AI Protocols
Comprehensive guide to AI communication protocols including Model Context Protocol (MCP), Agent-to-Agent Protocol (A2A), and emerging standards for AI interoperability.
Updated September 16, 2025 - Overview of core AI agent protocols with verified features, current sources, and summary comparisons across legacy, foundational, and emerging standards.
🔄 Modern Core AI Agent Protocols (2025)
The modern AI ecosystem relies on standardized protocols for communication, coordination, and interoperability between AI agents, models, and systems. These protocols collectively form the backbone of the Internet of Agents, supporting secure, robust, and ethical multi-agent ecosystems and setting the foundation for next-generation AI collaboration.
Agent-to-Agent Protocol (A2A)
Developer: Google
Purpose: Facilitates direct communication between AI agents across different platforms and frameworks
Key Features:
- • Agent Cards (JSON descriptors) for discovery and capability sharing
- • Structured messaging via JSON-RPC 2.0 over HTTPS
- • Real-time feedback through Server-Sent Events (SSE)
- • Open, secure, and scalable for enterprise applications
Use Cases: Multi-agent orchestration, cross-vendor interoperability, collaborative workflows
Model Context Protocol (MCP)
Developer: Anthropic
Purpose: Standardizes how AI models access external tools, APIs, and data sources
Key Features:
- • JSON-RPC 2.0 client-server architecture
- • Secure, dynamic connections to databases, file systems, APIs
- • Model-agnostic design compatible with various AI frameworks
- • Enterprise authentication standards (OAuth2, mTLS)
Use Cases: Contextual data integration, tool invocation, enhancing AI model capabilities
Agent Communication Protocol (ACP)
Developers: IBM and BeeAI
Purpose: Enables communication between AI agents, focusing on local-first coordination
Key Features:
- • REST-based communication for lightweight agent invocation
- • Offline agent discovery and decentralized environments
- • Compatible with stateless utilities to stateful conversational agents
- • Open governance under the Linux Foundation
Use Cases: Edge computing, offline AI applications, decentralized agent coordination
🌐 Emerging and Specialized Protocols
Next-generation protocols addressing decentralized agent discovery, ethical governance, and complex multi-agent coordination scenarios.
Agent Network Protocol (ANP)
Purpose: Supports decentralized agent discovery and secure collaboration
Key Features:
- • Decentralized identifiers (DIDs) and JSON-LD graphs
- • Open-network agent discovery
- • Security and interoperability in decentralized environments
Use Cases: Decentralized agent marketplaces, collaborative AI ecosystems
Coral Protocol
Purpose: Open and decentralized infrastructure for AI agent communication, coordination, and payments
Key Features:
- • Standardized messaging formats for agent communication
- • Modular coordination mechanisms for multi-agent tasks
- • Secure team formation for dynamic agent collaboration
Use Cases: Multi-agent AI ecosystems, emerging "Internet of Agents"
LOKA Protocol
Purpose: Decentralized framework for trustworthy and ethical AI agent ecosystems
Key Features:
- • Universal Agent Identity Layer (UAIL) for decentralized identity
- • Intent-centric communication for semantic coordination
- • Decentralized Ethical Consensus Protocol (DECP)
Use Cases: Ethical governance, interoperability in autonomous AI systems
Agent Collaboration Protocols (ACPs)
Purpose: Comprehensive protocol suite for the Internet of Agents (IoA)
Key Features:
- • Registration, discovery, interaction, and tooling protocols
- • Trustable access and capability orchestration
- • Workflow construction and coordination
Use Cases: Building secure, open, and scalable agent internet infrastructure
🧠 Legacy and Foundational Protocols
Established protocols that laid the groundwork for modern AI agent communication and continue to influence current standards.
FIPA-ACL (Agent Communication Language)
Developer: Foundation for Intelligent Physical Agents (FIPA)
Purpose: Standardizes communication between software agents using performative structures
Key Features:
- • Communicative acts (request, inform, propose)
- • Ontology-based agent communication
- • Semantic message understanding
Use Cases: Early multi-agent systems, research in agent communication
KQML (Knowledge Query and Manipulation Language)
Purpose: Facilitates communication among software agents and knowledge-based systems
Key Features:
- • Performative structures for message passing
- • Support for variety of communication protocols
- • Multiple content languages
Use Cases: Knowledge sharing, coordination among intelligent agents
Contract Net Protocol (CNP)
Purpose: Task-sharing protocol in multi-agent systems, resembling a sealed-bid auction
Key Features:
- • Agents can act as managers or contractors
- • Dynamic task allocation through proposals and bidding
- • Distributed decision making
Use Cases: Distributed problem-solving, resource allocation in agent networks
📊 Protocol Comparison Summary
A comprehensive overview of key AI agent protocols and their primary focus areas, helping developers choose the right protocol for their specific use cases.
| Protocol | Focus Area | Key Features | Use Cases |
|---|---|---|---|
| A2A | Agent-to-Agent Communication | Agent discovery, structured messaging, real-time updates | Multi-agent orchestration, cross-platform collaboration |
| MCP | Model-to-Tool Integration | Secure tool access, context sharing, model-agnostic design | Enhancing AI model capabilities, data integration |
| ACP | Local Agent Coordination | REST-based communication, offline discovery | Edge computing, decentralized agent coordination |
| ANP | Decentralized Agent Networks | DID-based discovery, JSON-LD graphs | Decentralized agent marketplaces, collaborative ecosystems |
| Coral | Multi-Agent Infrastructure | Standardized messaging, modular coordination | Internet of Agents, open agent collaboration |
| LOKA | Ethical AI Ecosystems | Decentralized identity, ethical consensus protocols | Trustworthy and ethical AI governance |
| ACPs | Agent Collaboration Suite | Registration, discovery, interaction protocols | Building scalable agent internet infrastructure |
🚀 The Future of AI Protocol Standards
The landscape of AI agent protocols is rapidly evolving, driven by the need for seamless interoperability, ethical governance, and scalable multi-agent systems. These protocols collectively shape the emerging Internet of Agents.
🔮 Key Trends in AI Protocol Development (2025)
- • Standardization Convergence: Major companies align on protocol standards for seamless integration
- • Decentralized Governance: Shift toward open, community-driven protocol development
- • Ethical Integration: Protocols embed responsible AI and consensus mechanisms
- • Cross-Platform Interoperability: Universal messaging for agent networks across ecosystems
- • Real-time Coordination: Low-latency structures for time-sensitive multi-agent tasks
- • Security by Design: Encryption, identity verification, and open trust mechanisms built-in
The future Internet of Agents will require robust, secure, and ethical communication protocols that enable unprecedented collaboration between AI systems while maintaining human oversight and control.