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A2A (Agent-to-Agent Protocol): The Other AI Standard Competing With MCP

Key Takeaways

  • Google introduces A2A in 2025 as an open standard for agent-to-agent interoperability, then contributes it to the Linux Foundation as an open, vendor-neutral project.
  • AgentCards describe an agent’s capabilities, how to reach it, and what input it expects, while Tasks track submitted, working, completed, or failed work between agents.
  • MCP connects one AI model to tools, data sources, and context, while A2A connects one AI agent to another AI agent for task handoff and negotiation.
  • WordPress site owners do not need to add A2A support today unless they run multiple distinct AI agents across systems; for most single-site work, MCP or the WordPress Abilities API remains the relevant layer.

MCP gets most of the WordPress-world attention because it’s the protocol that connects an AI model to your site’s tools and data. A2A solves a different problem: getting two separate AI agents, possibly built by two different companies, to talk to each other. If you’re building anything with multiple agents involved, the distinction matters.

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The A2A Protocol project documentation homepage
The A2A Protocol project site. Screenshot captured live, 2026-09-06.

What A2A Actually Standardizes

Google introduced the Agent2Agent (A2A) protocol in 2025 as an open standard for agent-to-agent interoperability: how one AI agent discovers what another agent can do, negotiates a task, and exchanges the results (“artifacts”) back and forth. It was later contributed to the Linux Foundation as an open, vendor-neutral project, with backing from a wide range of technology and enterprise software companies.

The Basic Mechanics: AgentCards and Tasks

Two concepts do most of the work in A2A. An AgentCard is a published, machine-readable description of what a given agent can do — its capabilities, how to reach it, and what kind of input it expects — the mechanism another agent uses to discover it in the first place, conceptually similar to how an OpenAPI spec describes a REST API’s endpoints before you call them. A task is the actual unit of work handed from one agent to another, with a defined lifecycle (submitted, working, and a completed or failed end state) so the requesting agent can track progress rather than treating the call as an opaque black box. This task-lifecycle model is what lets a long-running job on one agent be checked on or cancelled by the agent that requested it, instead of just waiting silently.

A2A vs. MCP: Different Layers, Not Competitors in the Way It Sounds

Despite “competing standard” framing you’ll see online, the two protocols solve adjacent, not identical, problems:

  • MCP — connects one AI model to tools, data sources, and context (a database, a WordPress site, a file system). Model-to-tool.
  • A2A — connects one AI agent to another AI agent, so they can negotiate and hand off tasks between systems that may be built on entirely different stacks. Agent-to-agent.

A realistic multi-agent setup can use both: an orchestrating agent talks to specialist agents over A2A, and each of those specialist agents talks to its own tools and data over MCP.

Diagram contrasting MCP model-to-tool connections with A2A agent-to-agent connections
MCP connects a model to tools and data; A2A connects one agent to another via AgentCard and Task.

Where This Shows Up for a WordPress Site Owner Today

Direct A2A adoption on individual WordPress sites is still early and mostly relevant to larger, multi-agent automation setups (an agency running several specialist agents across client sites, for example) rather than a single-site plugin feature yet. The practical takeaway is knowing the vocabulary: if a vendor pitches “A2A support,” that’s an agent-orchestration claim, not a your-site-connects-to-AI-tools claim — that second one is what MCP and the WordPress Abilities API cover.

Should You Actually Care Yet?

For a single WordPress site, no immediate action is needed — there’s no “add A2A support” setting to look for in your plugins today. Where it becomes relevant is if you’re running (or considering) a setup with multiple distinct AI agents that need to coordinate across systems: a content agent that hands research tasks to a separate research agent, or an agency-level system distributing tasks across many specialist agents for different client sites. If that’s not your setup, MCP and the Abilities API remain the layer worth understanding first.

FAQ

Do I need to choose between A2A and MCP for my WordPress site?

No, they’re not mutually exclusive alternatives for the same job. For most single-site WordPress work today, MCP (or a plugin’s own Abilities implementation) is the relevant layer; A2A matters once multiple separate agents need to coordinate.

Who governs the A2A protocol?

Google introduced it, then contributed it to the Linux Foundation as an open, vendor-neutral project with backing from multiple companies, rather than keeping it as a single-vendor standard.

Is A2A a WordPress-specific standard?

No. It’s a general-purpose protocol for agent interoperability, not tied to WordPress or any specific CMS.

What is an AgentCard?

A machine-readable description an A2A agent publishes of its own capabilities and how to reach it — the mechanism another agent uses to discover what it can do, conceptually similar to how an OpenAPI spec describes a REST API before you call it.

Conclusion

A2A and MCP aren’t rivals competing for the same job — they’re two layers of the same emerging AI-agent stack, and most WordPress site owners will only need to think seriously about MCP for now. Keep the vocabulary straight and vendor claims about “agent protocols” get a lot easier to evaluate.

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About the Author

Photo of Aditya Sharma CMO of Nexter
CMO at POSIMYTH Innovations · Nexter · 7 years experience

He has spent years in the WordPress ecosystem building, breaking, and optimizing sites until they actually perform. He works at the intersection of speed, growth, and usability, helping creators ship websites that load fast and convert. An active WordPress community contributor sharing through tools, tutorials, and direct collaboration. Tested practice, not theory.

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