AI/agent-related submissions relative to total IETF activity.

\begin{table}[h]
\centering
\caption{Growth of AI/agent Internet-Drafts relative to total IETF submissions.}
\label{tab:growth}
\begin{tabular}{rrrr}
\toprule
\textbf{Year} & \textbf{Total IETF Drafts} & \textbf{AI/Agent Drafts} & \textbf{AI Share} \\
\midrule
2021 & 1,108 & $\sim$0 & $\sim$0\% \\
2022 & 1,121 & $\sim$0 & $\sim$0\% \\
2023 & 1,241 & $\sim$0 & $\sim$0\% \\
2024 & 1,651 & 9 & 0.5\% \\
2025 & 2,696 & 190 & 7.0\% \\
2026 (Q1) & 1,748 & 162 & 9.3\% \\
\bottomrule
\end{tabular}
\end{table}

% ── 5. Findings ─────────────────────────────────────────────────────────

\section{Findings}

\subsection{Category Distribution: The Safety Deficit}

Our LLM-assisted classification assigned each draft to one or more of ten semantic categories (drafts may belong to multiple categories). Table~\ref{tab:categories} shows the distribution.

\begin{table}[h]
\centering
\caption{Draft distribution across categories. Percentages exceed 100\% due to multi-assignment.}
\label{tab:categories}
\begin{tabular}{lrr}
\toprule
\textbf{Category} & \textbf{Drafts} & \textbf{Share} \\
\midrule
Data formats / interoperability & 174 & 36\% \\
A2A protocols & 155 & 33\% \\
Agent identity / authentication & 152 & 32\% \\
Autonomous network operations & 114 & 24\% \\
Policy / governance & 91 & 19\% \\
ML traffic management & 73 & 15\% \\
Agent discovery / registration & 65 & 14\% \\
AI safety / alignment & 47 & 10\% \\
Model serving / inference & 42 & 9\% \\
Human-agent interaction & 34 & 7\% \\
\bottomrule
\end{tabular}
\end{table}

The most striking finding is the \textbf{safety deficit}. Protocol-focused categories (data formats, A2A protocols, identity/auth) collectively account for 481 category assignments, while AI safety/alignment has only 47 and human-agent interaction has 34. This yields an average \textbf{4:1 ratio of capability-building to safety proposals} (varying from 1.5:1 to 21:1 month-to-month). For every draft about keeping agents safe, approximately four are building new capabilities. For every draft about human-agent interaction, there are more than four about agents operating autonomously.

The safety drafts that \emph{do} exist are often among the highest-rated. \texttt{draft-aylward-daap-v2} (a comprehensive accountability protocol) and \texttt{draft-cowles-volt} (a tamper-evident execution trace format) each scored 4.75/5.0---among the highest in the entire corpus. The quality is there; the quantity is not.

\subsection{Rating Distributions}

Across all 474 rated drafts, Table~\ref{tab:ratings} summarizes the five rating dimensions. \textit{Note: Ratings are LLM-generated from abstracts and partial full text, without human calibration. They should be treated as relative rankings rather than absolute quality measures.}

\begin{table}[h]
\centering
\caption{Average scores across five rating dimensions ($n = 474$, scale 1--5). Scores are LLM-generated and uncalibrated against human baselines.}
\label{tab:ratings}
\begin{tabular}{lcc}
\toprule
\textbf{Dimension} & \textbf{Mean} & \textbf{Interpretation} \\
\midrule
Relevance & 3.81 & High: keyword selection captured genuinely AI-relevant drafts \\
Novelty & 3.27 & Moderate: mix of innovative and derivative proposals \\
Momentum & 3.02 & Moderate: many early-stage drafts without WG adoption \\
Maturity & 2.99 & Low--moderate: most proposals are early-stage \\
Overlap & 2.59 & Moderate: substantial redundancy in the corpus \\
\bottomrule
\end{tabular}
\end{table}

\noindent Key observations:
\begin{itemize}[nosep]
    \item \textbf{Relevance} is consistently high ($\mu = 3.81$), confirming that the keyword-based selection captured genuinely AI-relevant drafts rather than false positives.
    \item \textbf{Maturity} is the lowest-scoring dimension ($\mu = 2.99$), reflecting the early stage of most proposals---many lack complete protocol specifications, security considerations, or reference implementations.
    \item \textbf{Overlap} ($\mu = 2.59$) indicates moderate self-assessed redundancy. However, the embedding-based similarity analysis (Section~\ref{sec:overlap}) reveals that actual topical overlap is significantly higher than LLM-assessed overlap, suggesting that many drafts do not adequately acknowledge related work.
\end{itemize}

\subsection{Semantic Overlap and Redundancy}
\label{sec:overlap}

The pairwise cosine similarity analysis reveals substantial redundancy. At a 0.85 similarity threshold, we identify \textbf{42 overlap clusters}---groups of drafts addressing essentially the same technical problem. At a 0.90 threshold, \textbf{34 clusters} remain, representing near-duplicates or same-author variants.

Table~\ref{tab:clusters} shows the three largest competing clusters.

\begin{table}[h]
\centering
\caption{Three largest overlap clusters by draft count.}
\label{tab:clusters}
\begin{tabularx}{\textwidth}{clX}
\toprule
\textbf{Drafts} & \textbf{Cluster Topic} & \textbf{Description} \\
\midrule
13 & OAuth for AI Agents & All solving agent authentication/authorization via OAuth 2.0 extensions. Approaches range from Agentic JWTs to scope aggregation to accountability protocols. \\
10 & Agent Gateway / Multi-Agent Collaboration & Addressing cross-platform agent collaboration through gateway architectures, with competing semantic routing, task protocol, and infrastructure designs. \\
6 & Agent Discovery & DNS-based, URI-based, and custom protocol approaches to finding and invoking AI agents. \\
\bottomrule
\end{tabularx}
\end{table}

We also identified 25 near-duplicate draft pairs ($>$0.98 cosine similarity)---functionally identical proposals submitted under different names, in different working groups, or as renamed versions. Notable examples include \texttt{draft-rosenberg-aiproto} and \texttt{draft-rosenberg-aiproto-nact} (same N-ACT protocol, renamed), and \texttt{draft-abbey-scim-agent-extension} and \texttt{draft-scim-agent-extension} (same SCIM extension, different submission path).

This fragmentation has practical consequences. The most common recurring technical idea---``Multi-Agent Communication Protocol''---appears independently in 8 separate drafts from different teams. Yet of the 462 deduplicated technical ideas extracted from the corpus, the majority appear in only one or two drafts, with only 132 (33\%) showing cross-organizational convergence. Everyone is solving the same problems; nobody is solving them together.

\subsection{Technical Ideas Landscape}

The 462 deduplicated ideas (from approximately 1,780 raw extractions, consolidated via SequenceMatcher at 0.75 threshold) distribute across six primary types (Table~\ref{tab:ideas}).

\begin{table}[h]
\centering
\caption{Technical ideas by type.}
\label{tab:ideas}
\begin{tabular}{lrr}
\toprule
\textbf{Idea Type} & \textbf{Count} & \textbf{\%} \\
\midrule
Architecture & 107 & 23.2 \\
Protocol & 106 & 22.9 \\
Extension & 84 & 18.2 \\
Mechanism & 74 & 16.0 \\
Requirement & 47 & 10.2 \\
Pattern & 40 & 8.7 \\
Other & 4 & 0.9 \\
\midrule
\textbf{Total} & \textbf{462} & \textbf{100.0} \\
\bottomrule
\end{tabular}
\end{table}

\noindent \textit{Mechanisms} (concrete technical constructs like ``Pseudonymous Key Generation'' or ``Context-Aware Task Scheduling'') dominate at 36.4\%, followed by \textit{architectures} (system-level designs) and \textit{patterns} (reusable design approaches). The most frequently recurring convergent ideas---those appearing independently in 3+ drafts---include:

\begin{itemize}[nosep]
    \item Multi-Agent Communication Protocol (8 drafts)
    \item Agentic Network Architecture (7 drafts)
    \item Cross-Domain Agent Coordination (6 drafts)
    \item Agent-to-Agent Communication Paradigm (5 drafts)
    \item Action-Based Authorization (5 drafts)
    \item Agent Registration Process (5 drafts)
\end{itemize}

\noindent These convergent ideas represent areas of implicit community consensus---problems that multiple independent teams consider important enough to address. They are strong candidates for working group formation.

\subsection{Author and Organizational Dynamics}
\label{sec:authors}

\subsubsection{Organizational Concentration}

The authorship landscape shows significant organizational concentration. Table~\ref{tab:orgs} lists the top contributing organizations.

\begin{table}[h]
\centering
\caption{Top 10 organizations by draft contributions.}
\label{tab:orgs}
\begin{tabular}{lrr}
\toprule
\textbf{Organization} & \textbf{Authors} & \textbf{Drafts} \\
\midrule
Huawei & 55 & 69 \\
China Mobile & 24 & 35 \\
Cisco & 24 & 26 \\
Independent & 19 & 25 \\
China Telecom & 24 & 24 \\
China Unicom & 22 & 21 \\
Tsinghua University & 13 & 16 \\
ZTE Corporation & 12 & 12 \\
Five9 & 1 & 10 \\
Ericsson & 4 & 9 \\
\bottomrule
\end{tabular}
\end{table}

Huawei dominates with 55 authors contributing to 69 drafts---\textbf{approximately 16\% of the entire corpus} from a single company. Chinese technology organizations collectively (Huawei, China Mobile, China Telecom, China Unicom, ZTE, Tsinghua) contribute approximately 40\% of all drafts. Western participation is led by Cisco (26 drafts) and independent contributors (25 drafts), with notable concentrated contributions from Five9 (10 drafts from a single prolific author, Jonathan Rosenberg) and Ericsson (9 drafts from 4 authors).

\subsubsection{Team Blocs}

We identified 18 persistent co-author teams (``team blocs'') with $\geq$70\% pairwise draft overlap and $\geq$3 shared drafts. The largest is a 12-member Huawei team responsible for 23 drafts with 96\% internal cohesion---meaning team members almost always co-author together. Other notable blocs include a 5-member Cisco/Five9 team (13 drafts, 100\% cohesion) and a 5-member Ericsson team (6 drafts, 100\% cohesion).

\subsubsection{Cross-Organizational Collaboration}

Cross-organizational collaboration exists but is weaker than intra-organizational ties. The strongest cross-org links are between Chinese organizations: China Telecom--Huawei (8 shared drafts), China Unicom--Huawei (7), and China Mobile--ZTE (7). Western cross-org collaboration is led by Cisco--Google (5 shared drafts) and Bitwave--Five9 (6). Notably, cross-regional collaboration (Chinese--Western) is minimal in the dataset.

\subsection{Top-Ranked Proposals}

Table~\ref{tab:top} lists the five highest-scored drafts, representing the proposals our methodology identifies as most novel, relevant, and mature.

\begin{table}[h]
\centering
\caption{Top 5 drafts by composite score.}
\label{tab:top}
\small
\begin{tabularx}{\textwidth}{cclX}
\toprule
\textbf{Score} & \textbf{N/M/O/Mom/R} & \textbf{Draft} & \textbf{Summary} \\
\midrule
4.80 & 5/5/1/4/5 & draft-cowles-volt & Tamper-evident execution trace format for AI agent workflows using hash chains and cryptographic signatures \\
4.80 & 5/4/1/5/5 & draft-aylward-daap-v2 & Comprehensive protocol for AI agent accountability including authentication, monitoring, and audit \\
4.60 & 5/4/2/4/5 & draft-guy-bary-stamp & STAMP protocol for cryptographic delegation and proof in AI agent systems \\
4.60 & 5/5/2/3/5 & draft-drake-email-tpm & Hardware attestation for email using TPM verification chains \\
4.50 & 5/4/2/4/5 & draft-goswami-agentic-jwt & Extends OAuth 2.0 with Agentic JWT for autonomous agent authorization \\
\bottomrule
\end{tabularx}
\end{table}

\noindent It is notable that 3 of the top 5 drafts are safety/accountability-focused, suggesting that while the community underinvests in safety proposals, the ones that do exist tend to be high-quality.

% ── 6. Gap Analysis ─────────────────────────────────────────────────────

\section{Gap Analysis}

Our systematic gap analysis identified 12 areas where standardization work is missing or inadequate. Table~\ref{tab:gaps} summarizes these gaps by severity.

\begin{table}[h]
\centering
\caption{Identified standardization gaps by severity, with the number of existing technical ideas partially addressing each gap.}
\label{tab:gaps}
\begin{tabularx}{\textwidth}{clXr}
\toprule
\textbf{Sev.} & \textbf{Gap} & \textbf{Description} & \textbf{Ideas} \\
\midrule
CRIT & Behavior Verification & No mechanism to verify agents behave per declared policies at runtime & 53 \\
CRIT & Human Override Protocols & No standard for emergency stop, takeover, or constraint of running agents & 7 \\
\midrule
HIGH & Resource Exhaustion & No agent-specific resource quotas or enforcement mechanisms & 40 \\
HIGH & Data Provenance & Insufficient tracking of agent-generated data lineage & 4 \\
HIGH & Capability Degradation & No graceful degradation protocols for model drift or corruption & 45 \\
HIGH & Coordination Deadlocks & No deadlock detection/resolution for multi-agent circular dependencies & 11 \\
HIGH & Privacy Preservation & Lack of differential privacy or secure MPC for agent interactions & 11 \\
\midrule
MED & Cross-Protocol Migration & No state/context migration between different A2A protocols & 3 \\
MED & Real-time Debugging & No standard interfaces for production agent introspection & 23 \\
MED & Model Update Security & Missing cryptographically verified, rollback-capable agent updates & 79 \\
MED & Energy Optimization & No energy-aware agent deployment or energy budget enforcement & 17 \\
MED & GDPR Compliance & No mechanisms for DPIA support, right-to-erasure propagation, or purpose limitation in agent chains & 0 \\
\bottomrule
\end{tabularx}
\end{table}

\subsection{Critical Gap: Agent Behavior Verification}

While 108 drafts address agent identity and authentication---establishing \emph{who} an agent is---only 44 address AI safety/alignment, and none provides a real-time mechanism to verify that an agent is behaving according to its declared capabilities and policies \emph{while it is operating}. The gap is between policy declaration and policy enforcement: the difference between a speed limit sign and a speed camera.

Some drafts approach the problem from adjacent angles. \texttt{draft-aylward-daap-v2} (score 4.8) defines a behavioral monitoring framework with cryptographic identity verification. \texttt{draft-birkholz-verifiable-agent-conversations} (score 4.5) proposes verifiable conversation records using COSE signing. \texttt{draft-berlinai-vera} (score 3.9) introduces a zero-trust architecture with five enforcement pillars. But all focus on \emph{recording} behavior for post-hoc audit rather than \emph{detecting deviation in real time}.

\subsection{Critical Gap: Human Override Protocols}

Only 34 of 474 drafts address human-agent interaction, compared to 155 A2A protocol drafts and 114 autonomous operations drafts. Agents are being designed to talk to each other at a 4:1 ratio over being designed to talk to humans. The CHEQ protocol (\texttt{draft-rosenberg-aiproto-cheq}, score 3.9) is a rare exception---it defines human confirmation \emph{before} agent execution. But CHEQ is opt-in and pre-execution. No draft standardizes what happens \emph{during} execution: how a human pauses a running workflow, constrains an agent's scope, takes over a task, or issues an emergency stop.

\subsection{The Zero-Coverage Gap: Cross-Protocol Translation}

With 155 competing A2A protocols and no translation layer, agents speaking different protocols cannot interoperate. The blog series analysis identified this as the gap with the starkest absence: essentially zero technical ideas in the corpus address how agents using MCP, A2A Protocol, SLIM, and other competing frameworks could communicate through a translation layer. If the IETF does not build this, the market will---and the result will be vendor-locked ecosystems rather than open interoperability.

% ── 7. Discussion ────────────────────────────────────────────────────────

\section{Discussion}

\subsection{The Capability-Safety Asymmetry}

The 4:1 ratio of capability-building to safety proposals is the most consequential finding of this analysis. It mirrors a broader pattern observed across AI development: capabilities consistently outpace governance~\citep{amodei2016}. In the IETF context, this asymmetry has structural causes. Safety proposals require addressing harder, cross-cutting problems (behavior verification spans all protocol categories) while capability proposals can focus narrowly on a single well-defined problem (e.g., extending OAuth with an agent-specific claim). Additionally, organizations contributing drafts are primarily technology vendors with incentives to ship interoperable products, not safety researchers.

The quality signal offers a counterpoint: the highest-scored drafts in the corpus (\texttt{draft-cowles-volt}, \texttt{draft-aylward-daap-v2}, both 4.8/5.0) are safety-focused. The IETF community clearly values safety work when it appears. The deficit is one of \emph{volume}, not \emph{receptivity}. Targeted calls for safety-focused submissions, similar to IETF BOF sessions on specific topics, could help rebalance this.

\subsection{The Redundancy Problem}

With 42 overlap clusters and 155 competing A2A protocol proposals, the IETF AI/agent space shows significant coordination failure. The OAuth-for-agents cluster alone contains 13 independent proposals, none compatible with each other. This fragmentation wastes engineering effort, confuses implementers, and risks incompatible deployments that entrench rather than resolve the problem.

We observe that redundancy is partly a natural consequence of the IETF's open submission process---anyone can submit a draft---and partly reflects the ``gold rush'' dynamics where organizations race to establish their preferred approach as the standard. The embedding-based similarity tools developed here could help IETF area directors flag duplicates during triage and actively encourage consolidation.

\subsection{Geopolitical Dimensions}

The concentration of contributions---approximately 40\% from Chinese organizations, led by Huawei's 18\%---raises questions about geographic diversity in AI standardization. Our collaboration network analysis reveals two largely separate clusters: Chinese organizations collaborate heavily with each other (China Telecom--Huawei: 8 shared drafts; China Unicom--Huawei: 7; China Mobile--ZTE: 7) while Western organizations form a smaller, separate cluster (Cisco--Google: 5; Bitwave--Five9: 6). Cross-regional bridges are sparse.

This bifurcation extends to the technical foundations. The Chinese bloc tends to build on YANG/NETCONF for network management, while Western proposals favor COSE/CBOR/CoAP for IoT security and OAuth/JWT for identity. The only shared foundation is OAuth 2.0. Any architectural unification must be genuinely protocol-agnostic to bridge this divide.

\subsection{Methodological Contributions}

The LLM-assisted analysis pipeline itself represents a methodological contribution. Using Claude to systematically rate, categorize, and extract ideas from 474 technical documents would be infeasible manually but achieves results that are internally consistent and reproducible (via caching). Several design choices merit discussion:

\begin{itemize}[nosep]
    \item \textbf{LLM rating validity}: Claude rates based on abstracts and partial full text, which may not capture implementation depth. We mitigate this by using five orthogonal dimensions that capture different quality facets, and by validating that alternative weighting schemes produce highly correlated rankings (Appendix~\ref{app:sensitivity}, Spearman $\rho \geq 0.93$).

    \item \textbf{Embedding similarity}: Cosine similarity between nomic-embed-text embeddings captures topical similarity but not functional equivalence. Two drafts may address the same problem with different approaches (low similarity, high functional overlap). We treat high similarity as a signal for manual review, not definitive evidence of redundancy.

    \item \textbf{Cost efficiency}: The entire analysis cost approximately \$3.16 in API fees---orders of magnitude cheaper than equivalent expert analysis, enabling continuous monitoring as new drafts appear.
\end{itemize}

\subsection{Toward an Architectural Vision}

Our analysis suggests that the 12 gaps are not random absences but structurally related. They point to four missing architectural pillars for the AI agent ecosystem:

\begin{enumerate}[nosep]
    \item \textbf{DAG-based execution model}: Multi-agent workflows as directed acyclic graphs with checkpoints, rollback, and blast-radius containment---addressing error recovery, resource management, and coordination gaps.

    \item \textbf{Human-in-the-loop as first class}: Approval gates, override commands, escalation paths, and explainability tokens as native constructs in the execution model---addressing the human override and explainability gaps.

    \item \textbf{Protocol-agnostic interoperability}: A translation layer letting agents using different A2A protocols communicate through gateways---addressing the cross-protocol gap with zero existing ideas.

    \item \textbf{Assurance profiles}: Named configurations that dial up or down the proof requirements (from best-effort to cryptographic attestation per task)---addressing behavior verification, data provenance, and dynamic trust gaps.
\end{enumerate}

\noindent These pillars build on existing IETF work rather than competing with it: SPIFFE/WIMSE for identity, Execution Context Tokens for evidence, OAuth 2.0 for authorization, and the various A2A protocols for communication.

\subsection{Limitations}

\begin{itemize}[nosep]
    \item \textbf{Keyword bias}: Our twelve seed keywords may miss relevant drafts using different terminology (e.g., ``cognitive computing,'' ``neural network'' in draft names).
    \item \textbf{Single-LLM assessment}: All ratings come from Claude with no human calibration or inter-rater reliability testing. No intra-rater consistency check was performed. Cross-validation with other LLMs (GPT-4, Gemini) and human expert baselines would strengthen confidence.
    \item \textbf{Abstract-level rating}: Ratings are based on abstracts and partial full text (first 4,000 characters), which may not capture implementation depth in longer specifications.
    \item \textbf{Snapshot analysis}: The dataset reflects a point in time (March 2026); drafts expire, evolve, and merge continuously.
    \item \textbf{False positive filtering}: Despite removing 113 false positives, an estimated 20--30 borderline drafts may remain in the corpus. The filtering threshold (relevance $\leq 2$) is conservative.
    \item \textbf{Author disambiguation}: Datatracker affiliations are self-reported and may be inconsistent (e.g., ``Huawei'' vs.\ ``Huawei Technologies'' appear as separate entities).
    \item \textbf{No citation analysis}: We do not track inter-draft references, which would reveal influence networks beyond topical similarity.
    \item \textbf{Clustering threshold}: The 0.85 cosine similarity threshold for overlap clustering was chosen empirically without sensitivity analysis across multiple thresholds.
\end{itemize}

% ── 8. Related Work ─────────────────────────────────────────────────────

\section{Related Work}

\textbf{Standards landscape analysis.} Baron and Spulber~\citep{baron2019} provide bibliometric analysis of standards organizations but focus on patents and firm-level strategy rather than technical content. Simmons and Thaler~\citep{simmons2019} study IETF participation diversity but do not assess draft content or topical overlap. Our work extends this line by applying NLP techniques to the document content itself.

\textbf{AI governance and safety.} Amodei et al.~\citep{amodei2016} articulate the challenge of aligning AI systems with human values, a concern our safety deficit finding quantifies in the standards context. The EU AI Act~\citep{euaiact2024} and NIST AI Risk Management Framework~\citep{nist2023} provide regulatory perspectives on AI governance, but neither addresses Internet protocol standardization specifically.

\textbf{LLM-assisted evaluation.} Zheng et al.~\citep{zheng2023} demonstrate that LLM judges can match human evaluation quality for text assessment. Our pipeline extends this approach from evaluating model outputs to evaluating standards documents, using structured prompts for multi-dimensional rating.

\textbf{Multi-agent systems.} The AAMAS community has long studied multi-agent coordination~\citep{wooldridge2009}. The Foundation for Intelligent Physical Agents (FIPA) developed the first agent communication standards (FIPA-ACL, Agent Management, Interaction Protocols) in the late 1990s, which influenced many current IETF proposals~\citep{fipa2002}. IEEE P3394 (Standard for Trustworthy Autonomous and Semi-Autonomous Systems) addresses agent trust from a systems engineering perspective~\citep{ieeep3394}. The W3C Web of Things Architecture~\citep{w3cwot2020} defines discovery and description mechanisms relevant to agent registration. Our analysis reveals that the IETF is now addressing many of the same problems (coordination, trust, resource allocation) but from a protocol standardization perspective rather than an algorithmic one, with limited acknowledgment of this prior work.

% ── 9. Future Work ──────────────────────────────────────────────────────

\section{Future Work}

\begin{enumerate}[nosep]
    \item \textbf{Human validation}: Compare LLM ratings against expert assessments for a stratified sample of 30--50 drafts to quantify LLM judge accuracy in this domain.
    \item \textbf{Longitudinal monitoring}: Deploy the pipeline for continuous analysis as new drafts appear, tracking the evolution of the safety ratio, overlap clusters, and gap coverage over time.
    \item \textbf{Citation network}: Extract inter-draft references to build a citation graph, enabling influence analysis beyond topical similarity.
    \item \textbf{Gap-driven standardization}: Use identified gaps to propose new Internet-Drafts---we have already generated five experimental drafts addressing the architectural pillars described in Section~7.4.
    \item \textbf{Cross-venue analysis}: Extend the methodology to W3C, OASIS, ISO/IEC JTC 1, and 3GPP AI standardization activities for a comprehensive view of the global AI standards landscape.
    \item \textbf{Multi-LLM validation}: Cross-validate ratings using multiple LLM judges (Claude, GPT-4, Gemini) to assess systematic bias.
\end{enumerate}

% ── 10. Conclusion ────────────────────────────────────────────────────────

\section{Conclusion}

The IETF AI/agent standardization wave represents a unique moment in Internet governance: the community is attempting to standardize the infrastructure for autonomous agents concurrently with their deployment. Our analysis of 474 Internet-Drafts from 710 authors (361 after false-positive filtering) reveals a landscape characterized by both extraordinary energy and significant structural problems.

Three findings demand attention. First, the \textbf{4:1 safety deficit}: the community is building agent capabilities four times faster than safety mechanisms, despite the highest-quality proposals being safety-focused. Second, \textbf{extreme fragmentation}: 155 competing A2A protocol proposals, 13 independent OAuth-for-agents drafts, and only 33\% cross-organizational convergence among 462 deduplicated ideas indicate that coordination mechanisms are failing to keep pace with submission volume. Third, \textbf{organizational concentration}: 16\% of all drafts from a single company and approximately 40\% from Chinese organizations raise questions about geographic diversity in the standards that will govern global AI agent infrastructure.

The 462 deduplicated technical ideas we extract (with 132 showing cross-organizational convergence) represent a rich but disorganized design space. The 12 gaps we identify---from behavior verification to human override protocols to cross-protocol translation---highlight where the community's collective blind spots lie. The architectural vision we sketch, building on existing IETF primitives (WIMSE, ECT, OAuth), suggests a path from fragmentation toward coherence.

The methodology demonstrated here---combining LLM-assisted multi-dimensional rating with embedding-based similarity analysis---is itself a contribution. At \$3.16 in API costs, it provides a scalable, reproducible approach to standards landscape analysis that could be applied to any standards body facing a surge in submissions. As AI standardization accelerates globally, such tools become essential for maintaining coherence and directing limited community attention to the areas that matter most.

The gold rush will not slow down. The question is whether the safety inspectors can catch up.

% ── Acknowledgments ──────────────────────────────────────────────────────

\section*{Acknowledgments}

Analysis was performed using Anthropic Claude (Sonnet 4) for rating, categorization, and idea extraction, and Ollama with nomic-embed-text for embedding generation. We thank the IETF community for maintaining the open Datatracker API that made this analysis possible.

% ── References ───────────────────────────────────────────────────────────

\bibliographystyle{plainnat}

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% ── Appendix ─────────────────────────────────────────────────────────────

\appendix

\section{Composite Score Formula Sensitivity}
\label{app:sensitivity}

To verify that our findings are robust to weight choices, we tested three alternative weighting schemes:

\begin{table}[H]
\centering
\begin{tabular}{lcccccc}
\toprule
\textbf{Scheme} & \textbf{N} & \textbf{R} & \textbf{M} & \textbf{Mom} & \textbf{O\textsuperscript{--1}} & \textbf{Rank corr.} \\
\midrule
Default & 0.30 & 0.25 & 0.20 & 0.15 & 0.10 & 1.000 \\
Equal & 0.20 & 0.20 & 0.20 & 0.20 & 0.20 & 0.96 \\
Maturity-heavy & 0.20 & 0.20 & 0.30 & 0.15 & 0.15 & 0.95 \\
Novelty-only & 0.50 & 0.20 & 0.10 & 0.10 & 0.10 & 0.93 \\
\bottomrule
\end{tabular}
\caption{Spearman rank correlation between composite scores under alternative weighting schemes vs.\ the default. High correlations ($\geq 0.93$) indicate the rankings are largely robust to weight choice.}
\label{tab:sensitivity}
\end{table}

\section{Keyword Search Terms}
\label{app:keywords}

\begin{table}[H]
\centering
\begin{tabular}{ll}
\toprule
\textbf{Keyword} & \textbf{Rationale} \\
\midrule
\texttt{agent} & Core term for AI agent drafts \\
\texttt{ai-agent} & Specific AI agent proposals \\
\texttt{llm} & Large language model infrastructure \\
\texttt{autonomous} & Self-operating systems and agents \\
\texttt{machine-learning} & ML-related protocol work \\
\texttt{artificial-intelligence} & General AI drafts \\
\texttt{mcp} & Model Context Protocol ecosystem \\
\texttt{agentic} & Agentic AI paradigm \\
\texttt{inference} & AI inference infrastructure \\
\texttt{generative} & Generative AI protocols \\
\texttt{intelligent} & Intelligent networking/systems \\
\texttt{aipref} & AI preference signaling (AIPREF WG) \\
\bottomrule
\end{tabular}
\caption{Twelve seed keywords used for Datatracker API queries, with rationale for inclusion.}
\end{table}

\section{Top Convergent Ideas}
\label{app:convergent}

\begin{table}[H]
\centering
\small
\begin{tabularx}{\textwidth}{Xrl}
\toprule
\textbf{Idea} & \textbf{Drafts} & \textbf{Primary Type} \\
\midrule
Multi-Agent Communication Protocol & 8 & protocol \\
Agentic Network Architecture & 7 & architecture \\
Cross-Domain Agent Coordination & 6 & mechanism \\
ELA Protocol (EDHOC Lightweight Auth) & 6 & protocol \\
Agent-to-Agent Communication Paradigm & 5 & protocol \\
Action-Based Authorization & 5 & mechanism \\
AI Agent Communication Network & 5 & architecture \\
Agent Registration Process & 5 & protocol \\
AI Gateway & 4 & architecture \\
MCP Session Establishment over MOQT & 4 & protocol \\
Network Equipment as MCP Servers & 4 & mechanism \\
Multi-Agent Interaction Model & 4 & pattern \\
Distributed AI Inference Architecture & 4 & architecture \\
\bottomrule
\end{tabularx}
\caption{Most frequently occurring convergent ideas (appearing in $\geq$4 drafts independently). These represent areas of implicit community consensus.}
\end{table}

\end{document}
