MCP, ACP, and A2A, Oh My! The Growing World of Inter-agent Communication

The AI ecosystem is rapidly evolving from isolated AI models toward multi-agent systems: environments where AI agents must coordinate, communicate, and interoperate efficiently. At Camunda, we see this pattern quickly emerging as organizations are evolving their end-to-end business processes to take advantage of agent-driven automation.

As developers explore how to make multi-agent systems useful and reliable, new communication standards are emerging to address the need for interoperability, security, and shared understanding. Three notable efforts in this domain are:

• Model Context Protocol (MCP) developed by Anthropic
• Agent Communication Protocol (ACP) developed by IBM Research
• Agent2Agent (A2A) Protocol developed by Google and Microsoft

Each targets a specific layer of the multi-agent interaction stack and reflects different philosophical and architectural priorities.

Model Context Protocol (MCP)

Anthropic developed the Model Context Protocol (MCP) to solve a narrow but critical problem: how to give large language models (LLMs) structured context about tools, APIs, and systems they can interact with. MCP focuses on standardizing the input context that LLMs receive before agents execute their tasks. As the Anthropic documentation says, “Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.”

With MCP, tools expose a structured schema such as an OpenAPI or JSON schema along with natural language descriptions. The LLM receives the schema via MCP when it is being prompted to act, ensuring a consistent understanding of available actions.

As an early example of agent-to-agent communication, MCP has several advantages. It has promise as a way to equip AI agents with structured context to call APIs, tools, and plugins intelligently. It’s an open specification that’s designed to work with any LLM or agentic framework, making it highly flexible. And it’s lightweight and easy for software developers to adopt because it aligns with common software development practices.

However, it’s important to note that MCP is limited to tool and model interactions; it’s not a general-purpose agent protocol. As of now, it doesn’t define inter-agent negotiation or dynamic delegation of tasks.

Example: Customer onboarding in financial services

Imagine a customer onboarding process at a retail bank, which requires validating identity documents, performing Know Your Customer (KYC) checks, interfacing with fraud detection services, and activating new customer accounts. Traditionally, each of these tasks is handled by a separate back-end service with its own API.

Using MCP, an AI-powered onboarding agent could be equipped with structured, real-time context about all of these APIs. MCP ensures the agent understands what each service does, how to call it, and what inputs and outputs to expect, without the need for a developer to hard-code specific logic into the model.

This allows the onboarding agent to dynamically compose API calls, intelligently route requests, and adapt its workflow if a service is temporarily unavailable—all while minimizing human intervention. The result is a faster, more consistent onboarding experience that reduces manual handoffs and operational delays.

Agent Communication Protocol (ACP)

The Agent Communication Protocol (ACP) developed by IBM Research is designed to define how autonomous AI agents communicate with one another, with an emphasis on structured dialogue and coordination across heterogeneous systems. It aims to provide a shared semantic foundation for multi-agent communication, including message types, intents, context markers, and response expectations.

With ACP, agents exchange structured messages that encapsulate intention, task parameters, and context. The protocol enables dynamic negotiation between agents—for example, for delegation or task refinement.

ACP’s strong focus on semantics and interoperability mean it has the potential to become a very powerful protocol for high-level coordination of agents (beyond simple messaging). It can facilitate distributed task-solving by autonomous agents that have overlapping goals.

However, a potential hurdle to adoption is that ACP requires agent developers to agree on shared ontologies. This may position it as the protocol of choice for development teams that work for the same company or that work on the same software product. ACP is still in the early stages of development in terms of syntax, implementation, and tooling, so much remains to be seen as it grows.

Example: Supply chain coordination across departments

Consider a global manufacturer with autonomous agents representing procurement, inventory, and logistics. These agents must coordinate continuously to maintain optimal stock levels, anticipate shortages, and reroute shipments when needed. Using ACP, these agents could engage in structured, semantically rich dialogue to negotiate changes in supply orders, reallocate inventory based on real-time demand forecasts, or trigger alerts if a delay will cause cascading disruptions. For example:

• The procurement agent might notify logistics: “Delay expected from supplier X. Can we reassign delivery Y?”
• The logistics agent can respond: “Yes, rerouting via warehouse Z. Updating inventory accordingly.”

By using shared ontologies and structured message types, ACP aims to support adaptive, high-fidelity inter-agent collaboration across teams and systems. This is especially valuable in environments where distributed decision-making and resilience are key.

Agent2Agent Protocol (A2A)

The Agent2Agent Protocol (A2A) being developed by Google with support from Microsoft is another open standard. It’s designed to allow different AI agents from different companies or domains to exchange messages and perform coordinated tasks. Its development was prompted by the growing use of LLM-based agents in workflows that span multiple applications.

In A2A, agents advertise their capabilities using a structured metadata format called “agent cards.” Agents then communicate through signed, structured messages based on a shared schema. A2A includes provisions for trust, routing, and structured memory exchange. This design maximizes the options for composability and cross-platform collaboration between AI agents.

While A2A is a community-driven project, it is supported by two of the largest software companies in the world, both of which are cloud providers and LLM vendors, which may accelerate its development. However, the protocol is still an early-stage alpha with evolving security and governance capabilities, making it difficult for other vendors to start developing with it.

Example: Cross-platform customer support automation

Picture a scenario where a retail company uses Google Workspace, Zendesk, Salesforce, and Microsoft Teams. Different LLM-based agents exist in each environment and perform tasks such as summarizing conversations, logging support tickets, updating customer relationship management (CRM) records, and scheduling follow-ups.

With A2A, these agents can collaborate across platforms:

• A Google-based agent summarizes a support call and shares the summary with a Salesforce agent
• The Salesforce agent updates the customer record and flags a follow-up
• A Microsoft-based assistant sees the flag and books a Teams meeting with the customer

Through agent cards and structured messaging, A2A aims to enable interoperability across agent ecosystems, so tasks can flow fluidly without brittle, point-to-point integrations. This supports consistent, personalized, and efficient customer service—at scale.

Comparing developing communication protocols

The following table summarizes the current state of MCP, ACP, and A2A:

As you can see, these three protocols represent complementary approaches to the problem of inter-agent communication. MCP addresses the immediate need to contextualize LLMs effectively; ACP looks further ahead at semantic richness and intent modeling; and A2A targets broad interoperability across agents and platforms.

For developers and organizations building agentic architectures, understanding and experimenting with these protocols will be essential. While no single standard has emerged as dominant, the collective momentum suggests that interoperability and shared context will be key to unlock the full potential of multi-agent AI.

Camunda can help you operationalize AI agents

Whether you’re new to agentic AI or you’ve already started building agents, Camunda process orchestration and automation can help you put AI into action. To learn about Camunda’s agentic orchestration capabilities, check out our guide, “Why agentic process orchestration belongs in your automation strategy.”