From Context to Collaboration: The Rise of Agent2Agent (A2A) Protocol

In a recent article, I introduced the Model Context Protocol (MCP) — a key advancement that allows large language models (LLMs) to securely access enterprise data and interact with tools in real time. MCP adds a layer of contextual understanding that transforms how AI systems operate within business environments.

Today, I want to explore the next logical step: collaboration between AI agents.

Introducing the Agent2Agent Protocol (A2A)

Announced by Google, the Agent2Agent (A2A) protocol is a new open standard designed to enable seamless communication and task-sharing between AI agents. Developed with contributions from over 50 technology partners — including Atlassian, LangChain, Salesforce, SAP, and ServiceNow — A2A sets the foundation for a new era of multi-agent ecosystems.

While MCP focuses on connecting models to context, A2A addresses how agents coordinate and collaborate.

Key Features of A2A

A2A introduces a common language and task lifecycle that enables agents to:

Advertise their capabilities using JSON-based "Agent Cards"
Delegate and manage tasks between agents
Exchange contextual messages and data artifacts
Negotiate user interaction modes, such as text, voice, or visual interfaces

The protocol is designed to be vendor-neutral, extensible, and open — ideal for enterprise environments looking to break out of platform silos.

MCP and A2A: Better Together

These two protocols are complementary:

MCP enables secure, contextual data access for AI models.
A2A allows agents to collaborate and orchestrate actions across systems.

Imagine an HR onboarding assistant that securely retrieves employee data via MCP, then coordinates with IT, payroll, and facilities agents using A2A to execute the complete onboarding workflow.

This is the promise: a shift from isolated tools to intelligent ecosystems.

Why It Matters

Today’s AI agents are often task-specific and siloed, limited in scope and unable to communicate beyond their own domain.

Protocols like A2A enable:

Cross-platform agent collaboration
Scalable architectures where specialized agents interact
Multi-agent reasoning for more advanced problem-solving

We’re moving from AI tools to AI teams.

Limitations and Early-Stage Considerations

As with any emerging protocol, A2A is in its early days. Adoption will require experimentation, community involvement, and likely multiple iterations. But the foundation is promising, and its open nature should accelerate innovation.

Final Thoughts

MCP showed us how to give models context. A2A shows us how to make them collaborate.

Together, they form a powerful foundation for the future of enterprise AI — secure, contextual, and cooperative.

Have you started exploring these protocols in your own projects? I'd love to hear your thoughts.

Photo de Shubham Dhage sur Unsplash

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Agent2Agent Protocol: Transforming AI Collaboration Today