# Survey Phase 0 — Quantitative IETF AI/Agent Draft Landscape

_Generated 2026-05-23 20:49 UTC by `scripts/survey-phase0.py` (deterministic, no LLM API calls)._

**Corpus definition ("clean IETF corpus"):** `source='ietf' AND (ratings.false_positive=0 OR NULL)`, joining `drafts d` with `ratings r` on `d.name=r.draft_name`. **N = 524 drafts.**

## 1. Author / Working-Group Concentration

- Distinct authors across the clean corpus: **619**
- Clean drafts with at least one resolved author in `draft_authors`: **396** of 524 (75.6%); the remainder have no rows in `draft_authors`.
- Total (author, draft) authorship memberships: **1193**

### Top 15 authors by clean-IETF draft count

| # | Author | Drafts |
| --- | --- | --- |
| 1 | Bing Liu | 22 |
| 2 | Nan Geng | 21 |
| 3 | Zhenbin Li | 20 |
| 4 | Qiangzhou Gao | 19 |
| 5 | Xiaotong Shang | 18 |
| 6 | Jianwei Mao | 14 |
| 7 | Guanming Zeng | 13 |
| 8 | Cullen Fluffy Jennings | 11 |
| 9 | Jonathan Rosenberg | 11 |
| 10 | Henk Birkholz | 10 |
| 11 | Qin Wu | 10 |
| 12 | Aijun Wang | 9 |
| 13 | Chongfeng Xie | 8 |
| 14 | Göran Selander | 8 |
| 15 | Luis M. Contreras | 8 |

### Concentration (top 10 authors)

- Top-10 authors account for **159 of 1193 authorship memberships (13.3%)**.
- Top-10 authors appear on **57 of 524 distinct clean drafts (10.9%)** (a draft is counted once even if multiple top-10 authors appear on it).

### Working groups

The `drafts.group` text column is effectively unpopulated for this corpus (only 524 drafts resolve to no/empty/'none' group, and the column has no real WG names). The meaningful grouping signal is `drafts.group_uri` (Datatracker group IDs).

- Drafts with no working group (group/1027 "individual" pseudo-group or null `group_uri`): **456** of 524.
- Distinct `group_uri` values: **28**.

Top 15 groups by `group_uri` (clean-IETF draft count):

| # | Group (Datatracker ID / label) | Drafts |
| --- | --- | --- |
| 1 | (individual / no WG) | 456 |
| 2 | group/2440 | 9 |
| 3 | group/2249 | 8 |
| 4 | group/2231 | 7 |
| 5 | group/2164 | 6 |
| 6 | group/1674 | 4 |
| 7 | group/1326 | 4 |
| 8 | group/1452 | 3 |
| 9 | group/38 | 2 |
| 10 | group/2430 | 2 |
| 11 | group/2380 | 2 |
| 12 | group/2317 | 2 |
| 13 | group/1956 | 2 |
| 14 | group/1921 | 2 |
| 15 | group/1718 | 2 |

> Note: ID 1027 is the Datatracker "none" pseudo-group for individually submitted drafts; it is not a real working group. Real WG/RG IDs are the remaining rows.

## 2. Embedding Overlap / Redundancy

- Embedding model: `nomic-embed-text`, dimension **768**, stored as float32 BLOB.
- Drafts embedded (clean corpus): **524** (100% coverage); pairwise comparisons: **137,026**.

### Pairwise cosine similarity (off-diagonal)

| Mean | Median | p90 | p99 | Max |
| --- | --- | --- | --- | --- |
| 0.7106 | 0.7110 | 0.7901 | 0.8497 | 1.0000 |

- Near-duplicate pairs (cosine > 0.9): **125**.
- Drafts with at least one near-duplicate (cosine > 0.9): **170** of 524 (32.4%).

### Top 20 most-similar draft pairs

| # | Cosine | Draft A / Draft B | Title A / Title B |
| --- | --- | --- | --- |
| 1 | 1.0000 | `draft-howard-rats-coserv`<br>`draft-ietf-rats-coserv` | Concise Selector for Endorsements and Reference Values<br>Concise Selector for Endorsements and Reference Values |
| 2 | 1.0000 | `draft-ietf-savnet-inter-domain-architecture`<br>`draft-wu-savnet-inter-domain-architecture` | Inter-domain Source Address Validation (SAVNET) Architecture<br>Inter-domain Source Address Validation (SAVNET) Architecture |
| 3 | 1.0000 | `draft-fv-rats-ear`<br>`draft-ietf-rats-ear` | EAT Attestation Results<br>EAT Attestation Results |
| 4 | 1.0000 | `draft-ietf-rats-evidence-trans`<br>`draft-smith-rats-evidence-trans` | Evidence Transformations<br>Evidence Transformations |
| 5 | 0.9993 | `draft-rosenberg-aiproto`<br>`draft-rosenberg-aiproto-nact` | Normalized API for AI Agents Calling Tools (N-ACT)<br>Normalized API for AI Agents Calling Tools (N-ACT) |
| 6 | 0.9986 | `draft-lake-pocero-authkem-ikr-edhoc`<br>`draft-pocero-authkem-ikr-edhoc` | KEM-based Authentication for EDHOC in Initiator-Known Responder (IKR) <br>KEM-based Authentication for EDHOC in Initiator-Known Responder (IKR)  |
| 7 | 0.9976 | `draft-dunbar-onions-ac-te-applicability`<br>`draft-dunbar-onsen-ac-te-applicability` | Applying Attachmet Circuit and Traffic Engineering YANG Data Model to <br>Applying Attachmet Circuit and Traffic Engineering YANG Data Model to  |
| 8 | 0.9971 | `draft-dunbar-neotec-ac-pe2pe-ucmp-applicability`<br>`draft-dunbar-onsen-ac-pe2pe-ucmp-applicability` | Applying Attachmet Circuit and PE2PE YANG Data Model to dynamic polici<br>Applying Attachmet Circuit and PE2PE YANG Data Model to dynamic polici |
| 9 | 0.9968 | `draft-ar-emu-hybrid-pqc-eapaka`<br>`draft-ar-emu-pqc-eapaka` | Enhancing Security in EAP-AKA' with Hybrid Post-Quantum Cryptography<br>Enhancing Security in EAP-AKA' with Hybrid Post-Quantum Cryptography |
| 10 | 0.9967 | `draft-aft-ai-traffic`<br>`draft-ai-traffic` | Handling inter-DC/Edge AI-related network traffic: Problem statement<br>Handling inter-DC/Edge AI-related network traffic: Problem statement |
| 11 | 0.9967 | `draft-ahn-nmrg-5g-security-i2nsf-framework`<br>`draft-ahn-opsawg-5g-security-i2nsf-framework` | An Integrated Security Service System for 5G Networks using an I2NSF F<br>An Integrated Security Service System for 5G Networks using an I2NSF F |
| 12 | 0.9961 | `draft-liu-saag-zt-problem-statement`<br>`draft-liu-ztcpp-zt-problem-statement` | Zero trust standards in IETF: use cases and problem statement<br>Zero trust standards in IETF: use cases and problem statement |
| 13 | 0.9954 | `draft-zw-nmrg-mcp-network-mgmt`<br>`draft-zw-rtgwg-mcp-network-mgmt` | Model Context Protocol (MCP) Extensions for Network Equipment Manageme<br>Model Context Protocol (MCP) Extensions for Network Equipment Manageme |
| 14 | 0.9952 | `draft-dunbar-neotec-ac-te-applicability`<br>`draft-dunbar-onsen-ac-te-applicability` | Applying Attachmet Circuit and Traffic Engineering YANG Data Model to <br>Applying Attachmet Circuit and Traffic Engineering YANG Data Model to  |
| 15 | 0.9948 | `draft-ietf-emu-pqc-eapaka`<br>`draft-ra-emu-pqc-eapaka` | Post-Quantum Key Encapsulation Mechanisms (PQ KEMs) in EAP-AKA prime<br>Post-Quantum Key Encapsulation Mechanisms (PQ KEMs) in EAP-AKA prime |
| 16 | 0.9946 | `draft-sun-zhang-iaip`<br>`draft-sz-dmsc-iaip` | Intent-based Agent Interconnection Protocol at Agent Gateway<br>Intent-based Agent Interconnection Protocol at Agent Gateway |
| 17 | 0.9944 | `draft-ar-emu-hybrid-pqc-eapaka`<br>`draft-ietf-emu-hybrid-pqc-eapaka` | Enhancing Security in EAP-AKA' with Hybrid Post-Quantum Cryptography<br>Enhancing Security in EAP-AKA' with Hybrid Post-Quantum Cryptography |
| 18 | 0.9942 | `draft-intellinode-cats-in-network-scheduling`<br>`draft-li-cats-intellinode-network-scheduling` | IntelliNode: In-Network Intelligent Scheduling Extensions for CATS<br>IntelliNode: In-Network Intelligent Scheduling Extensions for CATS |
| 19 | 0.9941 | `draft-ar-emu-pqc-eapaka`<br>`draft-ietf-emu-hybrid-pqc-eapaka` | Enhancing Security in EAP-AKA' with Hybrid Post-Quantum Cryptography<br>Enhancing Security in EAP-AKA' with Hybrid Post-Quantum Cryptography |
| 20 | 0.9936 | `draft-rosenberg-aiproto-cheq`<br>`draft-rosenberg-cheq` | CHEQ: A Protocol for Confirmation AI Agent Decisions with Human in the<br>CHEQ: A Protocol for Confirmation AI Agent Decisions with Human in the |

## 3. Category Coverage Map

Primary category = first element of the `ratings.categories` JSON array. Sum check: **524 = 524** (OK).

- Drafts carrying more than one category: **487** of 524 (92.9%).
- Drafts with no category (uncategorized): **0**.

### Primary-category distribution + mean scores

Scores are 1-5 integers from `ratings`. `composite` = mean of the 5 dimensions (novelty, maturity, overlap, momentum, relevance) per draft, averaged over the category.

| Category | N | Share | Relevance | Composite | Novelty | Maturity | Overlap | Momentum |
| --- | --- | --- | --- | --- | --- | --- | --- | --- |
| Agent identity/auth | 141 | 26.9% | 4.26 | 3.38 | 3.64 | 3.28 | 2.43 | 3.29 |
| A2A protocols | 108 | 20.6% | 4.18 | 3.32 | 3.67 | 3.1 | 2.48 | 3.18 |
| Autonomous netops | 64 | 12.2% | 3.86 | 3.13 | 3.23 | 2.73 | 2.83 | 3.02 |
| ML traffic mgmt | 48 | 9.2% | 3.85 | 3.09 | 3.1 | 2.71 | 2.94 | 2.83 |
| Data formats/interop | 44 | 8.4% | 3.66 | 3.14 | 3.18 | 3.55 | 2.11 | 3.2 |
| Agent discovery/reg | 35 | 6.7% | 4.2 | 3.24 | 3.6 | 3.09 | 2.31 | 3 |
| Policy/governance | 30 | 5.7% | 4.1 | 3.25 | 3.37 | 2.97 | 2.53 | 3.27 |
| AI safety/alignment | 24 | 4.6% | 4.5 | 3.38 | 4.17 | 3.17 | 1.75 | 3.33 |
| Model serving/inference | 16 | 3.1% | 4.19 | 3.31 | 3.31 | 3.19 | 2.62 | 3.25 |
| Other AI/agent | 9 | 1.7% | 3.56 | 3.18 | 3.22 | 3.44 | 2.22 | 3.44 |
| Human-agent interaction | 5 | 1.0% | 4.4 | 3.48 | 3.8 | 3.4 | 2 | 3.8 |

### Three sparsest categories (descriptive)

Listed neutrally as the categories with the fewest drafts in this corpus:

| Category | N | Relevance | Composite |
| --- | --- | --- | --- |
| Human-agent interaction | 5 | 4.4 | 3.48 |
| Other AI/agent | 9 | 3.56 | 3.18 |
| Model serving/inference | 16 | 4.19 | 3.31 |

