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ietf-draft-analyzer/data/reports/landscape.md
Christian Nennemann d6beb9c0a0 v0.3.0: Gap-to-Draft pipeline, Living Standards Observatory, blog series 
Gap-to-Draft Pipeline (ietf pipeline):
- Context builder assembles ideas, RFC foundations, similar drafts, ecosystem vision
- Generator produces outlines + sections using rich context with Claude
- Quality gates: novelty (embedding similarity), references, format, self-rating
- Family coordinator generates 5-draft ecosystem (AEM/ATD/HITL/AEPB/APAE)
- I-D formatter with proper headers, references, 72-char wrapping

Living Standards Observatory (ietf observatory):
- Source abstraction with IETF + W3C fetchers
- 7-step update pipeline: snapshot, fetch, analyze, embed, ideas, gaps, record
- Static GitHub Pages dashboard (explorer, gap tracker, timeline)
- Weekly CI/CD automation via GitHub Actions

Also includes:
- 361 drafts (expanded from 260 with 6 new keywords), 403 authors, 1,262 ideas, 12 gaps
- Blog series (8 posts planned), reports, arXiv paper figures
- Agent team infrastructure (CLAUDE.md, scripts, dev journal)
- 5 new DB tables, schema migration, ~15 new query methods

Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-03-04 00:48:57 +01:00

118 KiB
Raw Blame History

IETF AI/Agent Draft Landscape

Generated 2026-03-03 19:58 UTC

A2A protocols (85 drafts)

AI safety / guardrails / alignment (1 drafts)

AI safety/alignment (35 drafts)

  • draft-cowles-volt (score: 4.8) — Defines tamper-evident execution trace format for AI agent workflows using hash chains and cryptogra
  • draft-aylward-daap-v2 (score: 4.8) — Defines comprehensive protocol for AI agent accountability including authentication, monitoring, and
  • draft-guy-bary-stamp-protocol (score: 4.6) — Defines STAMP protocol for cryptographic delegation and proof in AI agent systems. Provides task-bou
  • draft-drake-email-tpm-attestation (score: 4.6) — Defines hardware attestation for email using TPM verification chains to prevent spam and provide Syb
  • draft-goswami-agentic-jwt (score: 4.5) — Extends OAuth 2.0 with Agentic JWT to address authorization challenges in autonomous AI systems. Int
  • draft-birkholz-verifiable-agent-conversations (score: 4.5) — Defines CDDL-based data format for verifiable agent conversation records using COSE signing. Support
  • draft-aylward-aiga-2 (score: 4.5) — Comprehensive AI governance framework with tiered risk model, federated authority network, and econo
  • draft-mw-wimse-transitive-attestation (score: 4.3) — Defines WIMSE profile for cryptographically binding workload identities to their execution environme
  • draft-aylward-aiga-1 (score: 4.2) — Specifies AI Governance and Accountability Protocol with tiered risk-based governance model. Include
  • draft-aap-oauth-profile (score: 4.2) — Defines an OAuth 2.0 authorization profile specifically for autonomous AI agents, extending existing
  • draft-zhang-dmsc-mas-communication (score: 4.2) — Analyzes security risks in multi-agent communication and limitations of existing protocols like TLS
  • draft-jewell-aibdp (score: 4.2) — Defines AI Boundary Declaration Protocol for expressing content usage boundaries for AI systems. Pro
  • draft-liu-agent-operation-authorization (score: 4.1) — Specifies framework for verifiable delegation of actions from humans to AI agents using JWT tokens.
  • draft-cui-nmrg-llm-nm (score: 4.1) — Defines framework for collaborative network management between LLM agents and human operators. Intro
  • draft-schulze-ecap (score: 4.1) — ECAP defines a cryptographically-verified protocol for web crawlers to obtain consent from hosts bef
  • draft-mw-spice-actor-chain (score: 4.1) — Extends OAuth 2.0 Token Exchange with cryptographically verifiable actor chains to provide tamper-ev
  • draft-barney-caam (score: 4.0) — Specifies Contextual Agent Authorization Mesh for runtime authorization of agents after discovery, p
  • draft-han-anima-ai-asa (score: 4.0) — Analyzes the impact of enhancing ANIMA's Autonomic Service Agents with AI/LLM capabilities, focusing
  • draft-rosenberg-aiproto-cheq (score: 3.9) — Proposes CHEQ protocol for human confirmation of AI agent decisions before execution. Protects again
  • draft-berlinai-vera (score: 3.9) — Introduces VERA, a zero-trust architecture for AI agent security with five enforcement pillars and c
  • draft-chen-ai-agent-auth-new-requirements (score: 3.8) — Identifies new authentication and authorization requirements for AI agents that go beyond traditiona
  • draft-ni-a2a-ai-agent-security-requirements (score: 3.7) — Establishes security requirements for AI agents across their operational lifecycle. Covers provision
  • draft-kotecha-agentic-dispute-protocol (score: 3.6) — Defines a protocol for autonomous agents to file and resolve disputes through structured automated p
  • draft-rosenberg-oauth-aauth (score: 3.6) — Extends OAuth 2.1 for AI agents operating through PSTN/SMS channels to obtain access tokens using PI
  • draft-rosenberg-cheq (score: 3.6) — Proposes CHEQ protocol for human-in-the-loop confirmation of AI agent decisions before execution. Us
  • draft-messous-eat-ai (score: 3.6) — Defines an Entity Attestation Token profile for remote attestation of autonomous AI agents, specifyi
  • draft-reilly-sentinel-protocol (score: 3.6) — Defines blockchain-anchored integrity protocol for AI lifecycle provenance using Sentinel Evidence P
  • draft-vaughan-aipref-vocab (score: 3.6) — Proposes vocabulary for rightsholders to express content preferences for AI training use through met
  • draft-wang-hjs-accountability (score: 3.5) — Defines HJS accountability layer for AI agents using blockchain-anchored timestamps to create immuta
  • draft-ietf-sml-trust (score: 3.5) — Provides trust and security recommendations for handling structured data in email messages. Addresse
  • draft-yuan-rtgwg-security-agent-usecase (score: 3.4) — Proposes AI Network Security Agents for routers to provide intelligent, adaptive security capabiliti
  • draft-huang-rats-agentic-eat-cap-attest (score: 3.4) — Extends Entity Attestation Token (EAT) to support capability attestation for agentic AI systems. Ena
  • draft-jiang-seat-dynamic-attestation (score: 3.4) — Defines dynamic attestation mechanisms for AI agents to convey runtime posture changes during long-l
  • draft-romanchuk-normative-admissibility (score: 3.4) — Establishes a framework for evaluating whether autonomous agent speech acts are admissible based on
  • draft-diaconu-agents-authz-info-sharing (score: 3.2) — Addresses authorization challenges in distributed multi-agent systems across multiple domains. Cover

Agent discovery / registration (5 drafts)

Agent discovery/reg (43 drafts)

  • draft-narajala-ans (score: 4.2) — Introduces Agent Name Service (ANS) as a DNS-based universal directory for AI agent discovery and ve
  • draft-li-dmsc-macp (score: 4.2) — Specifies a comprehensive multi-agent collaboration protocol suite using Agent Gateways for registra
  • draft-cui-dns-native-agent-naming-resolution (score: 4.1) — Specifies DNS-native naming and resolution for AI agents using FQDNs and SVCB records. Emphasizes DN
  • draft-nederveld-adl (score: 4.1) — Defines ADL, a JSON-based standard for describing AI agents including their capabilities, tools, per
  • draft-ainp-protocol (score: 3.9) — Defines semantic communication protocol for AI agents using intent-based routing and negotiation. Re
  • draft-ietf-lake-authz (score: 3.9) — Specifies lightweight authorization using EDHOC for zero-touch device onboarding. Enables secure enr
  • draft-agent-gw (score: 3.9) — Proposes an Intelligent Agent Communication Gateway for large-scale multi-agent collaboration. Featu
  • draft-mp-agntcy-ads (score: 3.9) — Describes Agent Directory Service for storing and discovering AI agent metadata based on skills. Fea
  • draft-rosenberg-aiproto-a2t (score: 3.9) — Defines Agent-to-Tool (A2T) protocol for integrating third-party APIs into AI agent operations. Prov
  • draft-cui-ai-agent-discovery-invocation (score: 3.9) — Proposes standardized protocol for AI agent discovery and invocation with common metadata format and
  • draft-sogomonian-ai-uri-scheme (score: 3.8) — Defines experimental AI URI scheme for dedicated AI resource access. Enables native connectivity for
  • draft-abbey-scim-agent-extension (score: 3.8) — Extends SCIM 2.0 protocol to manage AI agents and agentic applications across domains. Adds new sche
  • draft-eckert-anima-acp-free-ani (score: 3.8) — Describes lightweight variation of Autonomic Networking Infrastructure without expensive ACP impleme
  • draft-zheng-agent-identity-management (score: 3.7) — Defines comprehensive agent identity management for Internet of Agents systems. Covers agent registr
  • draft-an-nmrg-i2icf-cits (score: 3.7) — Defines framework for orchestrating In-Network Computing Functions in Cooperative Intelligent Transp
  • draft-howe-sipcore-mcp-extension (score: 3.7) — Defines SIP extension to carry Model Context Protocol with new option-tags, headers, and media types
  • draft-rosenberg-aiproto (score: 3.7) — Defines N-ACT protocol for AI agents to discover and invoke third-party tools and APIs. Focuses on e
  • draft-rosenberg-aiproto-nact (score: 3.7) — Defines N-ACT protocol for AI agents to discover and invoke third-party tools and APIs. Focuses on e
  • draft-zyyhl-agent-networks-framework (score: 3.6) — Defines comprehensive framework for AI agent networks based on Agent Network Protocol (ANP). Provide
  • draft-zlgsgl-rtgwg-agents-networking-framework (score: 3.6) — Introduces a comprehensive agents networking framework for enterprise and broadband environments. De
  • draft-han-rtgwg-agent-gateway-intercomm-framework (score: 3.6) — Defines framework for intercommunication between Agent Gateways in Agent Internet ecosystem. Address
  • draft-yang-ioa-protocol (score: 3.6) — Defines the Internet of Agents Protocol for distributed collaboration among heterogeneous AI agents.
  • draft-mozleywilliams-dnsop-bandaid (score: 3.6) — Proposes using DNS with SVCB records to enable AI agent discovery and capability advertisement. Leve
  • draft-li-semantic-routing-architecture (score: 3.6) — Introduces semantic routing architecture using intent vectors and trust scores for AI agent communic
  • draft-mozleywilliams-dnsop-dnsaid (score: 3.6) — Uses existing DNS infrastructure for AI agent discovery through structured namespace and metadata ex
  • draft-eggert-mailmaint-uaautoconf (score: 3.6) — Specifies automatic configuration mechanism for email, calendar, and contact applications. Enables s
  • draft-li-cats-idn (score: 3.6) — Introduces Intelligence Delivery Network (IDN) framework for deploying ML models across distributed
  • draft-liu-dmsc-acps-arc (score: 3.6) — Proposes Agent Collaboration Protocols architecture for Internet of Agents, covering agent lifecycle
  • draft-liang-agentdns (score: 3.5) — Proposes a DNS-inspired naming and service discovery system for LLM agents to enable autonomous disc
  • draft-mozley-aidiscovery (score: 3.5) — Defines requirements and considerations for AI agent-to-agent discovery mechanisms. Addresses the fu
  • draft-ye-problems-and-requirements-of-dns-for-ioa (score: 3.5) — Analyzes DNS challenges for Internet of Agents scenarios and identifies technical requirements. Expl
  • draft-li-dmsc-mcps-agw (score: 3.5) — Defines a protocol suite using Agent Gateways as control-plane entities for multi-agent collaboratio
  • draft-kartha-grd (score: 3.4) — Defines architectural framework for discovering network resources based on physical location and con
  • draft-du-catalist-routing-considerations (score: 3.4) — Proposes routing considerations for AI agent-to-agent communication in overlay networks. Focuses on
  • draft-kartha-internet20-ainative (score: 3.4) — Proposes Internet 2.0 architecture with AI models as first-class network entities, including HTTP+AI
  • draft-jimenez-tbd-robotstxt-update (score: 3.4) — Proposes updates to robots.txt standard to handle AI-specific crawlers with new syntax for user-agen
  • draft-stephan-ai-agent-6g (score: 3.4) — Examines AI agent communication protocols specifically for 6G systems based on 3GPP requirements. Ex
  • draft-zeng-nmrg-mcp-usecases-requirements (score: 3.4) — Presents problem statement for integrating Model Context Protocol into network management for AI age
  • draft-sz-dmsc-iaip (score: 3.3) — Defines Intent-based Agent Interconnection Protocol (IAIP) for dynamic agent discovery and routing a
  • draft-jeong-opsawg-intent-based-sdv-framework (score: 3.3) — Proposes intent-based management framework for Software-Defined Vehicles in ITS environments. Covers
  • draft-liu-rtgwg-agent-gateway-requirements (score: 3.2) — Discusses requirements for Agent Gateways in agent-to-agent communications to improve scalability, e
  • draft-pioli-agent-discovery (score: 3.2) — Specifies ARDP, a lightweight protocol for agent registration and discovery in distributed environme
  • draft-gaikwad-woa (score: 3.2) — Specifies Web of Agents (WoA) format using JSON Schema to describe AI agent inputs/outputs served fr

Agent identity/auth (75 drafts)

Agent-to-agent communication protocols (5 drafts)

Autonomous netops (46 drafts)

Data formats / semantics for AI interop (2 drafts)

  • draft-liu-agent-context-protocol (score: 3.5) — This draft proposes a standard protocol for AI agents to communicate context information to each oth
  • draft-narvaneni-agent-uri (score: 3.3) — This draft defines the agent:// URI scheme for addressing and interoperating with software agents ac

Data formats/interop (85 drafts)

Human-agent interaction (19 drafts)

Identity / authentication for AI agents (6 drafts)

ML traffic mgmt (30 drafts)

Model serving/inference (22 drafts)

Other AI/agent (8 drafts)

Policy / governance / ethical frameworks (1 drafts)

Policy/governance (63 drafts)

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