This draft proposes an agent networking architecture centered on agent gateways for digital banking scenarios, discussing how banking digitalization is evolving toward agentic service interconnection. It appears to be an early-stage exploration of applying agent networking concepts to enterprise banking use cases.

Incremental application of agent networking concepts to banking domain, but architecture appears to be straightforward gateway-based approach without novel technical contributions

Early sketch level - describes scenarios and proposes basic architecture but lacks detailed protocol specifications, implementation details, or concrete mechanisms

Shares general agent networking and gateway concepts with other drafts in the space, though banking-specific application provides some differentiation

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

Directly relevant to AI agent networking, focusing on agent interconnection architecture and service communication in enterprise environments

A2A protocols
Agent discovery/reg
Data formats/interop


This draft analyzes network-assisted collective communication optimizations for AI training/inference clusters, identifying gaps between emerging RDMA-based acceleration approaches and current IETF standards like iWARP. It proposes requirements for new standards to optimize Many-to-Many communication patterns essential for large-scale AI workloads.

Useful contribution identifying specific gaps between AI collective communication needs and existing IETF transport standards

Early analysis phase - identifies problems and gaps but lacks detailed technical solutions or protocol specifications

Limited overlap with existing IETF work, addresses specific AI networking requirements not extensively covered elsewhere

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

Directly addresses critical AI infrastructure needs for distributed training and inference communication optimization


This draft explores applying the Model Context Protocol (MCP) to network management, proposing it as a framework to integrate AI capabilities into network operations and provide more agile architecture. It covers MCP server discovery, network element communication, and network exposure capabilities in IP networks using IETF technologies.

Useful application of MCP to network management domain, though applying existing protocols to new domains is fairly incremental

Early sketch stage - discusses applicability and use cases but lacks detailed protocol specifications or implementation details

Some conceptual overlap with other network management and AI integration drafts but focuses specifically on MCP application

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

Directly relevant to AI agent integration in networking, specifically addressing how MCP can enable AI-driven network management

Autonomous netops
A2A protocols
Agent discovery/reg
Data formats/interop


DAAP defines a comprehensive OAuth 2.0-based authorization framework for AI agents acting on behalf of humans, incorporating cryptographic identity, consent flows, audit trails, and policy engines. The protocol addresses the critical gap of verifying human authorization for specific agent actions with real-time revocation and tamper-evident logging.

Notable originality in combining OAuth patterns with agent-specific requirements like cascade revocation, hash-chained audit trails, and multi-agent delegation models

Detailed 40-page specification with comprehensive protocol flows, token formats, and integration points, though lacks implementation examples

While building on OAuth 2.0 foundations, the agent-specific extensions and cryptographic identity integration represent a distinct approach

Single revision draft with 2026 date suggests early stage development without demonstrated working group or community engagement

Core AI agent topic directly addressing fundamental authorization challenges for autonomous agent systems acting on human behalf

Agent identity/auth
Policy/governance
Human-agent interaction


SLIM is a protocol for real-time interactive AI applications that combines gRPC over HTTP/2/3 with secure messaging and group communication. It provides transport layer capabilities for agent protocols like A2A and MCP with connection management, stream multiplexing, and MLS encryption.

Incremental combination of existing technologies (gRPC, HTTP/2/3, MLS) for AI agent transport

Defines protocol mechanisms and integration approach but lacks detailed specifications and implementation examples

Significant overlap with existing gRPC, HTTP transport, and MLS specifications - primarily integrates existing protocols

Single revision from February 2026 with no clear working group adoption or community activity

Directly relevant to AI agent infrastructure, specifically addressing transport needs for agent-to-agent communication


This draft extends SCIM identity management to support AI agents by defining a JSON schema for representing agentic identities. It enables AI agents to be provisioned, authenticated, and authorized across cloud services using existing SCIM infrastructure.

Useful contribution applying established identity management patterns to the emerging need for AI agent identity representation

Early sketch level - defines basic schema concepts but lacks detailed protocol mechanisms and implementation guidance

Minor similarities to general identity management work but addresses the specific domain of AI agent identities

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

Directly addresses a core infrastructure need for AI agent deployment - identity management and cross-domain authentication

Agent identity/auth
Data formats/interop