# A Layered Protocol Architecture for the Internet of Agents

arXiv: https://arxiv.org/abs/2511.19699 · HTML: https://mlsanity.com/paper/2511.19699/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use.
- Builds on 2 of its 14 references that are in the map.
- First posted on arXiv 24 Nov 2025.

## Placed in

- LLM agents and tool use / [General LLM agent frameworks and benchmarks](https://mlsanity.com/agents/general-llm-agent-frameworks-and-benchmarks/)

## Builds on (most cited first, 2 of 2)

- [Generative Agents: Interactive Simulacra of Human Behavior](https://mlsanity.com/paper/2304.03442/) (2023)
- [Ripple Effect Protocol: Coordinating Agent Populations](https://mlsanity.com/paper/2510.16572/) (2025)

## Built on by (most cited first, 6 of 6)

- [Agentic Peer-to-Peer Networks: From Content Distribution to Capability and Action Sharing](https://mlsanity.com/paper/2603.03753/) (2026)
- [AONA: A Comprehensive Architecture and Workflow Design for Global Agentic Collaboration](https://mlsanity.com/paper/2606.20573/) (2026)
- [InterSAGE: The Secure and Verifiable Interoperability Protocol for An Internet of Agents](https://mlsanity.com/paper/2608.13030/) (2026)
- [The Civilization Framework: Sovereign-Anchored Communication Between Personal Multi-Agent Systems](https://mlsanity.com/paper/2609.03425/) (2026)
- [SemDHT: Certified Semantic Discovery for Peer-to-Peer Agent Networks over Exact-Key DHTs](https://mlsanity.com/paper/2609.23539/) (2026)
- [Toward SLM-based agentic task-tool intent matching](https://mlsanity.com/paper/2610.03213/) (2026)

## Abstract (arXiv)

Large Language Models (LLMs) have demonstrated remarkable performance improvements and the ability to learn domain-specific languages (DSLs), including APIs and tool interfaces. This capability has enabled the creation of AI agents that can perform preliminary computations and act through tool calling, which is now being standardized via protocols like MCP. However, LLMs face fundamental limitations: their context windows cannot grow indefinitely, restricting their memory and computational capacity. Agent collaboration emerges as essential for solving increasingly complex problems, mirroring how computational systems rely on different types of memory to scale. The "Internet of Agents" (IoA) represents the communication stack that enables agents to scale by distributing computation across collaborating entities. Current network architectural stacks (OSI and TCP/IP) were designed for data delivery between hosts and processes, not for agent collaboration with semantic understanding. To address this gap, we propose two new layers: an Agent Communication Layer (L8) and an Agent Semantic Layer (L9). L8 formalizes the structure of communication, standardizing message envelopes, speech-act performatives (e.g., REQUEST, INFORM), and interaction patterns (e.g., request-reply, publish-subscribe), building on protocols like MCP. The proposed L9 layer: (1) formalizes semantic context discovery and negotiation, (2) provides semantic grounding by binding terms to semantic context, and (3) semantically validates incoming prompts and performs disambiguation as needed. Furthermore, L9 introduces primitives for coordination and consensus, allowing agents to achieve alignment on shared states, collective goals, and distributed beliefs. Together, these layers provide the foundation for scalable, distributed agent collaboration, enabling the next generation of multi-agentic systems.