A2A vs MCP: How AI Agents Actually Talk to Each Other

AI agents in isolation are basically very smart rocks. They can reason, generate content, maybe even surprise you with their capabilities—but they're stuck inside their own little bubbles. The interesting stuff happens when agents can talk to each other and access your actual infrastructure: databases, code repositories, file systems, all the messy reality of your tech stack.

That's where things typically turn into a custom integration nightmare. Every new agent means more bespoke code, more one-off connections, more technical debt. The industry knows this is a problem, which is why we're now seeing protocol solutions emerge. Two acronyms in particular are gaining traction: A2A (Agent /article/google-a2a-protocol-ai /article/google-a2a-protocol-ai-agents-communication-agent-communication-to-Agent) and MCP (Model Context Protocol).

IBM Technology's Martin Keen and Anna Gutowska recently walked through both protocols, and what's interesting is how cleanly they divide the territory. These aren't competing standards—they solve different problems.

The Agent-to-Agent Problem

A2A tackles multi-agent orchestration. It's an open protocol that lets agents built by different vendors or frameworks communicate without custom middleware for every possible pairing. "Siloed agents can communicate and work together regardless of differing vendors or frameworks," Gutowska explains.

The mechanism is surprisingly straightforward. Agents use something called an "agent card"—essentially a machine-readable resume that advertises capabilities. Other agents can discover these cards dynamically, figure out who does what, and delegate tasks accordingly. As Keen puts it: "Other agents can discover these cards dynamically and figure out what skills or services are offered and decide, hey, you do that, I'll do this."

What makes this work in practice is the transport layer. A2A runs on plain HTTP using JSON RPC 2.0 for request and response payloads. That means you get all the infrastructure you've already built for web services: routing, security layers, load balancing, logging. No exotic new stack required.

The modality-agnostic design is worth noting. A text-focused agent can collaborate with an image-processing agent without translation layers. One agent generates a design mockup, another reviews it, a third handles client approval—all within the same structured flow.

For long-running tasks, A2A supports streaming updates via server-sent events. Remote agents can push progress updates and partial results in near real-time instead of going silent until completion. This addresses one of the more frustrating aspects of agent workflows: the black box problem where you have no idea if your agent is working or stuck.

The Context Problem

But agent-to-agent communication only gets you so far. What about when a single agent needs to pull data from your PostgreSQL database, commit code to GitHub, and read files from your local system?

You could write custom code for each integration. You'd also be rewriting it every time you swap models or tools. This is where MCP (Model Context Protocol) enters.

MCP creates an abstraction layer between agents and external resources. The architecture is simple: an MCP host (where the agent runs) communicates with an MCP server (which knows how to talk to your actual resources). The agent doesn't need database-specific knowledge or file system APIs—it just uses standardized primitives exposed by the MCP server.

Those primitives come in three flavors: tools (functions the model can invoke, like searching a database), resources (things the model can read, like files or records), and prompts (pre-built templates for efficient interaction). "The agent doesn't need to know how the database is implemented or which API works best with the local file system," Keen notes. "It just kind of passes the request to the primitives exposed by the MCP server and it handles translation."

The transport layer adapts to deployment context. Local MCP servers use standard input/output. Remote servers use HTTP with streaming support. Either way, the message format is JSON RPC.

Because MCP is open, you're not building from scratch. Pre-made MCP servers already exist for common systems: Slack, GitHub, various databases, multiple file systems. Write an MCP server once for your CRM, and any MCP-compatible host can use it. Swap out your underlying model, and the integration still works.

When You Need Both

The retail inventory scenario Keen and Gutowska outline shows how these protocols complement each other. An inventory agent uses MCP to interact with product and stock databases. When stock runs low, it uses A2A to notify an internal order agent, which then communicates via A2A with external supplier agents. Each supplier might use different frameworks, but that doesn't matter—A2A handles the inter-agent communication while MCP handles each agent's resource access.

"A2A for agents talking to agents and MCP for agents talking to tools and data," Gutowska summarizes. "It turns out we weren't competing. We were complementing."

This division makes sense from a security perspective too. A2A operates between autonomous systems that might have different trust levels and owners. MCP operates within your infrastructure, where you control both the agent and the resources it accesses. Different threat models, different protocols.

What's less clear is adoption trajectory. Open protocols are great until they fragment into competing implementations or fade because no one commits resources to tooling. JSON RPC and HTTP provide solid foundations, but integration ecosystems live or die by their libraries, documentation, and community momentum.

The other unresolved question: what happens when these standardized communication channels become attack vectors? An agent card is a capability advertisement—which also makes it a target map. MCP servers are abstraction layers with privileges to your actual data. Both protocols will need robust authentication and authorization mechanisms as they mature beyond proof-of-concept deployments.

For now, the protocols solve real problems. Agents that can discover each other's capabilities and delegate work without custom integration code. Agents that can access your infrastructure through reusable, standardized interfaces. That's not theoretical value—it's the difference between agent systems that stay in demos and ones that ship.

Rachel "Rach" Kovacs is Buzzrag's cybersecurity and privacy correspondent.