MCP vs A2A vs ANP vs ACP: Choosing the Right AI Agent Protocol

The battle for AI agent interoperability is heating up. Four major protocols are vying to become the universal standard for how AI agents communicate, collaborate, and access tools. Just as the early internet needed HTTP to connect disparate systems, today’s emerging “agent internet” needs its own communication layer to avoid a tangle of custom integrations. In this analysis, I compare the four leading AI agent communication protocols — Model Context Protocol (MCP), Agent-to-Agent Protocol (A2A), Agent Network Protocol (ANP), and Agent Communication Protocol (ACP) — by examining their developers, architectures, discovery mechanisms, session handling, transport layers, strengths, limitations, and use cases.

Notably, Anthropic’s MCP focuses on linking a single AI agent to external tools (providing context to an LLM), whereas Google’s A2A and Cisco’s ANP enable direct agent-to-agent collaboration (albeit one via a centralized directory, the other fully decentralized). IBM’s ACP takes an enterprise-friendly middle ground, using a brokered model for multi-agent orchestration. Each comes with distinct advantages and trade-offs for businesses.

Key attributes of four leading AI agent communication protocols (MCP, A2A, ANP, ACP) are compared side by side. Each protocol offers a distinct approach across these dimensions, from system architecture to discovery and session management. For example, Anthropic’s MCP employs a simple client-server model, best suited for tool integration, whereas Cisco’s ANP adopts a decentralized “network of agents” vision with greater complexity. Such differences carry significant strategic implications, which I explore below for each protocol.