As IBM's automation ecosystem evolves with the infusion of AI and digital workers, new communication protocols like MCP, ACP, and A2A are becoming central to how intelligent agents coordinate and execute tasks. However, these acronyms often create confusion due to their overlapping use of the term "agent."
Let’s break down what each one actually does — and how they differ — so you can better architect and build with them in mind
MCP – Model Context Protocol
What it is:
MCP defines a standardized way for the automation platform to deliver task instructions, along with relevant context, to downstream agents or digital workers.
Designed for enriching a single model’s context with tools, memory and resources. Introduced by Anthropic.
Focus: one model, many tools
Why it matters:
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Tool-first: Designed to standardize how agents invoke models, APIs, and information sources.
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Not meant for agent‑to‑agent communication: Its JSON-RPC patterns are less suitable for peer discovery, async messaging, and complex multi-agent interactions.
Key Characteristics:
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Used for agent-to-platform interactions.
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Passes task metadata and execution context (user, role, model type).
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Forms the foundation of agent-agnostic orchestration in CP4BA.
Typical flow:
Each agent uses MCP to request data or capabilities (e.g., “get supply chain data” or “run sentiment model”) from the central platform. Then it may collaborate using ACP or A2A.
ACP – Agent Communication Protocol
What it is:
Agent Communication Protocol (ACP) is an open standard for agent-to-agent communication (A2A) . With this protocol, we can transform our current landscape of siloed agents into interoperable agentic systems with easier integration and collaboration.
Designed for communication between independent agents across systems and organizations. Introduced by IBM’s BeeAI.
Focus: many agents, securely working as peers, no vendor lock in, open governance
Key Characteristics:
- REST-based communication: ACP uses standard HTTP conventions for communication that makes it easy to integrate into production. Whereas MCP relies on the JSON-RPC format that requires much more complex communication methods.
- No SDK required: ACP doesn’t require any specialized libraries. You can interact with intelligent agents by using tools like cURL, Postman or even your browser. For added convenience, there is an SDK available.
- Offline discovery: ACP agents can embed metadata directly into their distribution packages, which enables discovery even when they’re inactive. This supports scale-to-zero environments, where resources are dynamically allocated and might not always be online.
- Async-first, sync supported: ACP is designed with asynchronous communication as the default. This method is ideal for long-running or complex tasks. Synchronous requests are also supported.
Agent2Agent Protocol (A2A): Designed for communication between independent agents across systems and organizations. Introduced by Google.
Google’s Agent2Agent protocol (A2A), which was introduced shortly after ACP, also aims to standardize communication between AI agents. Both protocols share the goal of enabling multi-agent systems, but they diverge in philosophy and governance.
Why it matters:
Side-by-Side Comparison MCP , ACP & A2A
| Feature |
MCP |
ACP |
A2A |
| Governance |
Open Standard , Introduced by Anthropic. |
Open Standard, Community led under the Linux foundation |
Google led & closely tied to Google's eco-system |
| Scope |
Agent ↔ Platform (tool/controller) |
Agent ↔ Agent messaging & coordination |
Multi-agent Collaborating |
| Primary Function |
Standardized tool invocation |
Agent Communication & Orchestration |
Collaborate or delegate across agents |
| Communication Style |
JSON-RPC-based |
REST/HTTP, metadata-enabled discovery |
JSON-RPC-based |
| Use Case Layer |
Orchestration / Task Assignment |
Agent To & From Agents communication |
Dynamic peer-to-peer task delegation |
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