This draft proposes a networking architecture for AI agents in enterprise and broadband environments, defining core components and their interactions. The document appears to be an early framework for how agents communicate and coordinate within network infrastructures.

Useful contribution addressing agent networking needs, though agent-based architectures are becoming common

Single revision with recent date, no evidence of working group adoption or broader community engagement yet

Shares networking architecture concepts with existing agent communication drafts but focuses on enterprise/broadband context

Directly relevant to AI agents with focus on networking architecture and agent interactions

A2A protocols
Autonomous netops
Agent discovery/reg


This draft proposes an EPP extension allowing TLD registries to perform pre-registration verification of domain names using AI/ML techniques to assign abuse risk scores from 0-100. The extension aims to proactively identify potentially abusive domain registrations before they are completed.

Applies AI/ML scoring to existing EPP protocol for abuse detection - useful contribution combining established registry operations with modern ML approaches

Detailed EPP protocol extension with XML schemas, command definitions, data flows and security considerations - appears implementation-ready

While domain abuse detection exists, the specific EPP pre-registration extension approach with ML scoring appears relatively unique

Single draft from May 2024 with no apparent working group adoption or broader community discussion yet

Directly relevant as it specifies AI/ML integration into network protocols for automated decision-making in domain registration processes

A2A protocols
Policy/governance
Autonomous netops


This draft explores IPv6 networking considerations for AI agent communication, focusing on stable agent identifiers, forwarding policies, network context, and telemetry after discovery. It aims to complement existing agent protocols by addressing network-layer support rather than defining new discovery or collaboration mechanisms.

Useful contribution applying IPv6 concepts to AI agent networking, though builds incrementally on established networking principles

Early sketch stage with conceptual discussion but lacking detailed protocol specifications or implementation guidance

Minor overlap with general IPv6 and SRv6 work, but specific AI agent focus appears relatively unique

Single revision with future date suggesting early development phase, unclear community engagement

Directly relevant to AI agent networking infrastructure, addressing important networking layer considerations for agent communication

A2A protocols
Agent identity/auth
ML traffic mgmt


This draft specifies a two-party Private Set Intersection protocol using Elliptic Curve Diffie-Hellman to enable participants to find common records without revealing non-intersecting private data. The protocol instantiates the Meadows match-making protocol with standard elliptic curves and hash-to-curve methods for privacy-preserving record matching.

Incremental work applying well-known ECDH-based PSI techniques to IETF context - the underlying cryptographic approach is established

Detailed technical specification with 31 pages covering protocol mechanics, though uncertain about test vectors and implementation details

PSI protocols exist in academic literature and other standards bodies, shares core concepts with established cryptographic PSI work

Single draft submission with future date (2025-08-20) suggests early stage, no clear working group adoption signals

Privacy-preserving computation is tangentially related to AI/agent systems but this is primarily a general cryptographic protocol without specific AI focus


Proposes a dual-gate safety architecture for autonomous AI agents with a deterministic denylist (Gate 1) and stateless LLM judge (Gate 2) to prevent self-manipulation of safety rules. The architecture separates reasoning from safety judgment through a four-layer priority hierarchy.

Notable architectural insight of separating reasoning model from judging model to prevent conversational manipulation, with specific dual-gate implementation approach

Detailed specification with components, layer semantics, conflict resolution rules, trust models, and conformance requirements across 27 pages

While AI safety is a common concern, the specific dual-gate architecture with stateless judge separation appears distinct from existing approaches

Single draft from 2026 with no indication of working group adoption or community feedback yet

Directly addresses core AI agent safety concerns with action execution capabilities - fundamental to autonomous agent deployment

AI safety/alignment
Autonomous netops
Policy/governance


This draft explores multicast networking applications for Large Language Model inference services and synchronization. The document appears to be an early examination of how multicast protocols could optimize LLM deployment architectures in cloud environments.

Applying multicast to LLM inference represents a useful contribution addressing practical scaling challenges, though the core networking concepts are established

At 6 pages focusing on use cases, this appears to be an early sketch without detailed protocol specifications or implementation guidance

While networking optimization for ML workloads exists, the specific focus on multicast for LLM synchronization has minor overlap with existing approaches

Single draft submission with future date suggests early stage work without demonstrated community engagement or working group adoption

Directly addresses practical infrastructure concerns for LLM deployment and inference optimization, which is highly relevant to AI systems


This draft specifies a flow-level load balancing mechanism for Computing-Aware Traffic Steering (CATS) that reduces control plane overhead by enabling autonomous flow distribution in the data plane. The approach aims to improve resource utilization in computing-aware networks through distributed load balancing decisions.

Incremental extension of existing CATS framework with standard load balancing concepts applied to flow-level granularity

Defines specific protocol mechanism for flow-level load balancing with clear technical approach, though implementation details may need refinement

Significant conceptual overlap with existing CATS drafts and general load balancing mechanisms, primarily differentiating on flow-level granularity

Part of active CATS working group development with ongoing revisions and community engagement

Directly relevant to autonomous network operations and intelligent traffic management in computing-aware environments


Proposes BANDAID, a DNS-based discovery mechanism for AI agents using existing DNS infrastructure and SVCB records. Defines structured namespace conventions and metadata exchange to enable agent-to-agent discovery without requiring DNS protocol changes.

Useful application of existing DNS mechanisms to emerging AI agent discovery needs, though the core concept of DNS-based service discovery is well-established

Detailed specification with clear namespace conventions, record formats, and operational procedures, appears implementation-ready

While DNS service discovery exists, the specific application to AI agent discovery and the BANDAID framework appear relatively unique

Single revision from October 2025 with no clear working group adoption or community discussion visible

Directly addresses AI agent infrastructure needs, specifically discovery and capability advertisement between autonomous agents

Agent discovery/reg
A2A protocols
Agent identity/auth


This draft proposes an integrated security framework for 5G networks that uses Intent-Based Networking (IBN) to translate high-level security intents into enforceable policies via the I2NSF architecture. The system supports adaptive policy enforcement across 5G core components and user equipment with closed-loop monitoring for autonomous security management.

Useful integration of existing I2NSF and IBN concepts for 5G security, but combines known technologies rather than introducing fundamentally new approaches

Presents a defined framework with clear architecture components and policy translation mechanisms, though lacks detailed protocol specifications and implementation examples

Shares concepts with existing I2NSF and IBN drafts, as well as 5G security frameworks, but focuses on their specific integration for edge security

Appears to be an early revision with limited evidence of working group adoption or broader community engagement in OPSAWG

Directly relevant to autonomous network operations through IBN controllers that automatically translate intents to policies and provide closed-loop security management


This draft proposes technical mechanisms for AI agent communication in enterprise environments, including agent identification, registration, capability discovery, and semantic routing. It focuses on enabling trusted access and efficient collaboration among heterogeneous agents at campus scale.

Incremental application of existing networking concepts like service discovery and routing to AI agents in enterprise settings

Early sketch with proposed mechanisms but lacks detailed protocol specifications, message formats, or implementation guidance

Significant conceptual overlap with existing service discovery, identity management, and enterprise networking drafts, just applied to AI agents

Single revision draft with no evidence of working group adoption or broader community engagement

Directly addresses AI agent networking and communication challenges, though focused on enterprise deployment rather than broader internet scenarios

A2A protocols
Agent identity/auth
Agent discovery/reg


This IETF draft proposes an algorithm for DNS resolvers to revalidate NS record delegations by querying child zones directly and periodically refreshing delegation information. It aims to improve DNS resolution reliability by ensuring resolvers use the most authoritative and up-to-date nameserver information.

Incremental improvement to existing DNS resolution practices, formalizing what some resolvers already do informally

Active IETF working group draft with clear community interest in DNS operational improvements

Shares concepts with existing DNS operations drafts but addresses a specific gap in delegation handling

This is a DNS infrastructure specification with no relation to AI or autonomous agents


This draft defines EAT Attestation Result (EAR) message format for verifiers to encode appraisal results of attester evidence using AR4SI's trustworthiness vectors. EAR provides normalized evaluation results and contextual information to help relying parties make authorization decisions, supporting both simple and composite devices with CWT/JWT serialization.

Useful contribution that extends existing EAT framework with standardized result format and trustworthiness vectors

Detailed specification with clear message format definitions, examples, and support for both CWT and JWT serialization

Builds on existing EAT and AR4SI work, shares attestation concepts with related RATS WG drafts

Active development in RATS working group with clear progression toward standardization

Generic attestation and trust framework not specifically designed for AI agents, though could potentially be applied to AI system verification


Defines HTTP status code 427 (Budget Required) and Budget authentication scheme for software agents authorized to spend money or resources. Uses post-quantum signatures (ML-DSA-87) to provide cryptographic attestations of spending authorization separate from basic authentication.

Novel separation of authentication vs budget authorization concerns with post-quantum cryptographic attestations for agent spending

Detailed specification with concrete status codes, CBOR encoding, signature algorithms, and version negotiation mechanisms

Some conceptual similarity to HTTP payment protocols but unique approach combining agent authorization with post-quantum cryptography

Single draft from 2026 with no apparent working group adoption or community discussion

Directly addresses core challenge of autonomous agent resource authorization and spending controls with practical HTTP integration

Agent identity/auth
A2A protocols
Policy/governance


This draft proposes extended per-hop BGP metrics to enable more sophisticated routing policies that are common in practice but currently require complex configurations. It supplements BGP-4 by adding globally significant metrics beyond the current limited set.

Incremental improvement to BGP routing metrics - addresses practical operator needs but follows established patterns of BGP extensions

Defines the protocol mechanism with clear problem statement and proposed solution, though may lack detailed implementation specifics

Likely shares concepts with other BGP extension drafts and traffic engineering proposals, but appears to have a specific focus on per-hop metrics

IETF IDR working group adoption suggests active development and community interest in BGP routing improvements

This is a traditional BGP routing protocol extension with no apparent connection to AI, agents, or machine learning applications


This draft extends OAuth 2.1 with an Agent Authorization Grant for AI agents and software with persistent identities to obtain user consent and access tokens through polling, SSE, or WebSocket mechanisms. It builds on the OAuth Device Authorization Grant but adds agent-specific features like natural language descriptions and reasoning parameters.

Useful adaptation of existing OAuth patterns for agent use cases with meaningful extensions like natural language consent descriptions

Early sketch stage - only 6 pages with basic protocol outline but lacks detailed specification, examples, and security considerations

Significant overlap with RFC 8628 Device Authorization Grant and general OAuth 2.1 framework, essentially a specialized variant

Single draft from 2025 with no indication of working group adoption or community engagement

Directly addresses agent authentication needs - a core requirement for AI agent systems interacting with protected resources

Agent identity/auth
Human-agent interaction


IntelliNode introduces an in-network intelligent scheduling mechanism for CATS that uses FPGAs and programmable switches to perform real-time tensor-aware traffic steering for AI workloads. It creates a data-plane closed loop for perception-inference-decision-execution to optimize distributed AI training and inference through line-rate feature extraction and lightweight prediction models.

Notable integration of tensor semantics awareness into network data plane with FPGA-assisted real-time inference - goes beyond typical traffic steering

Early sketch with high-level architecture description but lacks detailed protocol specifications, message formats, or concrete implementation guidance

Shares CATS framework foundation and some traffic management concepts with existing work, but tensor-aware data-plane approach is distinct

Single revision draft from 2026 with no visible WG adoption or community discussion yet

Directly addresses AI workload optimization through intelligent network scheduling, highly relevant to AI agent infrastructure needs


This draft identifies requirements for differentiated security services in networks and explores trustworthiness-based routing as a solution. It focuses on extending current QoE/SLA guarantees to include security differentiation for various network scenarios.

Incremental extension of existing QoS/SLA concepts to security domain, but trustworthiness-based routing adds some novelty

Pure problem statement document with high-level discussion, no concrete protocols or mechanisms defined

Shares concepts with security-aware routing and QoS differentiation work, but specific trustworthiness angle is less common

Single draft from July 2024 with no visible follow-up or working group adoption

Mentions trustworthiness concepts that could apply to AI systems, but primarily focused on traditional network security


This draft defines a canonical CBOR-based representation for describing and distributing EDHOC application profiles to enable interoperability. It provides coordination mechanisms for profile use and defines a set of well-known profiles for the lightweight authenticated key exchange protocol.

Incremental work standardizing profile formats and coordination for existing EDHOC protocol

Detailed specification with CBOR representations, coordination mechanisms, and concrete well-known profiles

Shares profile standardization concepts with other authentication and key exchange protocols

IETF working group draft with strong industry interest in lightweight authentication for IoT

Authentication protocol standardization only tangentially related to AI agents through potential IoT device authentication


Defines Agent Authorization Envelope (AAE), a structured authorization container with MANDATE, CONSTRAINTS, and VALIDITY blocks for autonomous AI agents, using W3C DIDs and VCs. Addresses the gap where existing OAuth/API key mechanisms lack machine-evaluable semantics for agent actions without direct human oversight.

Notable originality in defining machine-evaluable authorization semantics specifically for autonomous agents with structured mandate/constraint blocks

Detailed specification with clear block structure, protocol bindings to W3C standards, and comprehensive coverage of authorization semantics

Minor similarities to general authorization frameworks but unique focus on autonomous agent-specific authorization containers

Single revision draft without evident working group adoption or community momentum signals

Core AI agent topic directly addressing fundamental authorization challenges for production-scale autonomous agents

Agent identity/auth
Policy/governance
Data formats/interop


This draft outlines use cases and requirements for communication protocols that enable measurement agents deployed on network devices to interact and coordinate. It focuses on the protocol needs for AI-driven measurement agents to collect, share, and act on network performance data.

Incremental work applying existing agent communication concepts to network measurement domain

Early stage focusing on use cases and requirements rather than concrete protocol specifications

Shares concepts with existing network measurement and agent communication work but targets specific measurement agent use case

Appears to be initial submission with limited evidence of broader community engagement

Directly relevant as it addresses AI agents in networking context with specific focus on measurement and monitoring

A2A protocols
Autonomous netops
Data formats/interop


This draft extends SCIM 2.0 to support identity management for AI agents and agentic applications. It proposes new resource types and schemas to enable interoperability between identity providers and agent systems.

Applies existing identity management framework to agents - useful but incremental extension

Early sketch with basic problem statement and approach, lacks detailed protocol specifications

Some conceptual overlap with general identity management but unique focus on agent identity

Future date (2026) suggests early/speculative work with limited current activity

Directly addresses agent identity management, a key infrastructure need for AI systems

Agent identity/auth
Data formats/interop


```json
{
  "s": "CoSERV defines a structured query/result format for discovering and retrieving endorsements and reference values in remote attestation systems. It uses CDDL and CBOR serialization to enable efficient interoperability across diverse attestation infrastructure.",
  "n": 3,
  "nn": "Addresses a specific gap in RATS architecture with a structured approach, though query languages for resource discovery are well-established concepts",
  "m": 4,
  "mn": "Detailed specification with CDDL definitions and CBOR serialization format, appears implementation-ready",
  "o": 2,
  "on": "Builds on RATS architecture but appears to be the first to tackle this specific endorsement/reference value discovery problem",
  "mo": 3,
  "mon": "Active development with recent publication date, fits into established RATS working group ecosystem",
  "r": 2,
  "rn": "Remote attestation infrastructure that could support AI agents but not specifically focused on AI/agent systems",
  "c": ["Agent identity/auth", "Data formats/interop"]
}
```


Clawdentity defines a cryptographic identity and trust protocol specifically for AI agent-to-agent communication, featuring Ed25519 identities, registry-issued credentials, and authenticated relay transport. The protocol enables agents to prove identity and establish bilateral trust through pairing ceremonies without exposing private infrastructure.

Notable originality in designing identity protocols specifically for AI agent communication patterns, addressing unique challenges of agent-to-agent trust establishment

Detailed specification with specific cryptographic primitives, protocol flows, and transport mechanisms, appears implementation-ready

Minor similarities to general identity protocols but unique focus on AI agent communication scenarios with specialized pairing ceremonies

Single draft from 2026 with no indication of working group adoption or community momentum

Core AI agent topic directly addressing fundamental identity and trust challenges in agent-to-agent communication


This draft explores applying existing IETF YANG data models (AC, TE Topology, SR Policy, QoS) to support dynamic UCMP policy enforcement for AI inference traffic between distributed edge data centers. It serves as a practical evaluation of current IETF specifications for cloud-initiated, time-bounded network policy coordination.

Incremental application of existing YANG models to AI inference use case, not introducing new technical approaches

Defines specific application of existing models with clear use case but appears to be more analysis than implementation-ready specification

Shares concepts with existing YANG model drafts and traffic engineering work, but specific AI inference application has some uniqueness

Single revision with specific author, unclear WG involvement or broader community engagement

Directly addresses AI inference traffic management needs with specific focus on distributed AI workloads and dynamic network policy coordination

ML traffic mgmt
Autonomous netops
Model serving/inference


Proposes a dynamic authorization framework for AI agents based on intent and behavioral trustworthiness rather than static identity/roles. Aims to provide just-in-time permissions that decouple authorization policies from business operations.

Intent-based authorization is established but applying it specifically to AI agent interactions with behavioral trust components represents a useful contribution

At 6 pages this appears to be an early framework sketch without detailed protocol mechanisms or implementation specifics

Shares concepts with existing intent-based access control and zero-trust models, though AI agent focus provides some differentiation

Single revision with recent date suggests early stage work without demonstrated community engagement

Directly addresses core AI agent authorization challenges, highly relevant to agent-to-agent interaction security

Agent identity/auth
A2A protocols
AI safety/alignment


This draft defines standardized attestation result information elements that allow relying parties to evaluate the trustworthiness of attesters in heterogeneous environments. It provides CBOR and JSON serializations for consistent policy application across different attester and verifier types.

Useful contribution to standardizing attestation results format, though builds on established RATS architecture concepts

Detailed specification with concrete information model and two serialization formats, appears implementation-ready

Shares concepts with existing RATS framework drafts but focuses specifically on attestation result standardization

IETF working group draft with clear progression toward standardization in established RATS WG

General attestation and trust framework - tangentially related to AI agents through trust establishment but not AI-specific


VOLT defines a tamper-evident logging protocol for AI agent operations using cryptographic hash chains and portable Evidence Bundles. It functions as a 'flight recorder' capturing agent workflow events with integrity guarantees while supporting privacy through content-addressed references and explicit redaction.

Novel application of cryptographic audit trails specifically designed for AI agent workflows with privacy-first design and portable evidence bundles

Detailed protocol specification at 40 pages with defined formats, hash chain mechanisms, and evidence bundle structure appears implementation-ready

While audit logging exists generally, the specific combination of hash chains, privacy preservation, and AI agent workflow tracing appears relatively unique

Single draft from 2026 with no clear indication of working group adoption or broader community engagement yet

Directly addresses critical AI agent accountability and auditability needs with practical cryptographic integrity mechanisms for agent operations

AI safety/alignment
Data formats/interop
Policy/governance


This draft proposes Virtual Aggregation (VA) for FIB suppression to address routing table growth by selectively not loading certain RIB entries into the FIB. The approach allows ISPs to reduce FIB size by an order of magnitude while maintaining compatibility with existing infrastructure.

Useful contribution addressing practical FIB scalability challenges with a systematic suppression approach

Detailed specification with clear mechanisms, examples, and deployment considerations

Some conceptual overlap with route aggregation techniques but distinct FIB-focused approach

Active IETF GROW working group draft with clear industry problem focus

Traditional network routing optimization, not related to AI or autonomous agents


This draft identifies the problem of establishing verifiable connections between software agents and the humans who authorize them, highlighting gaps in current identity systems for agent delegation and provenance. It frames the need for human-anchored agent identity without proposing specific solutions.

Useful contribution identifying an emerging problem space as agent ecosystems proliferate, though the core identity delegation concepts exist

Pure problem statement with no proposed mechanisms, protocols, or architectural solutions

Some conceptual overlap with general identity delegation work but focused specifically on human-agent relationships

Single revision from April 2026 with no indication of working group interest or follow-up activity

Directly addresses core AI agent infrastructure concerns around identity, delegation, and human oversight

Agent identity/auth
Human-agent interaction
Policy/governance


Proposes a PKI-inspired infrastructure for cryptographically attesting the provenance of data sources consumed by AI agents through tool calls, addressing 'semantic laundering' where data acquires unwarranted trust from interface appearance. Defines Warrant Certificate Authorities (WCA) with graduated attestation levels and reference monitor properties for complete mediation of agent-tool interactions.

Novel application of PKI trust models to AI agent provenance with original concept of 'semantic laundering' and graduated Warrant Attestation Levels

Future-dated draft (2026) suggests early stage with no apparent working group activity or community adoption yet

Draws from established PKI and provenance concepts but applies them uniquely to AI agent context with minimal direct overlap

Directly addresses core AI agent challenge of data provenance and trust in tool-call chains, highly relevant to agent security architecture

Agent identity/auth
AI safety/alignment
Policy/governance
Data formats/interop


This document proposes an inter-domain SAVNET architecture for AS-level Source Address Validation (SAV) that enables autonomous systems to share SAV-specific information to generate more accurate validation rules. The architecture provides a framework for ASes to autonomously determine and exchange information needed for source address validation across domain boundaries.

Useful contribution to inter-domain source address validation with structured information sharing approach

Early architectural sketch with component definitions but lacks detailed protocol specifications

Shares concepts with existing SAV and inter-domain routing security work but focuses specifically on information sharing architecture

Active development as indicated by recent date, part of ongoing SAVNET working group efforts

Partially relevant as it involves autonomous decision-making by network systems for security validation, though not traditional AI agents


This draft establishes standardized benchmarking methodologies for evaluating Ethernet-based AI inference serving network fabrics, focusing on disaggregated LLM architectures. It defines KPIs and test procedures for measuring fabric performance under realistic AI inference workloads including RDMA-based operations and MoE communication patterns.

Useful contribution applying established benchmarking principles to emerging AI inference fabric requirements, with some novel considerations for disaggregated architectures

Detailed specification with comprehensive test procedures, KPIs, and methodology covering multiple fabric scenarios though implementation examples could be enhanced

Minor conceptual overlap with general network benchmarking standards but addresses unique AI inference fabric requirements not covered elsewhere

Active development as evidenced by 2026 date and companion training draft, though community adoption level unclear from abstract

Directly relevant to AI infrastructure with focus on LLM inference performance measurement, though more infrastructure than agent-focused


Proposes an architecture for auditing AI agent interactions on the Internet by linking intent, delegation, authorization, and execution through distributed audit records. The framework addresses gaps in existing auditing mechanisms that capture isolated events but miss delegation relationships and user intent in agent-driven systems.

Novel approach to agent auditing that specifically addresses delegation chains and intent propagation, going beyond traditional system logging

Single draft submission with no apparent working group adoption or community discussion yet

Some conceptual overlap with general audit logging standards but unique focus on agent delegation and intent tracking

Directly addresses core AI agent challenges around accountability, delegation, and auditable autonomous actions on the Internet

Agent identity/auth
Policy/governance
A2A protocols


EDHOC defines a compact authenticated Diffie-Hellman key exchange protocol optimized for constrained IoT environments. It provides mutual authentication, forward secrecy, and identity protection while reusing existing COSE, CBOR, and CoAP standards to minimize code footprint.

Incremental improvement combining existing cryptographic primitives and encoding standards for IoT constraints

Comprehensive 113-page specification with detailed protocol flows, message formats, cryptographic operations, and security considerations - implementation ready

Shares key exchange concepts with TLS/DTLS and other authenticated DH protocols, but specific combination for constrained environments is distinctive

IETF working group draft with strong industry backing for IoT security, addressing real deployment needs in constrained networks

Generic cryptographic protocol for IoT devices - only tangentially related to AI agents through potential use in securing agent communications


IPP defines a cryptographic protocol for preserving verified human intent through chains of AI agent actions using signed tokens with lineage tracking. The protocol addresses accountability gaps in autonomous AI systems by creating tamper-evident audit trails from human principals to terminal agent actions.

Notable originality in applying cryptographic provenance specifically to human intent preservation in AI agent chains, with novel narrowing invariant concept

Well-defined protocol with clear properties and mechanisms, but lacks detailed implementation specifics and test vectors

Minor conceptual overlap with general provenance and chain-of-custody protocols, but distinct focus on AI agent intent tracking

Single revision draft with no apparent working group adoption or community feedback yet

Directly addresses core AI agent challenges around accountability, compliance, and human-AI interaction in enterprise environments

Agent identity/auth
Human-agent interaction
Policy/governance
A2A protocols


Proposes TTTPS protocol that extends TLS 1.3 with cryptographically verifiable temporal ordering to prevent manipulation by strategic channel controllers. Introduces Proof-of-Time mechanism specifically designed to address ordering attacks that are particularly acute for autonomous AI agents with symmetric reaction times.

Novel approach combining temporal ordering proofs with TLS extension, addressing previously unformalized Strategic Channel Controller Problem with mathematical foundation

Defined protocol with GRG integrity pipeline and AdaptiveSwitch mechanism, includes empirical validation but specification appears incomplete (truncated)

Some conceptual overlap with consensus protocols and timestamping services, but unique TLS integration and AI agent focus

Active development evidenced by deployment testing and empirical data collection, though experimental status indicates early adoption phase

Directly addresses critical infrastructure problem for AI agent economies where ordering manipulation becomes economically strategic

A2A protocols
AI safety/alignment
Autonomous netops


This draft analyzes gaps in existing networking and AI agent protocols for cross-device communication between AI agents deployed on network devices. It evaluates classical networking protocols and popular AI agent communication methods to identify what's missing for agent collaboration in network operations.

Incremental gap analysis work that applies existing protocol evaluation methodology to the emerging domain of AI agent networking

Early stage gap analysis document that identifies problems but doesn't propose specific solutions or detailed mechanisms

Shares concepts with related cross-device communication framework draft and general AI agent networking approaches

Single draft with future date suggests early development stage, unclear working group engagement or broader community interest

Directly addresses AI agent communication protocols in network infrastructure, highly relevant to autonomous network operations


This draft proposes Network Management Agents (NMA) as AI-driven entities that provide autonomous Level 4 network operations by adding cognitive capabilities like intent reasoning and planning on top of existing SDN controllers. It defines architectural integration modes and interface requirements for SDN-NMA interoperability.

Useful contribution combining AI agents with SDN architecture, though builds incrementally on established autonomous network concepts

Defines clear concepts and architecture with integration modes, but lacks detailed protocol specifications and implementation examples

Shares concepts with autonomous network management and AI-driven networking drafts, but offers distinct SDN integration approach

Early stage draft with single revision, no clear working group adoption signals yet

Core AI agent topic directly addressing autonomous network operations with cognitive AI capabilities

Autonomous netops
A2A protocols
Data formats/interop


This draft proposes a standardized vocabulary for machine-readable opt-outs from AI training and text/data mining, defining three key use cases (TDM, AI Training, Generative AI Training) that can be referenced by opt-out systems. The vocabulary is mechanism-agnostic and aims to enable consistent interpretation of content usage restrictions across different implementations.

Incremental work creating a simple taxonomy for existing opt-out concepts rather than introducing fundamentally new approaches

Early sketch defining basic vocabulary terms but lacking detailed protocol mechanisms, implementation guidance, or concrete examples

Significant conceptual overlap with robots.txt, Creative Commons licensing, and other existing content usage restriction mechanisms

Single revision with no apparent working group adoption or broader community engagement

Directly addresses AI training data governance, a core concern for AI systems and content creators seeking to control AI model training


SIRP proposes a semantic inference routing protocol that analyzes the content of AI inference requests to make intelligent routing decisions, rather than relying solely on client metadata. The framework includes value-added routing extensions for cost optimization, urgency handling, domain specialization, and privacy considerations.

Content-level semantic analysis for AI inference routing represents notable originality beyond traditional network routing approaches

Defines a complete protocol framework with semantic classification and routing mechanisms, though implementation details may need refinement

While AI inference serving exists, semantic content-based routing with VAR extensions shows minor similarities to existing work

Active development indicated by 2026 date, though community adoption and WG interest unclear from available information

Directly addresses core AI agent infrastructure needs for intelligent request routing and inference optimization

Model serving/inference
ML traffic mgmt
A2A protocols


This draft defines a lightweight DNS error reporting mechanism where validating resolvers automatically signal DNS resolution/validation failures to monitoring agents specified by authoritative servers. The error reporting uses extended DNS errors from RFC8914 and encodes the error information directly in the QNAME of the query itself.

Incremental improvement building on existing RFC8914 extended DNS errors, adding automated reporting capability

Well-defined protocol with clear mechanisms, message formats, and operational considerations though lacks detailed implementation examples

Shares DNS operational monitoring concepts with other DNS management drafts but has distinct automated error reporting approach

Active IETF dnsop working group draft with recent updates showing ongoing development interest

Pure DNS operational protocol for error reporting between DNS infrastructure components, not related to AI agents or autonomous systems


AINS defines a protocol for discovering and establishing trust between autonomous agents (AI, devices, humans, services) using structured metadata records and cryptographic trust scores. Unlike DNS which maps names to addresses, AINS maps agent identifiers to rich capability and trust metadata objects with federated replication logs.

Notable approach combining agent discovery with cryptographic trust resolution in a federated system, going beyond traditional naming services

Detailed 34-page specification with structured record formats, HTTPS-based resolution protocol, and federated replication mechanism

While building on DNS concepts, the agent-focused metadata and trust resolution represents a distinct approach with minor similarities to existing naming systems

Single draft with future date (2026-03-29) and references to multiple companion protocols that may not exist yet

Core AI agent infrastructure addressing fundamental needs for agent discovery, identification, and trust establishment in heterogeneous networks

Agent discovery/reg
Agent identity/auth
A2A protocols


This draft extends OAuth 2.0 Token Exchange with six profiles for preserving and cryptographically validating delegation chains across multi-hop service-to-service and agentic workflows. It addresses the gap where RFC 8693 allows nested actor claims but doesn't define how delegation paths are maintained and verified across successive token exchanges.

Notable originality in defining comprehensive cryptographically verifiable delegation chain profiles for multi-hop workflows, addressing a real gap in OAuth 2.0 Token Exchange

At 97 pages with six detailed profiles covering different disclosure and verification approaches, this appears to be a comprehensive specification with substantial technical detail

While building on OAuth 2.0 and Token Exchange foundations, the specific actor chain verification mechanisms and multi-hop delegation profiles appear relatively unique

Future-dated draft (2026-05-01) suggests this may be early-stage work, though the comprehensive nature indicates significant development effort

Directly relevant to AI agent authentication and authorization, particularly for multi-hop agentic workflows where delegation path integrity is crucial for security and accountability


This IETF draft proposes AI Network Traffic Optimization Agents as dynamic alternatives to static network optimization, covering use cases for tunnel adjustment, traffic steering, and network slice management. The document defines basic concepts and outlines three key application scenarios where autonomous AI agents can enhance network efficiency and reduce manual operations.

Incremental application of well-known AI concepts to standard network optimization problems without novel technical approaches

Early sketch that defines basic concepts and use cases but lacks detailed protocols, mechanisms, or implementation specifics

Significant conceptual overlap with existing network automation and AI-driven traffic management work in standards bodies

Single revision with future date suggests early-stage work without demonstrated community engagement or working group adoption

Directly relevant to AI agents in networking context, focusing specifically on autonomous network traffic optimization scenarios


This draft defines Service Intent for Autonomic Networks, providing a structured semantic model to express service-level objectives and constraints across network, compute, and storage resources for emerging services like AI inference. It extends ANIMA Intent with service-oriented rather than connection-centric descriptions.

Useful contribution extending ANIMA Intent to service-level objectives, particularly relevant for AI services, though builds incrementally on existing autonomic networking concepts

Appears to define a concrete protocol mechanism with structured semantics, identification, and lifecycle management, though implementation details may need further elaboration

Shares concepts with existing ANIMA Intent work and general service orchestration approaches, but appears to have distinct focus on cross-resource service objectives

Single revision from 2026 with no indication of working group adoption or broader community engagement yet

Directly relevant to AI agent operations by addressing service intent for AI inference workloads and autonomic network management of AI services

Autonomous netops
Model serving/inference
Policy/governance


This draft specifies extensions to the OpenAI API for multi-provider distributed AI inference, enabling provider diversity, load balancing, and failover while maintaining backward compatibility. The revision adds identity-based authorization, RBAC, and rate limiting for secure multi-tenant deployments.

Useful contribution applying multi-provider orchestration patterns to AI inference APIs, though the concept of API federation and load balancing is well-established in other domains

Detailed specification with concrete extensions, security mechanisms, and multi-tenant considerations spanning 49 pages, indicating implementation-ready detail

Shares architectural concepts with API gateway and service mesh approaches, and overlaps with other AI inference management proposals

Single revision (-01) with incremental updates, no clear indication of working group adoption or broader community engagement

Core AI agent infrastructure topic addressing critical needs for provider diversity, scalability, and vendor independence in AI inference deployments

Model serving/inference
Agent identity/auth
Data formats/interop


Proposes Symmetry-Driven Asynchronous Forwarding (SDAF) for LEO satellite networks to address routing black holes and micro-loops by leveraging toroidal topology symmetry for microsecond-scale convergence without control-plane coordination. The mechanism is designed for resource-constrained satellites and claims compatibility with existing protocols like OSPF and IS-IS.

Novel application of topology symmetry for autonomous forwarding decisions in satellite networks, representing a creative departure from traditional IGP-based approaches

Well-defined mechanism with detailed protocol description, simulation results, and actual satellite router testing, though some implementation details could be more comprehensive

While fast reroute mechanisms exist, the symmetry-driven approach for satellite networks appears to have minimal overlap with existing IETF work

Single draft submission without clear working group adoption or broader community engagement visible

Partially relevant as it involves autonomous network behavior and self-healing mechanisms, though focused on networking rather than AI agents specifically


This draft proposes an Agent Collaboration Protocols (ACP) architecture for Internet of Agents, defining conceptual domains and functional components for agent interconnection. It covers agent registration, authentication, discovery, interaction, tool invocation, and monitoring to support large-scale agent collaboration.

Useful architectural framework for agent collaboration, though builds on established distributed systems and multi-agent concepts

Early architectural sketch with high-level components but lacks detailed protocol specifications, message formats, or concrete examples

Shares conceptual overlap with existing multi-agent system architectures and distributed computing frameworks, but specific to IoT agent context

Single revision draft with no indication of working group adoption or broader community engagement

Directly addresses core AI agent networking challenges including discovery, authentication, and collaboration protocols

A2A protocols
Agent identity/auth
Agent discovery/reg


This draft proposes using MOQ Transport (MOQT) as a unified substrate for inter-agent communication protocols by providing an abstraction layer that maps request-response and streaming patterns to MOQT's publish/subscribe model. The approach is demonstrated with two protocols: Agent-to-Agent (A2A) for collaboration and Model Context Protocol (MCP) for tool access.

Useful contribution applying existing MOQT to agent communication domain, though the core concept of protocol abstraction layers is well-established

Defines the protocol abstraction and mapping mechanisms with concrete examples for A2A and MCP protocols, but lacks implementation details

Minor overlap with general transport protocol work, but unique focus on agent-specific communication patterns over MOQT

Single revision with future date suggests early stage, unclear community engagement or working group interest

Directly addresses inter-agent communication infrastructure, a core requirement for AI agent ecosystems and multi-agent systems

A2A protocols
Data formats/interop
Agent discovery/reg


```json
{
  "s": "PLPES defines a structured framework for standardizing AI prompts with machine-readable descriptors, classification taxonomy, versioning, and security requirements. It addresses the lack of interoperability standards and integrity mechanisms for prompts in enterprise and infrastructure AI deployments.",
  "n": 4,
  "nn": "Novel approach to treating prompts as protocol artifacts requiring formal specification - this is a genuinely new perspective on prompt engineering standardization",
  "m": 4,
  "mn": "Detailed specification with concrete PDO structure, five-tier taxonomy, versioning model, and security requirements - appears implementation-ready",
  "o": 2,
  "on": "Minor conceptual overlap with general AI safety practices, but the protocol-layer approach to prompt standardization is largely unique",
  "mo": 1,
  "mon": "Single draft from 2026 with no visible community engagement or working group adoption yet",
  "r": 5,
  "rn": "Directly addresses core AI agent infrastructure needs for prompt standardization, security, and interoperability in enterprise deployments",
  "c": ["Data formats/interop", "AI safety/alignment", "Policy/governance"]
}
```


This draft defines a URI scheme 'llm://' for standardizing connections to Large Language Model endpoints, encoding provider, model, credentials, and parameters in a single string similar to database connection URIs. The scheme aims to provide portable, standardized identification of LLM services across different providers and deployment scenarios.

Useful standardization of LLM endpoint addressing, though conceptually straightforward adaptation of existing URI patterns to AI services

Well-detailed specification with clear syntax definitions, parameter encoding, and practical examples for implementation

Novel application domain with minimal overlap, though builds on established URI scheme design principles

Single draft submission with no clear working group adoption or community discussion visible

Directly addresses practical interoperability needs in AI agent and LLM integration scenarios

Model serving/inference
Data formats/interop
Agent discovery/reg


This draft proposes using RPKI to authenticate parties in OpenID Connect flows for peering APIs, binding authentication to ASN operators. It addresses the lack of standardized authentication in the GROW Working Group's Peering API discussions.

Useful contribution combining RPKI trust anchoring with OIDC for network operator authentication - not obvious but builds on existing technologies

Early sketch stage with basic concept outlined but lacking detailed protocol mechanisms, implementation specifics, or comprehensive security analysis

Minor conceptual similarities to general identity attestation work but unique application of RPKI to OIDC for ASN-based authentication

Active development evidenced by recent date and GitHub repository, appears to be responding to ongoing GROW WG discussions

Directly relevant to agent identity and authentication in network automation contexts where ASN-bound agents need trusted identity attestation


```json
{
  "s": "This draft proposes the PTV protocol for hardware-anchored attestation of AI agent identity, allowing agents to prove they're running authorized models without exposing sensitive data. The protocol defines CBOR message formats and composes with existing RATS attestation mechanisms for critical applications like healthcare and industrial control systems.",
  "n": 4,
  "nn": "Novel application of hardware attestation specifically to AI agent identity with separation of identity binding from behavioral continuity - addresses a gap in existing attestation frameworks",
  "m": 3,
  "mn": "Defines protocol structure, message types, and threat model but lacks detailed specification and implementation guidance for a complete solution",
  "o": 2,
  "on": "Builds on RATS attestation concepts but applies them specifically to AI agents in a novel way with minimal direct overlap",
  "mo": 2,
  "mon": "Recent draft date suggests early stage work without evidence of working group adoption or broader community engagement",
  "r": 5,
  "rn": "Directly addresses core AI agent security concerns around identity verification and model authorization - highly relevant to AI agent infrastructure",
  "c": ["Agent identity/auth", "AI safety/alignment", "Policy/governance"]
}
```


TCT Protocol defines an HTTP-based method for efficient delivery of web content to automated agents using JSON envelopes that encapsulate resources in text formats like Markdown. It employs bidirectional URL discovery, JSON sitemaps, and conditional requests to optimize bandwidth while preserving semantic structure.

Useful contribution combining existing HTTP mechanisms (ETags, conditional requests) with structured content delivery for agents, though individual components are not novel

Detailed specification at 48 pages with defined protocol mechanisms, JSON schemas, and URL discovery patterns ready for implementation

Shares concepts with web content syndication formats and HTTP optimization techniques, but specific combination for agent content delivery is distinct

Single author draft without clear working group adoption or broader community engagement indicated

Directly addresses automated agent content consumption with structured data formats and efficient transfer mechanisms

Data formats/interop
Human-agent interaction
Agent discovery/reg


This document consolidates IPv6 Neighbor Discovery protocol issues from 20+ RFCs, identifying three root causes and proposing host isolation solutions. It provides guidance for selecting appropriate isolation methods to prevent ND security and operational problems in untrusted network environments.

Incremental consolidation work that organizes existing knowledge but doesn't introduce fundamentally new concepts

Well-developed analysis with detailed guidance and comprehensive coverage of existing solutions

Builds upon extensive prior work in IPv6 ND but provides unique consolidation perspective

Strong working group backing as IETF draft with clear operational need

Pure IPv6 networking protocol work with no AI or agent-related content


Proposes a framework for integrating LLM agents into network management systems with mandatory human oversight and approval loops. Defines telemetry enhancements, agent decision modules, and interaction data models to enable collaborative automation while maintaining human control.

Useful contribution applying LLMs to network management with human-in-the-loop design, though the general concept of AI-assisted network operations with human oversight is becoming common

Single revision draft from April 2026 with no clear indication of working group adoption or broader community engagement

Some conceptual overlap with general AI-assisted network management approaches but the specific human-in-the-loop framework design appears relatively unique

Directly addresses AI agents in network management with human oversight - highly relevant to autonomous network operations and human-agent interaction

Autonomous netops
Human-agent interaction
Data formats/interop


This draft compares six messaging protocols for agentic AI systems, analyzing their streaming performance, delivery guarantees, security models, and operational complexity. It proposes AGNTCY SLIM as a purpose-built solution combining MLS with gRPC over HTTP/2 for quantum-safe end-to-end encryption and dynamic group membership for collaborative AI agents.

Useful contribution applying existing messaging protocols to agentic AI context, though the core protocols and MLS integration are not novel

Single revision from future date suggests early stage work without demonstrated community engagement or working group adoption

Shares messaging infrastructure concepts with existing protocol drafts and MLS applications, but specific agentic AI focus is less common

Directly addresses core infrastructure needs for agentic AI systems including real-time collaboration, distributed coordination, and security requirements


This draft proposes enhancing ANIMA's Autonomic Service Agents (ASA) with AI/LLM capabilities to improve intelligent network management functions. The document focuses on analyzing the communication protocol implications when ASAs become AI-powered.

Incremental enhancement of existing ANIMA ASA framework with AI capabilities - straightforward application of LLMs to network automation

Early conceptual stage with analysis of impacts but lacks concrete protocol specifications or implementation details

Builds directly on established ANIMA/GRASP protocols while sharing AI-enhanced network automation concepts with other autonomous networking approaches

Recently published single revision with unclear working group adoption status or broader community engagement

Directly addresses AI-powered autonomous agents in networking context with focus on agent communication protocols

Autonomous netops
A2A protocols
Agent discovery/reg


Proposes a communication framework for multi-agent collaboration in network AIOps with AI gateway and Agent Name Service components. Aims to enable coordination of heterogeneous agents for intelligent network operations and maintenance.

Useful contribution applying multi-agent coordination concepts to network operations, though agent communication frameworks exist in other domains

Early sketch with high-level architecture description but lacks detailed protocol specifications, message formats, or concrete implementation details

Shares concepts with existing agent discovery and communication work, though specific focus on network AIOps is somewhat distinct

Single revision draft with no clear working group engagement or community feedback visible

Core AI agent topic directly addressing multi-agent coordination and communication for network operations

A2A protocols
Autonomous netops
Agent discovery/reg
Agent identity/auth


This draft identifies the problem of managing AI/LLM training and inference traffic across federated datacenters and edge locations. It focuses on the unique network characteristics and requirements of collective communication libraries used in distributed AI workloads.

Problem is becoming well-recognized in industry; incremental contribution to document it formally

Pure problem statement document with no proposed solutions or mechanisms

Shares concepts with general DC networking and ML infrastructure work, but specific AI traffic focus is somewhat distinct

Active area of interest given AI growth, but draft appears to be initial submission without established momentum

Directly addresses AI/ML infrastructure challenges, though more focused on network transport than agent behavior


This draft defines a structured metadata format for OAuth 2.0 First-Party Applications to enable interoperable authorization challenges for AI agents and non-browser clients. It extends the FiPA specification by adding an elicitations array to authorization challenge responses, using Model Context Protocol bindings for just-in-time agent authorization.

Useful contribution bridging OAuth 2.0 with AI agent authorization needs through structured metadata

Well-defined protocol extension with clear metadata format but lacks implementation examples

Minor similarities to existing OAuth extensions but addresses unique AI agent authorization gap

Single revision with 2026 date suggests early stage development

Directly addresses core AI agent authentication challenge in human-to-agent workflows

Agent identity/auth
Human-agent interaction
Data formats/interop


This draft analyzes structural vulnerabilities in ASRank, an algorithm for inferring AS business relationships from BGP data, showing how small input changes can manipulate inference results in adversarial conditions. The document identifies the algorithm's sensitivity issues and discusses security implications with potential countermeasures.

Useful contribution analyzing a specific vulnerability in widely-used BGP inference algorithms, though vulnerability analysis of existing systems is somewhat incremental

Defines the vulnerability problem and provides analysis with examples, but appears to be more of an analysis document than a protocol specification

Specific focus on ASRank vulnerabilities appears relatively unique, with only minor conceptual overlap with general BGP security work

Single draft from 2026 with no clear indication of working group adoption or broader community engagement

While analyzing algorithmic vulnerabilities, this is primarily about BGP routing inference rather than AI agents or autonomous systems in the AI context


This draft proposes updates to the robots.txt standard to better handle AI-specific crawlers by introducing new syntax for distinguishing between AI training and inference activities. It aims to give website owners more granular control over how AI systems access their content.

Incremental extension of existing robots.txt standard with AI-specific considerations

Early sketch - only 5 pages suggests limited detail on implementation specifics

Significant overlap with existing robots.txt standards and various AI crawler management proposals

Single revision with no evidence of working group adoption or community feedback

Directly relevant to AI agent governance and web crawling policy management

Policy/governance
Agent identity/auth
Data formats/interop


This draft defines EDHOC-AKA, combining the EDHOC key exchange protocol with AKA authentication for efficient mobile network access authentication. It targets resource-constrained environments like Non-Terrestrial Networks using symmetric cryptography and pre-shared keys.

Incremental combination of two existing protocols (EDHOC and AKA) for mobile authentication scenarios

Early specification with basic protocol definition but lacks detailed implementation specifics and test vectors

Significant overlap with existing EDHOC and AKA specifications, primarily combining established mechanisms

Single revision draft with no clear working group adoption or community feedback visible

This is a mobile network authentication protocol, not related to AI agents or artificial intelligence systems


This IETF draft defines an HTTP mechanism for content creators to signal preferences about how automated systems (like AI) may consume their content. It updates RFC 9309 to include usage preference signaling during content acquisition.

Useful contribution addressing a timely need for content usage preferences in the AI era, though the HTTP signaling approach is relatively straightforward

Detailed specification that updates an existing RFC with clear mechanisms for preference signaling in HTTP

Minor conceptual similarities to existing preference/policy mechanisms but addresses a distinct use case for AI content consumption

Strong community momentum as an active IETF working group draft addressing urgent AI governance needs

Directly relevant to AI agent interactions with web content, though focuses more on content creator preferences than agent behavior

Policy/governance
Data formats/interop
Human-agent interaction


This draft specifies a framework for Agent-to-Agent communication over the Media over QUIC (MoQ) protocol, introducing application-layer framing for control signaling and session management. It aims to enable structured, low-latency communication between autonomous agents with support for both intra-domain and inter-domain deployment.

Useful contribution applying existing MoQ transport to agent communication, but the core concept of agent-to-agent protocols over network transports is well-established

Early sketch phase - abstract indicates framework design but lacks detail on specific protocol mechanisms, message formats, or implementation considerations

Shares conceptual space with other agent communication protocols and MoQ application drafts, though specific combination appears distinct

Future-dated draft (2026) suggests this is speculative work with no current implementation or community engagement

Directly addresses core agent infrastructure needs for inter-agent communication, which is fundamental to multi-agent AI systems


This draft proposes using Media Over QUIC (MOQ) to transport large-scale, real-time sensing data in 6G networks, addressing challenges in autonomous driving, smart cities, and industrial IoT. The approach focuses on leveraging QUIC's low-latency and multiplexing capabilities for efficient data fragmentation, secure transmission, and end-to-end QoS in next-generation wireless networks.

Incremental application of existing MOQ protocol to sensing data transport; straightforward extension without novel protocol mechanisms

Single revision draft with no clear working group adoption or community engagement signals

Shares transport optimization concepts with other networking drafts, though specific focus on sensing data is somewhat distinct

Partially relevant - enables AI/autonomous systems through sensing data transport but is primarily a networking transport solution rather than core AI agent technology


This IETF draft provides implementation guidance for EDHOC, an authenticated key exchange protocol based on Ephemeral Diffie-Hellman Over COSE. The document offers practical considerations for developers implementing this cryptographic protocol in constrained environments.

Incremental contribution providing implementation guidance for existing EDHOC protocol

Detailed implementation considerations with specific guidance and examples

Builds directly on base EDHOC specification with some overlap on protocol details

Part of ongoing LAKE working group efforts with moderate activity

Pure cryptographic key exchange protocol, not related to AI or autonomous agents


Proposes a 'Reality Layer' framework to filter out synthetic noise from autonomous agents using deterministic admission control. Introduces the concept of Saturation Agentic Stridential (SAS) when agent-generated traffic overwhelms system capacity.

Novel framing of agent noise pollution as a systemic problem requiring protocol-level solutions, with creative terminology and O(1) reality checking approach

Early conceptual sketch with buzzwords like 'NIST-validated IRP-189' but lacks concrete protocol details, message formats, or implementation guidance

Unique problem formulation and solution approach, though shares general concerns about AI traffic management with other drafts

Single revision from 2026 with no indication of working group adoption or community feedback

Directly addresses core challenge of autonomous agent systems overwhelming network infrastructure with synthetic traffic

ML traffic mgmt
AI safety/alignment
Policy/governance


This draft extends the Model Context Protocol (MCP) JSON-RPC framework to enable network equipment to act as MCP servers for AI-driven network management. It defines capability tokens, tools, resources, and error codes to integrate network devices with AI controllers while maintaining backward compatibility.

Useful contribution applying existing MCP framework to network domain with appropriate extensions

Defines protocol extensions and mechanisms but lacks detailed implementation examples and test cases

Minor conceptual overlap with network management protocols but unique application of MCP to networking

Single revision with October 2025 date, no evidence of working group adoption or community engagement

Directly relevant to AI agents in network operations, enabling autonomous network management through standardized protocol


```json
{
  "s": "This draft analyzes requirements and enabling technologies for agent protocols specifically tailored to 6G mobile networks. It represents an early attempt to bridge AI agent communication protocols with telecommunications-specific performance and usage requirements.",
  "n": 3,
  "nn": "Useful contribution connecting agent protocols to 6G requirements, though the intersection of AI agents and telecom is becoming well-explored territory",
  "m": 2,
  "mn": "Early sketch phase - provides analysis and requirements but lacks concrete protocol definitions or detailed technical specifications",
  "o": 3,
  "on": "Shares concepts with other telecom AI integration work and general agent protocol discussions, but 6G-specific focus provides some differentiation",
  "mo": 3,
  "mon": "Active development with recent publication date, though momentum depends on 6G standardization timeline and telecom industry adoption",
  "r": 4,
  "rn": "Directly relevant as it focuses on agent protocols, though primarily from telecommunications infrastructure perspective rather than general agent-to-agent communication",
  "c": ["A2A protocols", "ML traffic mgmt", "Autonomous netops", "Policy/governance"]
}
```


This draft proposes a structured vocabulary for content rightsholders to express preferences about AI training use of their content through metadata and delivery protocols. The vocabulary covers permissions, usage scope, and data retention at various granularity levels to enable interoperability across Internet protocols.

Incremental extension of existing content preference mechanisms like robots.txt to the AI training domain

Early sketch stage with basic vocabulary outline but lacks detailed protocol specifications and implementation guidance

Shares conceptual ground with web crawling standards and content licensing frameworks, but addresses AI-specific concerns

Single revision draft without clear working group adoption or broad community engagement

Directly addresses the pressing need for AI training content governance and rightsholder control mechanisms


```json
{
  "s": "Defines Agent Identity Protocol (AIP) for verifiable AI agent authentication and policy enforcement through cryptographic identity and proxy-based access control. Addresses the problem of AI agents operating with unbounded permissions by creating identity boundaries between human and non-human actors.",
  "n": 4,
  "nn": "Notable originality in applying cryptographic identity specifically to AI agents with registry-based verification and policy enforcement proxy architecture",
  "m": 4,
  "mn": "Detailed specification with two-layer architecture, registry system, and enforcement mechanisms - appears implementation-ready",
  "o": 2,
  "on": "Minor similarities to general identity protocols but unique focus on AI agent-specific challenges and policy enforcement",
  "mo": 2,
  "mn": "Single revision dated 2026 suggests early stage development without established community momentum",
  "r": 5,
  "rn": "Core AI agent topic addressing fundamental authentication and authorization challenges for autonomous systems",
  "c": ["Agent identity/auth", "Policy/governance", "AI safety/alignment"]
}
```


Mangrove proposes a unified framework for creating comprehensive Internet topology models by aggregating diverse data sources including traceroute, BGP, and IP allocation records. The system uses hierarchical abstraction and incremental updates to provide real-time Internet path estimation and performance metrics for research and operational analysis.

Useful contribution combining existing measurement techniques into unified topology modeling, though individual components are well-established

Single draft submission with no clear working group adoption or community feedback visible

Significant conceptual overlap with existing Internet measurement platforms like CAIDA, RIPE Atlas, and various topology mapping efforts

Traditional Internet measurement and topology mapping system with no apparent AI, ML, or autonomous agent components


This draft proposes using AI for autonomous security and network management in B5G networks through SOAR (Security Orchestration Automation and Response) technology. The document aims to define requirements for intent-based network management to ensure constant security quality.

Incremental application of existing AI concepts (SOAR, intent-based networking) to B5G without novel technical contributions

Abstract-level problem statement with poor English and unclear technical details, lacks concrete protocols or mechanisms

Significant overlap with existing work on intent-based networking, AI-driven network management, and autonomous security systems

Single draft from 2024 with no evidence of working group adoption or community interest

Directly relevant to AI agents in network operations and autonomous systems, though poorly articulated


AIGA specifies a comprehensive governance protocol for autonomous AI agents using tiered risk management, federated authority networks, and economic incentive alignment. The protocol includes immutable kernel architecture, action-based authorization, and advanced attestation mechanisms for high-risk scenarios.

Novel integration of federated governance, immutable TCB architecture, and economic incentive alignment specifically for AI agent oversight - addresses unique challenges not covered by existing protocols

Detailed specification with concrete mechanisms like M-TACE, warden triumvirate, and quarantine protocols - appears implementation-ready though lacks test vectors

While governance concepts exist elsewhere, the specific combination of AI agent oversight, federated authorities, and immutable kernel architecture is largely unique

Single revision (draft-02) with no clear WG adoption - appears to be individual work without demonstrated community engagement

Directly addresses core AI agent governance challenges including autonomous operation, adversarial scenarios, and scalable oversight mechanisms

Policy/governance
Agent identity/auth
AI safety/alignment


This draft introduces a networking framework for AI agents in enterprise and broadband environments, defining core components and their interactions. The document appears to be a high-level architectural framework rather than a detailed technical specification.

Incremental work applying existing networking concepts to agent environments without significant novel insights

Early sketch level with basic framework definition but lacking detailed protocols, mechanisms, or implementation guidance

Shares general concepts with other agent networking and discovery drafts but appears to have distinct enterprise/broadband focus

Single revision with recent publication date, no evidence of working group adoption or broader community engagement yet

Directly addresses AI agent networking infrastructure, which is highly relevant to current AI deployment challenges

A2A protocols
Autonomous netops
Agent discovery/reg


This draft identifies requirements for multicast protocols in AI data centers, focusing on optimizing point-to-multipoint communications during large language model training and inference. It analyzes gaps between current multicast capabilities and the specific needs of AI workloads in data center environments.

Incremental application of existing multicast concepts to AI data centers - useful but not particularly novel

Early requirements and gap analysis document without detailed protocol mechanisms or specifications

Shares networking optimization concepts with other AI datacenter drafts but focuses specifically on multicast

Single draft submission with no clear working group adoption or broader community engagement

Partially relevant - addresses networking infrastructure for AI but not core agent-to-agent communication protocols


This draft defines a semantic layer for agent-to-agent interaction in an Internet of Agents using ontology models and JSON-LD serialization. It specifies classes, properties, negotiation procedures, and alignment rules for semantic interoperability across heterogeneous agent domains.

Useful contribution applying established semantic web technologies to agent interaction, though the core concepts of ontology-based interoperability are well-established

Appears to be early stage work without evidence of working group adoption or broader community engagement

While semantic interoperability has been explored, the specific application to Internet of Agents with JSON-LD profile appears relatively unique

Directly addresses core AI agent infrastructure needs for semantic interoperability and communication protocols


This draft inventories use cases for Agentic AI systems that interact across networks, identifying protocol requirements for agent-to-agent communication, tool integration, and multi-domain coordination. It aims to guide IETF standardization efforts by analyzing concrete needs like long-lived interactions, delegation patterns, and security considerations.

Useful contribution applying established use case methodology to emerging agentic AI domain, bridging AI capabilities with network protocol requirements

Early-stage document focused on use case collection and requirement identification rather than concrete protocol specifications

References emerging A2A and MCP protocols but appears to be first comprehensive use case analysis specifically for IETF protocol implications

Active development with recent date, addresses timely topic of agentic AI protocols, though WG adoption status unclear

Directly addresses core AI agent networking challenges including multi-agent coordination, tool interaction, and cross-domain operation

A2A protocols
Agent discovery/reg
Data formats/interop
Agent identity/auth
Policy/governance


This draft defines an ASIL-M profile for RATS that enables authorization decisions based on evidence from multiple independent trust roots, specifically targeting AI inference systems. It introduces three key components: an Evidence Synthesis Protocol, the Twin Attestation Policy Language (TAPL), and Canonical Attestation Records (CAR) for audit and replay verification.

Notable originality in addressing multi-root attestation synthesis for AI systems, combining evidence from independent trust anchors with deterministic policy evaluation

Single revision with future date suggests early stage development with uncertain community engagement

Minor similarities to existing RATS work but the multi-root synthesis approach and AI-specific application profile are relatively unique

Directly relevant to AI agent security and inference authorization, addressing critical attestation needs for AI system deployment

Agent identity/auth
AI safety/alignment
Model serving/inference


This draft extends an unspecified core protocol to provide granular server-side controls for automated interactions, including rate limiting, technology restrictions, and permission management. It aims to give service owners better control over how AI agents and automation systems interact with their services.

Incremental extension focusing on server-side automation controls - useful but not particularly original given existing rate limiting and permission frameworks

Early sketch stage - references undefined [CORE-SPEC], lacks concrete protocol details, mechanisms, or implementation guidance

Significant overlap with existing rate limiting protocols, API permission frameworks, and web automation control mechanisms like robots.txt

Appears inactive with undefined core specification reference and minimal development indicators

Directly relevant to AI agent interactions - addresses practical needs for controlling automated systems accessing web services

Policy/governance
Agent identity/auth
ML traffic mgmt


Defines a cryptographically-signed transaction protocol for agent-to-agent value exchanges that maps to existing payment infrastructure while providing legal defensibility under current contract law. Part of a comprehensive multi-draft framework for agentic commerce including escrow, dispute resolution, and agent identity verification.

Novel approach to bridging AI agent commerce with existing legal and financial infrastructure through canonical transaction bundles

Detailed 27-page specification with concrete rail mappings, conformance tiers, and integration with companion protocols

Unique focus on legally-defensible agent transactions, minimal overlap with existing payment or identity protocols

Active development with coordinated multi-draft submission suggesting sustained effort, but unclear WG adoption status

Directly addresses core challenge of autonomous agent commerce with practical legal and technical framework

A2A protocols
Agent identity/auth
Policy/governance


Specifies Intent Token, a cryptographic authorization primitive that binds autonomous AI agent actions to human-declared authorization envelopes before execution. Addresses gaps in existing OAuth/OIDC frameworks which handle session-level access but not action-level authorization for autonomous agents.

Notable originality in bridging human intent declaration with autonomous agent execution through cryptographic binding - addresses a genuine gap between existing session-based auth and action-level governance

Early sketch level - presents the concept and problem statement but likely lacks detailed protocol mechanics, message formats, and implementation specifics needed for a complete specification

Minor similarities to existing authorization frameworks but the specific focus on cryptographically binding human intent to autonomous agent actions appears relatively unique

Single revision from 2026 draft with no clear indication of working group adoption or broader community engagement yet

Core AI agent topic directly addressing fundamental authorization challenges for autonomous systems - highly relevant to AI agent infrastructure

Agent identity/auth
AI safety/alignment
Policy/governance


This draft specifies how to transport Model Context Protocol (MCP) messages over Media over QUIC Transport (MOQT) to enable efficient communication between AI language models and external data sources. It maps MCP's request-response and streaming patterns onto MOQT's publish-subscribe object delivery model over QUIC/WebTransport.

Incremental work combining two existing protocols - straightforward transport layer adaptation

Detailed 32-page specification with message mapping procedures and session management

Shares transport concepts with other QUIC-based protocols but specific MCP/MOQT combination is distinct

Single revision with specific October 2025 date, unclear adoption status

Directly relevant to AI agent infrastructure - addresses core transport needs for model-external system integration

A2A protocols
Data formats/interop
Model serving/inference


Proposes requirements for digital identity management in AI agent communication protocols, focusing on cross-industry interoperability and security. Aims to establish foundational identity mechanisms for agent-to-agent and agent-to-entity communication.

Requirements document for applying existing digital identity concepts to AI agents - incremental adaptation rather than novel approach

Early requirements sketch at 9 pages - lacks detailed protocol specifications, implementation guidance, or concrete technical mechanisms

Significant overlap with existing digital identity frameworks (OAuth, DID, PKI) and agent communication standards - mostly adaptation work

Single revision draft with no indication of working group adoption or broader community engagement in AI agent identity space

Directly addresses core challenge in AI agent ecosystems - identity management is fundamental for secure multi-agent communication


This draft extends RFC7030 EST to provide server-controlled recommendations for when certificates should be renewed, replacing the typical 50% lifetime rule. It addresses the need for more flexible renewal timing as certificate lifetimes decrease from years to months or shorter periods.

Incremental extension to existing EST protocol, addressing a practical timing issue but not introducing novel concepts

Appears to define a specific protocol mechanism with clear problem statement, though implementation details unclear from abstract

Likely shares renewal timing concepts with other certificate management protocols and PKI standards

IETF working group adoption suggests active development and community interest in certificate lifecycle management

This is a certificate management protocol extension with no relation to AI or agents - appears to be a false positive for AI/agent relevance


This IETF draft specifies an ACE framework profile that uses EDHOC for mutual authentication between OAuth clients and resource servers, establishing OSCORE security contexts for protecting communications. It enables delegation of authorization management from resource-constrained servers to more capable trusted hosts.

Incremental combination of existing protocols (EDHOC, OSCORE, ACE) for constrained environments

Detailed 89-page specification with comprehensive protocol definitions and examples

Builds on established ACE, EDHOC, and OSCORE standards but creates new integration profile

Active IETF working group draft with 2026 target date indicating strong development momentum

Focuses on IoT device authentication and authorization rather than AI agents specifically


This draft explores the conceptual relationship between remote attestation (verifying system trustworthiness) and behavioral evidence recording (tracking what systems actually do), discussing how these complementary approaches could work together in accountability frameworks. It's purely informational and doesn't propose new protocols or modifications to existing RATS architecture.

Incremental conceptual discussion connecting existing attestation concepts with behavioral monitoring - useful but not groundbreaking

Early conceptual sketch that explicitly avoids defining mechanisms, protocols, or cryptographic bindings

Builds directly on RFC 9334 RATS architecture and shares behavioral monitoring concepts with some security frameworks

Single revision draft with no clear working group adoption or broader community engagement yet

Only tangentially related to AI/agents - focuses on general system attestation and behavioral evidence without specific AI agent considerations


This draft proposes dynamically adjusting ECN marking thresholds to align with dynamic buffer allocation in network devices. It provides a deterministic algorithm to maintain adaptive relationships between congestion marking and actual buffer capacity without requiring machine learning models.

Useful contribution addressing real misalignment problem between static thresholds and dynamic buffers, though the concept of adaptive thresholds isn't entirely new

Single revision from 2026 with no indication of working group adoption or broader community engagement yet

Some conceptual overlap with general AQM and buffer management work, but specific approach to ECN-dynamic buffer coupling is relatively unique

Tangentially AI-related only through mention of avoiding ML models - primarily a network buffer management mechanism rather than AI/agent technology


This draft specifies a Pre-Shared Key authentication method for the EDHOC key exchange protocol, enabling efficient mutual authentication and session resumption with quantum resistance. The PSK method reduces computational overhead while maintaining security properties including identity protection and ephemeral key exchange.

Incremental extension adding PSK authentication to existing EDHOC protocol - useful but straightforward adaptation

Detailed specification with complete protocol flows, key derivation procedures, message formats, and comprehensive security considerations

Shares core EDHOC concepts with base protocol draft, but PSK authentication method has distinct characteristics

IETF working group draft with clear adoption path and active development trajectory

General cryptographic protocol for secure communications - not specifically designed for AI agents despite potential applicability


AEPP defines a standardized protocol for executing AI agents and handling payments on the internet, providing a missing execution layer for existing agent URI schemes. It implements a three-phase model (discovery, payment, execution) with on-chain verification and claims to serve 1.8M agents with zero per-agent fees.

Notable originality in combining agent execution standardization with integrated payment verification and bridging multiple existing agent URI schemes

Strong momentum evidenced by live implementation at scale and integration with multiple existing agent naming schemes

Minor similarities to existing payment and execution protocols but unique in agent-specific integration approach

Core AI agent topic addressing fundamental infrastructure needs for agent execution and economic coordination

A2A protocols
Agent discovery/reg
Model serving/inference


This draft defines concrete benchmarking profiles for LLM serving systems, specifying System Under Test boundaries, measurement points, and interpretation constraints. It builds on companion drafts for terminology and methodology to enable reproducible performance comparisons.

Useful contribution addressing the practical need for standardized LLM benchmarking approaches, though the concept of performance profiles is well-established in other domains

Detailed specification with clear profile definitions, measurement boundaries, and concrete examples at 53 pages, appears implementation-ready

Shares conceptual framework with general benchmarking standards and references companion LLM benchmarking drafts, but applies established concepts to LLM-specific context

Part of active multi-draft series on LLM benchmarking with recent publication date, showing ongoing development effort

Directly addresses critical need for standardized LLM performance evaluation, essential for deployment and comparison of AI systems


This document extends RFC2181's DNS data trustworthiness ranking by adding entries for root server data and DNSSEC-verified data, while assigning numerical values to ranking positions. It aims to provide a more complete and quantifiable framework for comparing DNS data trustworthiness.

Incremental extension of existing RFC2181 ranking system with logical additions

Early specification with basic framework but lacks detailed implementation guidance

Directly builds upon and extends RFC2181's existing trustworthiness ranking system

Single revision draft with no clear working group momentum or community engagement

Standard DNS protocol enhancement unrelated to AI or autonomous agents


Pilot Protocol creates an overlay network providing autonomous AI agents with virtual addressing, transport services, NAT traversal, and encrypted communication tunnels over UDP. The protocol aims to give AI agents first-class network citizenship with stable identities and reachable addresses, operating beneath application protocols like A2A and MCP.

Creating a dedicated overlay network specifically for AI agent communication with virtual addressing and bilateral trust models represents notable originality in agent networking

At 43 pages with detailed protocol specification including addressing, transport, NAT traversal, and encryption mechanisms, this appears to be a comprehensive and detailed specification

While overlay networks exist, the specific focus on AI agent communication needs and integration with emerging agent protocols like A2A/MCP shows minor similarities to existing work

As a single draft from 2026 with no indication of working group adoption or broader community engagement, momentum appears limited to initial publication

Directly addresses core AI agent networking infrastructure needs including identity, addressing, transport, and secure communication - central to agent-to-agent interaction

A2A protocols
Agent identity/auth
Agent discovery/reg


Defines AITLP protocol for organizational governance of autonomous software agents, focusing on identity, trust, lifecycle management and authority boundaries rather than capabilities. Complements existing agent communication protocols by providing organizational actor framework with cryptographic scope enforcement.

Notable focus on organizational boundaries and 'what agents cannot do' rather than capabilities, with novel hierarchical mandate enforcement and Agent Legacy Mode for knowledge transfer

Well-defined protocol with clear identity conventions, lifecycle states, and trust verification mechanisms, though implementation details and concrete examples could be stronger

Explicitly positions itself as complementary to MCP, A2A, and ANS rather than overlapping, addressing orthogonal governance concerns

Recent draft date suggests early stage, unclear if part of working group or has broader community engagement

Directly addresses core challenge of autonomous agent deployment in organizational contexts with focus on auditability and revocation

Agent identity/auth
Policy/governance
A2A protocols


This draft extends RFC8578 by defining new use cases for deterministic networks including industrial internet, high-experience video, intelligent computing, and ISAC-enabled smart factories. It outlines network requirements and common properties for scaling deterministic networks beyond the original DetNet scope.

Incremental extension of existing DetNet work with new application domains

Early stage problem statement and use case description without detailed technical solutions

Significant overlap with existing DetNet RFCs and related deterministic networking work

Active development as indicated by 2026 date, likely part of ongoing DetNet evolution

Partially relevant through intelligent computing and smart factory automation use cases that may involve AI agents


This draft defines an OAuth profile for Agent-to-Agent communication that embeds call chain context within Transaction Tokens to maintain agent identity and authorization across distributed agent workloads. It extends existing OAuth Transaction Token mechanisms specifically for A2A scenarios in trusted domains.

Incremental extension of existing OAuth Transaction Tokens to A2A context - useful but straightforward application

Early specification stage at only 5 pages - likely contains basic profile definition but lacks detailed implementation guidance

Builds directly on OAuth Transaction Tokens RFC work and shares concepts with other agent authentication approaches

Single draft submission with no clear working group adoption signals yet

Directly addresses agent-to-agent authentication and authorization, which is fundamental for distributed AI agent systems


```json
{
  "s": "Proposes hardware TPM attestation in email headers to verify senders have genuine security chips, making large-scale spam economically infeasible by requiring unique physical hardware per sender. Includes both direct TPM verification and privacy-preserving SD-JWT alternatives for Sybil-resistant authentication.",
  "n": 4,
  "nn": "Novel application of TPM attestation to email authentication with specific focus on AI-generated spam prevention - creative bridging of hardware security and messaging protocols",
  "m": 4,
  "mn": "Detailed 25-page specification with defined mechanisms, header formats, and verification procedures, though lacks implementation examples and test vectors",
  "o": 2,
  "on": "Limited overlap - builds on existing TPM attestation and SD-JWT standards but applies them uniquely to email authentication problem",
  "mo": 2,
  "mn": "Single revision draft with future date suggests early stage, no clear working group adoption or community feedback yet",
  "r": 4,
  "rn": "Directly addresses AI-generated content challenges by making automated spam campaigns require substantial hardware investment, highly relevant to AI agent authentication",
  "c": ["Agent identity/auth", "Policy/governance"]
}
```


This draft proposes extensions to OAuth for authenticating and authorizing AI agents within Agent Communication Networks (ACN). It addresses the critical need for scalable and trustworthy authentication mechanisms as AI agents become more prevalent in various industries.

Useful contribution applying established OAuth framework to emerging AI agent authentication needs

Early sketch stage - proposes workflows but lacks detailed protocol specifications and implementation details

Shares authentication concepts with existing OAuth and identity management drafts but focuses on AI agent-specific use cases

Single revision with future date suggests early development stage, no clear working group adoption

Core AI agent topic directly addressing fundamental authentication and authorization requirements for agent networks

Agent identity/auth
A2A protocols
Policy/governance


Proposes Early Retransmit mechanism for TCP and SCTP to recover lost segments when congestion window is small by reducing duplicate ACKs needed for fast retransmission. Aims to avoid lengthy retransmission timeouts in low-bandwidth or small-window scenarios.

Incremental improvement to existing TCP fast retransmit mechanism, addressing edge case of small congestion windows

Detailed specification with clear mechanism definition, specific conditions for triggering, and implementation guidance

Shares concepts with existing TCP congestion control and retransmission mechanisms but proposes specific enhancement

This appears to be Mark Allman's work from early 2000s, likely inactive given the author and timeframe

Pure TCP transport protocol optimization with no relation to AI, agents, or machine learning systems


SATP proposes a protocol framework for establishing verifiable identity, attribution, and governance for autonomous machines including AI agents. It aims to provide a non-repudiable root of trust for both digital AI agents and physical autonomous systems.

Notable approach to autonomous machine identity and governance, addressing an emerging need as AI agents become more prevalent

Early sketch level - only abstract provided, lacks protocol details, mechanisms, or implementation specifics

Some conceptual overlap with existing identity and trust frameworks, but specific focus on autonomous machines is relatively unique

Single revision with recent date, but no evidence of working group adoption or broader community engagement

Core AI agent topic directly addressing fundamental challenges of agent identity, trust, and governance in autonomous systems


AIBDP defines a declarative protocol for websites to specify AI usage boundaries, extending robots.txt-style mechanisms to control how AI systems can index, train on, and interact with web content. The protocol aims to provide machine-readable permissions and restrictions for various AI activities including training, mimicry, and agentic access.

Useful contribution extending existing web crawling control mechanisms to AI-specific use cases, though the core concept of declarative content usage policies is not entirely novel

Appears to define specific protocol mechanisms and likely includes examples, though without seeing full technical details it's positioned as a defined protocol rather than just conceptual

Shares concepts with existing robots.txt standards and likely overlaps with other web content control mechanisms, but applies them to the specific AI domain

Single revision from 2026 date suggests early stage development without demonstrated working group adoption or broader community momentum yet

Directly addresses a core AI governance challenge of how content creators can control AI system access to their materials, highly relevant to current AI deployment concerns