Unlocking the Internet of Agents: How AI Protocols Drive Scalable Intelligence

The Communication Challenge in AI Agent Ecosystems

As large language model (LLM) agents become more prevalent in both enterprise and research sectors, a critical obstacle has surfaced: effective communication. Despite their autonomous reasoning, planning, and acting capabilities, AI agents currently struggle to coordinate among themselves or interact seamlessly with external tools due to the lack of standardized communication protocols. This limitation hampers scalability, interoperability, and the development of collaborative AI systems.

A New Classification Framework for AI Agent Protocols

Researchers from Shanghai Jiao Tong University and the ANP Community have conducted a comprehensive survey presenting a taxonomy and evaluation of AI agent protocols. Their proposed classification organizes protocols along two dimensions:

Context-Oriented vs. Inter-Agent Protocols: Context-oriented protocols manage interactions between agents and external data or APIs, whereas inter-agent protocols facilitate peer-to-peer communication, task sharing, and coordination.
General-Purpose vs. Domain-Specific Protocols: General-purpose protocols support diverse environments and agent types, while domain-specific protocols target specialized applications such as robotics, IoT, or human-agent interaction.

This framework clarifies design trade-offs related to flexibility, performance, and specialization.

Key Protocols Shaping the Future

Model Context Protocol (MCP) by Anthropic: A general-purpose, context-oriented protocol that separates reasoning from execution, enhancing security and scalability. MCP processes sensitive data locally to reduce privacy risks.
Agent-to-Agent Protocol (A2A) by Google: Facilitates secure, asynchronous collaboration among agents in enterprise environments. It supports multimodal data, modular design, and protects intellectual property through opaque execution.
Agent Network Protocol (ANP) Open-Source: Envisions a decentralized, trustless agent network using decentralized identity and semantic meta-protocols. ANP enables encrypted communication, discovery, negotiation, and task execution across heterogeneous domains.

Evaluating Protocol Performance

The survey introduces seven criteria to assess AI agent protocols:

Efficiency (throughput, latency, resource usage)
Scalability (support for numerous agents and dynamic tasks)
Security (authentication, access control, data privacy)
Reliability (message delivery and connection stability)
Extensibility (ability to evolve without breaking compatibility)
Operability (ease of deployment and platform independence)
Interoperability (cross-platform and cross-vendor compatibility)

This evaluation integrates traditional network principles with challenges unique to AI agents.

Towards Collective Intelligence and Adaptive Protocols

Standardized protocols lay the groundwork for emergent collective intelligence, enabling agents to dynamically collaborate on complex tasks. Protocols like Agora empower agents to negotiate and adapt communication routines in real time, while LOKA incorporates ethical reasoning and identity management into communication layers.

Future Directions in AI Agent Protocols

The evolution of protocols is expected to progress through:

Short-Term: Moving from fixed function calls toward dynamic, evolvable protocols.
Mid-Term: Transitioning from rule-based APIs to self-organizing, negotiating agent ecosystems.
Long-Term: Developing layered infrastructures supporting privacy-preserving, collaborative, and intelligent agent networks.

These advancements mark a shift from traditional software to a flexible, agent-native computing paradigm, underscoring the critical role of communication protocols in unlocking the full potential of AI systems.