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

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