Mastering MCP Communication: A2A Coordination Unlocked. What Drives Transport Solutions?

The journey of distributed computing is characterized by an initial surge of various protocols, which later streamlined into more unified standards.

In the late 1990s, several protocols such as Common Object Request Broker Architecture (CORBA), Distributed Component Object Model (DCOM), Java Remote Method Invocation (RMI), and the early iterations of Simple Object Access Protocol (SOAP) competed in the enterprise integration landscape. Ultimately, Representational State Transfer (REST) emerged as the victor, favored for its simplicity and compatibility with HTTP.

Similarly, the realm of real-time messaging saw fragmentation with protocols like Extensible Messaging and Presence Protocol (XMPP), Internet Relay Chat (IRC), and several proprietary options, before MG Telemetry Transport (MQTT) and WebSockets found their respective niches. Each new paradigm in computing tends to spark a wave of competing standards, which gradually converge as interoperability becomes a financial necessity.

Currently, the AI agent ecosystem is experiencing a phase of rapid protocol development. Within the past year and a half, four major protocols have been introduced: Model Context Protocol (MCP) by Anthropic in late 2024, Agent Communication Protocol (ACP) from IBM Research in March 2025, Agent2Agent (A2A) released by Google in April 2025, and Agent Network Protocol (ANP) from an independent working group.

The World Wide Web Consortium (W3C) has established a community group focused on developing standards for AI agents. The Internet Engineering Task Force (IETF) is receiving drafts related to agent transport. Various conferences are hosting workshops on interoperability, and new GitHub repositories are regularly appearing that aim to tackle agent communication challenges.

Realizing how and when these protocols will converge is vital for the architectural decisions being made today.

Understanding the Protocols’ Functions

The appearance of disorder in the protocol landscape is somewhat misleading, as each protocol primarily addresses different layers within a stack, rather than competing for the same functions. The marketing narrative often describes each as “the standard for AI agent communication,” without clarifying which specific aspect of communication is being referred to.

MCP functions as a tool invocation interface. It outlines how a model identifies the functions offered by a server, how those functions are invoked, and how to interpret the responses. Essentially, it acts as a typed remote procedure call (RPC) contract between a model client and a tool server, utilizing HTTP. By April 2026, the Linux Foundation reported over 10,000 active public MCP servers and 164 million monthly downloads of the associated Python SDK, solidifying MCP’s dominance in the tool-calling domain.

A2A serves as a task coordination interface. While MCP details how an agent engages a tool, A2A specifies how one agent can delegate tasks to another. It introduces Agent Cards (which advertise capabilities), various task lifecycle statuses, and three interaction modes: synchronous, streaming, and asynchronous. Google contributed A2A to the Linux Foundation in June 2025, and it has gained traction among corporate AI teams, effectively filling a gap that MCP doesn’t cover.

ACP represents a lightweight message envelope format for agent-to-agent communication, designed to facilitate message exchange without the more complex semantics of A2A’s task coordination.

ANP acts as a discovery and identity protocol. By utilizing Decentralized Identifiers (DIDs) for agent identification and JSON-LD graphs for describing capabilities, it lays the groundwork for decentralized agent marketplaces that do not rely on a central registry.

Emerging from these developments is a coherent stack: ANP or simpler registries enable capability discovery, A2A facilitates task coordination, MCP handles tool calls, and ACP provides lightweight messaging for scenarios that do not necessitate comprehensive task management. These layers complement one another.

Addressing the Transport Challenge

All the listed protocols currently rely on HTTP. This choice stems from their origins in research, API development, and enterprise software creation, where HTTP is a foundational assumption, allowing for straightforward demonstrations.

However, the limitation of HTTP is its reliance on having a reachable server. Given that a substantial percentage of networked devices operate behind Network Address Translation (NAT), direct visibility is lost without a relay. For agent networks that require direct task routing across varied environments—such as cloud boundaries, home setups, and edge systems—this reliance means that all communication must pass through centralized relays, introducing potential bottlenecks, costs, and failure points.

While these application-layer protocols successfully define the semantics of agent communications, they do not tackle how agents establish direct peer connections. This gap pertains to session-layer issues in the OSI model, which remain unaddressed by MCP, A2A, ACP, and ANP.

Technological solutions exist for these challenges. Techniques like UDP hole-punching with STUN can facilitate NAT traversal for approximately 70% of network setups. Strong encryption methods, such as X25519 Diffie-Hellman and AES-256-GCM, establish secure communication channels without a need for certificate authorities. Protocols like Quick UDP Internet Connections (QUIC) or custom designs over UDP can ensure reliable data transfer without TCP’s inherent delays.

What sets the agent context apart is the need for capacity-based routing. Agents should locate peers based on their capabilities rather than their network addresses. For instance, a research agent might inquire, “Which peers have current foreign exchange data?” and receive responses from available specialists. This operates more like a service registry than DNS and fits naturally into ANP’s design philosophy applied at the transport layer.

A few initiatives are piecing together these solutions. The Pilot Protocol boasts a comprehensive specification, including NAT traversal, addressing, and tunnel creation tailored for agent networks. Additionally, libp2p offers a robust framework with similar capabilities while the IETF’s QUIC group is actively working on NAT traversal enhancements relevant to this domain.

The Future of Convergence

Protocols based on HTTP, such as MCP and A2A, are already reaching mature versions. Over the next year, we can anticipate enhancements in production readiness, security, scalable stateless MCP servers, and improved A2A federation, rather than entirely new frameworks. The layers addressing tool-calling and task coordination are mostly resolved.

However, the development of the transport layer is still lagging by 18 to 24 months. We should expect initial implementation diversity as teams explore various peer-to-peer agent networking strategies, followed by a consolidation around a few implementations as performance data become available. Standardization efforts from the IETF and W3C will likely yield results around 2027-2028, by which point one or two open-source implementations may establish unofficial benchmarks prior to formalization.

For leaders in engineering making current architectural choices, this presents a clear roadmap for layered implementation. The application-layer protocols are sufficiently mature for adoption. Using MCP now is a relatively low-risk venture, while integrating A2A for multi-agent tasks is sensible with the understanding that the protocol will evolve. The transport layer presents a choice between building custom solutions with plans for later replacement or assessing early implementations, knowing that this domain is still very much in flux.

Teams that focus on maintaining a clear distinction between the application semantics (MCP, A2A) and the transport mechanisms below will be best positioned once the transport layer stabilizes. Ensuring such separation is straightforward to implement initially but becomes costly to modify later—echoing lessons learned from past experiences during the microservices era.