Over the past year, we’ve witnessed an inflection point in AI infrastructure: the rise of interoperability protocols for AI agents. As large language models evolve into agents capable of reasoning, tool use, and collaboration, new challenges emerge around how agents communicate with one another, how they access external data and tools in a secure and structured manner, and whether these interactions can be standardized, so developers and companies don’t reinvent the wheel for every integration.

Three major players—Anthropic, IBM, and Google—have each released open protocols to address this:

• Anthropic’s Model Context Protocol (MCP)

• IBM’s Agent Communication Protocol (ACP)

• Google’s Agent-to-Agent Protocol (A2A)

In this post, we compare these protocols side-by-side, explore their design philosophies, and reflect on where they’re converging.

The Protocols at a Glance

Anthropic’s Model Context Protocol (MCP)

MCP is the most mature of the three and solves a foundational problem: how can a model access live, external information? It defines a universal interface—like a USB-C port—for connecting AI models to resources (files, databases), prompts (templates), and tools (functions or APIs). Agents (e.g. Claude) can interact with MCP Servers that expose this context via a well-defined schema. It’s LLM- and vendor-agnostic. While Anthropic initiated it, MCP is designed to work across ecosystems, and is already integrated into IDEs and assistants like Sourcegraph and Replit. Think of MCP as the protocol that turns an AI assistant into something truly plug-and-play.

IBM’s Agent Communication Protocol (ACP)

IBM’s ACP builds directly on MCP, but tackles a more complex challenge: multi-agent collaboration. ACP still uses JSON-RPC like MCP, but extends it to treat agents themselves as entities that can be discovered, queried, and coordinated. It introduces concepts like agent catalogs, task routing, and agent-specific capabilities. ACP is in alpha as part of IBM’s open-source BeeAI platform, which aims to let any agent—from LangChain to custom scripts—join a shared ecosystem. In short, where MCP is about “model ↔ tool”, ACP evolves this into “agent ↔ agent”.

Google’s Agent-to-Agent Protocol (A2A)

Google’s A2A protocol is the newest entrant, and it’s designed with enterprise-scale interoperability in mind. It enables peer-to-peer communication between agents—across vendors, across apps, and across companies. Built on web-native technologies (HTTP, JSON, Server-Sent Events), it supports long-running tasks, capability discovery, and secure delegation. It’s backed by ~50 companies, including Salesforce, SAP, and others, signaling serious momentum behind a shared industry standard. While MCP handles tool access and ACP handles multi-agent workflows, A2A is focused squarely on cross-domain interoperability.

Where They Converge

Despite differences, these protocols are not competitive—they’re complementary and increasingly aligned: MCP is about grounding agents with tools and data. ACP is about letting agents work together. A2A is about enabling that collaboration across organizational and vendor boundaries.

In fact, IBM’s ACP is built on top of MCP. Google explicitly notes that A2A can coexist with MCP—an agent could use MCP to access data, and A2A to collaborate with other agents.They all rely on JSON-based message formats, are governed by open standards, and share a vision of modular, interoperable AI ecosystems.

What This Means for Builders

We’re at the beginning of what feels like the HTTP moment for AI agents.

Just as the early web coalesced around common protocols (HTTP, TCP/IP, HTML), the AI agent world is settling on shared standards for communication and context. These protocols could form the foundation of agent ecosystems that are:

• Composable – mix and match capabilities from different agents

• Secure – delegate tasks safely across domains

• Future-proof – work across LLMs, toolchains, and organizations

Whether you're building developer agents, enterprise workflows, or autonomous assistants, understanding and adopting these protocols will be key to long-term scale and interoperability.

Conclusion

The race to standardize AI agent communication is just beginning. But if MCP, ACP, and A2A continue to evolve in harmony, we’ll soon have a shared “language” that lets intelligent systems work together—securely, flexibly, and at scale.

We’re excited to watch (and contribute to) this future unfold.

Want to go deeper? Check out:

• Anthropic's Model Context Protocol

• IBM’s Agent Communication Protocol (BeeAI)

• Google’s Agent-to-Agent Protocol