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MCP vs A2A: Understanding the protocols behind enterprise AI agents

Published Jul 15, 2026

Adam Peña

Technical Product Marketing Associate

Enterprise AI agents no longer run as isolated assistants. They read from systems of record, trigger workflows, and increasingly hand work to one another. That shift depends on two distinct protocols operating at different layers of the agentic stack, and conflating them is one of the most common architectural mistakes teams make today.

The distinction is simple to state. Model Context Protocol (MCP) governs how an autonomous agent accesses tools and business systems. Agent-to-Agent (A2A) governs how AI agents coordinate with each other. One protocol connects a single agent to the systems it needs to act on. The other lets multiple AI agents collaborate on a shared goal. A related emerging standard, Agent Communication Protocol (ACP), sits alongside A2A in the same coordination space, so ACP is worth understanding as part of the picture even though MCP and A2A dominate most discussions.

For enterprise architects and integration teams building multi-agent systems at scale, understanding MCP, A2A, and ACP is not optional.

They are complementary building blocks, not competing standards, and production agentic architectures generally require both MCP and A2A working together rather than a choice between them.

What is Model Context Protocol (MCP)?

Model Context Protocol is a standardized protocol introduced by Anthropic that enables AI agents to securely discover and invoke external tools, APIs, and business systems. It is infrastructure-level standardization, not a feature or a plugin system. A useful shorthand among technical teams is that MCP is the USB-C for AI tools: one consistent interface any compliant agent can use to reach any compliant system, replacing a tangle of bespoke connectors with a single standard.

Critically, MCP standardizes governed access to systems of record such as CRM, ERP, finance, and support platforms, rather than exposing direct, unmanaged API access to an LLM. Handing raw endpoints to an LLM tends to produce brittle, hard-to-control integrations. MCP instead defines a controlled interface through which AI agents interact with approved capabilities, making the protocol viable for enterprise governance.

MCP follows a client-server architecture. An MCP server exposes capabilities such as invoking a Salesforce opportunity, retrieving a NetSuite order, or running an integration flow, and AI agents dynamically connect to those servers.

Communication between them uses JSON-RPC, a lightweight remote procedure call format that carries structured requests and responses. The JSON-RPC layer standardizes how an agent and a server communicate: because the server advertises what it offers, the agent performs capability discovery at runtime and then handles authentication and execution through the same interface.

In effect, MCP abstracts the underlying API complexity, so discovery, authentication, and execution all run through a single predictable protocol rather than dozens of custom integrations, and the JSON-RPC contract keeps those interactions uniform across every connected system.

Three components define the model. The MCP host is the AI application or agent runtime that wants external capabilities. The MCP client is the component within the host that manages the connection to a server. The MCP server exposes tools and data sources to the client through the protocol. The host is usually an LLM-driven runtime, and the LLM decides which exposed tool to call for a given task. Adoption has been broad: major LLM providers now support MCP, and yes, the LLM behind ChatGPT is among the systems that can use it, which is a large part of why Anthropic’s MCP has become a default way for AI systems to reach enterprise data sources, letting the model act rather than only answer. When an LLM-powered agent needs live data sources, MCP is increasingly how it gets them.

What is A2A protocol?

Agent-to-Agent (A2A) is an architectural protocol governing how one AI agent delegates tasks to, coordinates with, or shares context with another AI agent. Where MCP is about tool access, A2A is about agent communication. The two solve different problems, and A2A sits above MCP in the agentic stack as the orchestration layer.

In enterprise terms, A2A is what lets a procurement agent invoke a logistics agent, or a support agent query a finance agent for invoice status, without a human brokering the handoff. This is also where ACP appears, since ACP targets the same agent-to-agent coordination need.

A2A enables delegation, negotiation, and context-sharing between agents.

The typical flow: an orchestrating agent identifies a task, delegates it to a specialist agent, that agent’s LLM executes using its available tools, potentially by calling an MCP server, and returns a result. Specialist agents themselves vary.

The four types most teams encounter (goal-based, reflex, utility-based, and model-based agents) can be composed into a single network, so each agent applies the reasoning approach its domain requires. The point is that agents collaborate rather than operate in isolation, and A2A gives that collaboration a structured communication protocol so they communicate on shared terms. That collaboration, not tool access, is the problem A2A exists to solve.

Three components anchor the model. Agent cards provide capability discovery: each agent publishes a machine-readable description of what it can do, so other agents discover the right collaborator. Task objects are the unit of work passed between agents, carrying the request and its context through the same kind of structured interactions MCP standardizes over JSON-RPC.

Capability discovery lets an orchestrator find the right agent before any task is sent. The communication layer handles delegation and responses, moving tasks and artifacts between agents reliably. Framed architecturally, A2A is the interoperability layer that lets autonomous agents built on different frameworks coordinate across enterprise workflows, and ACP pursues that same interoperability goal from a parallel direction.

MCP vs A2A: Key differences

MCP and A2A solve different problems at different layers of the stack. Comparing them is less about ranking and more about clarifying where each belongs, since a well-designed system uses both. ACP occupies adjacent territory to A2A, so the same layered logic applies when you evaluate ACP against the pair.

	MCP	A2A
Primary focus	Tool and system access for a single agent	Coordination and delegation between agents
Direction	Vertical: agent to system	Horizontal: agent to agent
Interaction mode	Request-response	Conversational and stateful
Core unit	Tool invocation	Delegated task
Layer in stack	Action layer	Orchestration layer

Focus and purpose

MCP focuses on standardized tool and API access for an individual agent. A2A focuses on coordination, delegation, and context-sharing between multiple agents. One operates vertically, connecting an agent down to the systems it acts on. The other operates horizontally, connecting an agent across to its peers. That difference in direction is the clearest way to keep the two protocols straight, and it is also how ACP is best classified, as a horizontal coordination protocol rather than a vertical access one.

Interaction mode

MCP interactions are typically request-response: an agent’s LLM invokes a tool and receives a result, and the exchange is largely stateless. A2A interactions are conversational and stateful, because agents negotiate, delegate, and iterate over a task. The implications are real. Governing how AI systems exchange state matters here. Request-response tool calls are simpler to govern and reason about for latency. Stateful agent communication requires managing shared task state across a workflow, which raises the bar for observability and governance as the volume of interactions grows. ACP-style coordination shares that stateful profile, so its interactions carry similar governance demands.

When to use each

Use MCP when you need a governed, reusable interface for agents to access business systems and APIs. Use A2A when you need AI agents to divide labor, delegate tasks, or operate as part of a multi-agent pipeline. Most enterprise deployments will need both, because standardized tool access and structured coordination are different requirements.

This is also why asking whether A2A is still relevant misreads the situation: as multi-agent systems grow, the coordination problem A2A and ACP address becomes more important, not less. The protocols are converging into a shared agent ecosystem rather than displacing one another, and the ecosystem is still evolving.

Can MCP replace A2A?

MCP cannot replace A2A, because the two operate at different layers. MCP handles tool access. A2A handles agent coordination. Treating one as a substitute for the other creates architectural gaps: MCP alone cannot manage delegation, track task state, or support the inter-agent negotiation that multi-step work depends on. The same is true of ACP, which addresses coordination MCP was never designed to handle.

A quote-to-cash workflow shows what breaks without A2A. Moving a deal from qualified opportunity to closed invoice requires a sales agent to coordinate with a pricing agent, a contract agent, and a finance agent across multiple systems. With MCP alone, each agent’s LLM can invoke tools against Salesforce, NetSuite, or a CPQ system perfectly well. But no protocol manages task delegation between agents, no shared state tracks where the workflow is, and no mechanism lets one agent hand off to another when its part is complete.

The result is a set of capable but uncoordinated agents that cannot finish a multi-step, cross-system workflow without custom orchestration code for every handoff, which is exactly the fragility a standard protocol is meant to remove. Remove A2A from a multi-agent architecture that relies only on MCP, and you are left with strong individual tool use and no way for agents to collaborate. The question is not which protocol to choose, but how to implement both correctly.

How MCP and A2A work together in enterprise AI workflows

MCP and A2A operate at different layers of the same architecture, which is exactly why they fit together. Consider a concrete multi-system workflow. An orchestrating sales agent uses A2A to delegate an order lookup to a specialist agent.

That specialist agent calls a Celigo MCP server to retrieve the record from NetSuite or Salesforce, then returns the result up the chain, where the orchestrating agent’s LLM decides the next step. A2A handled delegation and coordination at the top layer, MCP handled governed tool execution at the action layer, and neither would suffice alone.

Mapped explicitly, the pattern is consistent: A2A is how autonomous AI agents collaborate and pass work between themselves, and MCP is how any one of those agents reaches the systems it needs to act on. Enterprise AI systems at scale require both protocols operating together across their workflows, not in isolation, because AI agents need both coordination and access.

Agentic workflows that span procurement, logistics, finance, and support only hold up when coordination and tool access are each handled by a purpose-built standard, with ACP extending the same layered model as the agent ecosystem evolves.

How Celigo bridges MCP and A2A for enterprise AI agents

Protocols define the rules. An integration and governance layer is what makes those rules viable at enterprise scale, and this is where Celigo fits. Rather than one tool among many, Celigo functions as the runtime that lets both MCP and A2A architectures, and the AI systems running on them, operate reliably in production.

The Celigo MCP Server exposes APIs, integration flows, connectors, and business logic to AI agents in a standardized, governed, and secure way. Instead of pointing agents at raw endpoints, teams expose curated tools through a managed gateway that enforces scoped access, authentication, and audit logging on every call.

Because Celigo centralizes tool access this way, the multi-agent A2A architectures built on top of it inherit shared tool access, consistent permissioning, observability, and audit logging across every agent in the system, so cross-agent collaboration runs on a consistent foundation. Interoperability stops being something each team rebuilds and becomes a property of the platform, whether agents coordinate through A2A or ACP.

Several capabilities support this directly. Celigo AI Agents connect LLM reasoning to real enterprise action. Tool Builder lets teams define reusable integration tools any agent can invoke. The MCP Server governs how those tools are exposed and consumed. Ora AI Copilot brings natural-language interaction to the platform. Agent execution logging captures what each agent did, the foundation for both observability and audit.

Together, these give a multi-agent system the governance layer that A2A coordination and MCP tool access both assume but neither provides on its own. The standards themselves, from Anthropic’s MCP to the A2A specification, define agent communication and access; Celigo makes them operationally sound.

To see how governed MCP and A2A can support your own agentic workflows, explore Celigo’s agentic automation capabilities documentation or request a demo and consider where a governed integration layer would strengthen your architecture.

How does RAG differ from A2A and MCP?

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RAG, or retrieval-augmented generation, is not a protocol. It is an approach for improving an LLM’s outputs by retrieving relevant information from data sources before the model responds. A2A and MCP are communication protocols that define how agents coordinate and how they access tools. They complement RAG: an agent might use MCP to retrieve context from data sources as one step in a RAG pipeline, then let the LLM produce a better answer.

What security considerations apply to MCP and A2A in enterprise deployments?

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The core concerns are authentication, scoped access, and auditability. Every agent identity should be authenticated, every tool call constrained to approved capabilities, and every interaction logged. Because MCP centralizes tool access and A2A introduces coordination between autonomous agents, both benefit from a governed layer that enforces permissions consistently and provides the observability needed to trace how each LLM-driven agent behaves across a workflow of AI systems. The same discipline applies to ACP deployments.

Is A2A an open standard or vendor-specific?

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A2A is an open protocol, originally introduced by Google and developed with a broad set of contributors, which is why it is positioned as an interoperability layer rather than one vendor’s mechanism. MCP is likewise an open standard from Anthropic. Their open nature is what lets AI agents built on different frameworks communicate and collaborate across an enterprise ecosystem.

How does ACP relate to MCP and A2A?

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Agent Communication Protocol (ACP) is another emerging open standard focused on agent-to-agent coordination, so it sits closer to A2A than to MCP. Where MCP standardizes tool access, both A2A and ACP standardize how AI agents communicate, delegate, and collaborate, and each is a communication protocol built for that kind of collaboration. In practice, ACP is part of the same evolving landscape: teams evaluating A2A will often see ACP referenced alongside it, and because ACP addresses coordination rather than tool access, it complements MCP the same way A2A does. As these AI systems mature, MCP, A2A, and ACP are best treated as layers of one agent ecosystem rather than rivals, and Celigo’s governed integration layer supports agents regardless of which coordination protocol they communicate through.